# QNT 275 All Participations

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QNT/275

STATISTICS FOR DECISION MAKING

The Latest Version A+ Study Guide

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QNT 275 All Participations Link

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QNT 275 Week 1 participation Essentials of Business Statistics, Ch. 1

Inferential Versus Descriptive Statistics (A thought )

Team

I like the material in this chapter, I have always found it an enjoyable read. Simply put, Descriptive statistics is a form of statistics that use Graphs, Charts and simple mathematical techniques to summarize how the sample population is behaving. In fact, you can add a brief Executive Summary to the graphs, charts and simple mathematical techniques to articulate a message to the intended audience.

On the other hand, Inferential Statistics are the use of statistical techniques to draw an inference on how the sample population is behaving. It is through one simple Numeric, that a researcher can determine what is going on with the sample population, identify their behavior, identify patterns in the behavior and forecast future behavior. Inferential Statistical techniques include (but not limited to): Z-score, Z-test, T-test (and all forms of the T-test). ANOVA, Chi-Square and Regression Analysis.

In this class, we are going to learn about both Inferential and Descriptive statistics. Any questions or thoughts?

QNT 275 Week 1 participation Essentials of Business Statistics, Ch. 2

Adidas and Net Sales Increases (a question from one of your classmates and thoughts by Hermis)

Team

One of your classmates asked the following regarding Example 2.1 (Adidas Sales)

"Chapter 2 Tabular and Graphical Methods, specifically the example regarding the net sales over a 10 year period for Adidas. How would a business looking at the data and viewing the the net sales and seeing growth, but seeing a decline in the percentage of sales view the numbers as positive or negative? Specifically would a drop in the percentage say in Europe mean that they should focus more efforts such as more marketing in that area to increase the sales? Or would they view the decline as other regions grew more causing the loss in the percentage of sales and in turn focus on what would appear to be an emerging market in regions such as Asia or Latin America where they were able to steal 17% of the overall net sales from Europe and North America? After 10 years does the decline in North America and Europe overall percentage of Adidas sales say more about the efforts in those areas or more about the rise of Asia and Latin America efforts?"

This is a very good question and always goes to the cruxed of statistics as viewing the glass as "half full versus half empty". This table uses a math term called a Proportion. A Proportion is a number that exists between 0 and +1.0. It is calculated by dividing the number of subjects/data points with a specific characteristic by the total number of subjects or sample size. For example (simple), If you are graduating 100 students, and 10 are accounting majors then 10/100 = 10% would be accounting majors.

As you can see from the chart, total sales has increased (overall based on hard dollars) for the 4 regions combined over the 10 year period. However, based on a Proportion, sales in Europe and the US has actually declined when compared to the other 2 regions of Asia and Latin America. Which tells Adidas that their Asian and Latin American presence is growing but US and European is declining. This type of data helps a firm identify their weaknesses and shore-up resources and efforts in order to improve performance. If the organization choses to do so, as Adidas may want to focus more energy on emerging markets such as Asia and Latin America. They may view Europe and the US as mature, over-saturated markets with way too much competition.

This reminds me a lot of McDonald's Corporation. I bet if we had data that showed the same 4 regions, over the same period of time, the results would most likely be the same with a decline in the US but growth in other parts of the world. McDonald's acknowledges that the vast majority of their sales comes from International stores.

What are your thoughts?

QNT 275 Week 1 participation Presenting Data Effectively, Ch. 1

Read pp. 1-13 of Chapter 1: "The Justification for Presenting Data Effectively" in Presenting Data Effectively.

Presenting Data Effectively

Effective Data Presentation

QNT 275 Week 1 participation Presenting Data Effectively, Ch. 2

Read pp. 21-39 of Chapter 2: "Graphics" in Presenting Data Effectively.

Image vs. No Image

How images impact

QNT 275 Week 1 participation The Role of Statistics in Business Decision-Making

Watch the "The Role of Statistics in Business Decision-Making" video.

Consider the following as you watch:

Describe probability theory.

Explain how sampling theory relates to probability theory.

Differentiate between a population and a sample.

Explain, based on the video, why statistics is important in decision-making.

Interesting Article About the Relationship Between Gender and Career

http://www.npr.org/blogs/money/2014/10/28/359419934/who-studies-what-men-women-and-college-majors?utm_source=facebook.com&utm_medium=social&utm_campaign=npr&utm_term=nprnews&utm_content=2055

I believe you all will find this article and the various statistics interesting

Thoughts

I really like this introductory activity. Frankly, when most people here the word Statistics they freak out a bit (understatement). However, if you dig deeper, you will find that we all use statistical data on a daily basis. In fact, everything that you interact with regularly. Statistics drive local, State and Federal legislation, consumer product development and offering, marketing, internet research, internet advertising etc.

Statistics helps consumer product producers, government officials, marketers to understand the needs of their market and constituency. If they have data that clearly illustrates a picture of how their population is behaving, they can then customize their product and service offering to the market place. All statistical models enable researchers to understand how the population is behaving so they can proceed with a solution.

Based on your reading of this activity, what is an example of the application of statistics in your life?

QNT 275 Week 1 participation Final Exam Preparation

Review weekly topics and objectives in preparation for the final exam.

Discuss any questions or clarifications you need with the class.

Final Exam Sample Question #3 for Week 1

Team

Check out the following question that you may see on the Final:

The two branches of the study of statistics are generally referred to asdescriptive and inferential statistics

What are the differences between Descriptive and Inferential Statistics?

Final Exam Sample Question #1 for Week 1

Team

Below is a sample question that you may see on a quiz or on the final exam based on this Week's Material

1. The study of statistics can be defined as:

Simply put Statistics is the study of collecting, analyzing, presenting, and interpreting data. What are your thoughts? Would you agree with my assessment?

Final Exam Sample Question #4 for Week 1

Team

Here is another question that you may see on a quiz or the final:

Which of the following is a quantitative variable?

House Prices

House Age

House Size

All of the Above

In this case, I would select all the above. This is because quantitative variables are numeric in nature. Which in this case, every variable that you see listed applies to a quantitative variable. What are your thoughts?

Final Exam Sample Question #2 for Week 1

Team

Below is another sample question that you could see on a quiz or on the Final:

The manager of a retail shoe store has applied statistics to analyze sales, purchasing, and data and reached the conclusion that the store could reduce costs by reducing inventory of very small and very large sizes, and substituting an online ordering service to drop-ship rare sizes directly from suppliers. How should the manager communicate those results to the store owner?

Probably the best action for the manager would be to summarize the recommendations, supported by visual representation of the data and statistics. What are your thoughts on what his/her action to be?

QNT 275 Week 1 participation Da' Hermis Burger Joint

Tell me the Mean Mode and Median for the following data: The "Hermis Burger Joint" is a real greasy joint but they make great fried chicken, waffles and apple pie (mmmm.....Pie)! You lika da Yeero?!

If you are ever on Roselle Rd. in Schaumburg Il....check out Country Doughnuts....best doughnuts east of the Mississippi!

The Hermis Burger Joint pays its 5 hourly employees $6, $3, $5, $7 and $6 per hour.

What is the Mean Mode and Median of the data.

QNT 275 Week 1 participation The Wisconsin Water Table

What is the Variance, Standard Deviation and Mean of the data below?

Wisconsin Water Table

This is a great example of a real table that you can find in academic journals or government periodicals.

All you have to do is find the mean, standard deviation and variance. We are going to be using this very same table for Z-score as well:

The following table presents a data file that lists the peak discharge from a hydroelectric project in

Wisconsin for the years 1957 to 1968. The variable peak discharge has an interval level of measurement.

Year Peak Discharge

1957 1,120

1958 2,380

1959 886

1960 1,420

1961 1,480

1962 1,200

1963 657

1964 1,280

1965 1,640

1966 1,280

1967 1,740

1968 1,380

(source: U.S. Department of the Interior, Geological Survey, Water Resources Divisions, Estimating Magnitude and Frequency of Floods in Wisconsin, 1971. p. 77. I19.2:W75)

QNT 275 Week 2 participation Essentials of Business Statistics, Ch. 3

The Median & Mode

QNT 275 Week 2 participation Essentials of Business Statistics, Ch. 7

Sampling

QNT 275 Week 2 participation Presenting Data Effectively, Ch. 2

Read pp. 40-57 of Chapter 2: "Graphics" in Presenting Data Effectively.

Bar Charts

Images

QNT 275 Week 2 participation Obtaining a Suitable Sample

Watch the "Obtaining a Suitable Sample" video.

Consider the following as you watch:

Discuss the difference between convenience and random sampling.

Explain how you can obtain a sample size representative of a population.

Provide three examples.

Explain why a sample is used in place of the population.

Thoughts on Sampling

Team

I liked this Video, it provided a very good over-view of the basics of research design. In order to execute on a research initiative that is going to apply the concepts of statistical methods, you need to collect a sample population which will represent the Overall/Universal population to which your study is examining. Your Ultimate goal, you want to eliminate the bias (sampling error) as much as possible to come up with a general conclusion about the expected behavior of the overall population based on the observed behavior of your sample population.

There are several ways you can select your sample

1. Simple Random: A sampling procedure allowing for the equal and independent chance of subjects being selected as part of the sample. This is a very important process as we will be using it to apply to all the statistical formulas. You put names in a hat and pick your sample

2. Non-probability: A sampling procedure when the likelihood of selecting any one member of the population is unknown. Not much difference between this procedure and the one above. However, you do not know the percentage of the population that you are examining.

3. Systematic sampling: A random sampling procedure where increment determine who becomes a part of the sample.

4. Stratified sample: The process of selecting a sample that represents different groups of the population.

5. Cluster sampling: A probability sampling procedure where units of subjects are selected rather than the subjects themselves.

6. Convenience sample: Represents a captive audience.

Any questions on these techniques?

QNT 275 Week 2 participation Sampling, Surveying, and Data Analysis

Great Summary

Sampling, Surveying, and Data Analysis

QNT 275 Week 2 participation Measuring Central Tendency and Variability

Watch the "Measuring Central Tendency and Variability" video.

Consider the following as you watch:

Define the three standard measurements of the central tendency and provide example of each.

Explain when you might want to use a median number.

Provide an example.

Define a standard deviation and what a standard deviation shows, provide an example

Measurement of Disperion

Team

The Measurement of Dispersion is made up of 3 mathematical techniques, the standard deviation, variance and the range. Of the 3, the standard deviation is the most accurate numeric. The purpose of the Measurement of Dispersion is to tell a researcher how close or far data points are from each other. That is it! Which means that with the Measurement of Dispersion alone, we really do not know much about the sample population.

It is not until we augment the Standard Deviation with a Z-Score, that we are able to infer a conclusion on how the sample population is behaving. The Z-Score is the most basic of statistical formulas that fall under the umbrella of Inferential Statistics. The Z-Score is the gateway to other, more sophisticated formulas such as the Z-Test, the T-test (all forms of T-Test), the ANOVA and the Regression Analysis (just to name a few).

What is a Z-score?

QNT 275 Week 2 participation Statistics Intro: Mean, Median, and Mode

Watch the "Statistics Intro: Mean, Median, and Mode" video.

Consider the following as you watch:

Explain how mean is calculated.

Explain how a median is calculated with an even number list.

Define an array.

Provide an example.

Define a mode.

Identify if you can have more than one modal number in a set of data.

Note: All Khan Academy content is available for free at www.khanacademy.org.

QNT 275 Week 2 participation Finding the Range and Mid-Range

Watch the "Finding the Range and Mid-Range" video.

Consider the following as you watch:

Define a range and provide two examples.

Calculate a mid-range and provide two examples.

Note: All Khan Academy content is available for free at www.khanacademy.org.

QNT 275 Week 2 participation Range, Variance, and Standard Deviation as Measures of Dispersion

Watch the "Range, Variance, and Standard Deviation as Measures of Dispersion" video.

Note: All Khan Academy content is available for free at www.khanacademy.org.

Standard deviation

QNT 275 Week 2 participation Psychology Research in Context: Measuring Validity

Watch the "Measuring Validity" video.

measuring valididty

QNT 275 Week 2 participation Final Exam Preparation

Week 2 Practice Problem #1

Team

Here is a very good problem that you may see on either the quiz or the final:

A company wants to estimate the mean price of oil over the past 10 years. What kind of data does the company need?

In my opinion, the data that is required is Time Series data. As Time Series data is data that illustrates a pattern of behavior over a period of time. WHat are your thoughts?

Week 2 Practice Problem #2

Team

Below is a problem that you may see on either your quiz or your final exam.

Bias can occur in sampling. Bias refers to

· the division of the population into overlapping groups

· the tendency of a sample statistic to systematically over-or underestimate a population parameter

· the creation of strata, which are proportional to the stratum's size

· the use of cluster sampling instead of stratified random sampling

In my opinion, the correct answer is in Orange. Bias is the difference between the expected value of the sample population versus the population parameter the statistic is estimating. Simply put, Bias is error to which you get that puts your final calculation either higher or lower than what the actual calculation is supposed to be to best represent the overall population.

Week 2 Practice Problem #4

Team

Below is a problem that you could see on either the quiz or the Final Exam:

What is (are) the most widely used measure(s) of dispersion?

The answer is Variance and Standard Deviation. The only other measure of dispersion left is Range and Range is not commonly used to describe the spread of data.

WHY?

Week 2 Practice Problem #5

Team

Here is a sample problem that you may see on the Final Exam:

A stem-and-leaf diagram is constructed by separating each value of a data set into two parts. What are these parts:

· Stem consisting of the leftmost digits and leaf consisting of the last digit

· Stem consisting of the last digit and leaf consisting of the leftmost digits

· Stem consisting of the leftmost digits and leaf consisting of the second digit

· Stem consisting of the second digit and leaf consisting of the last digit

A Stem and Leaf Diagram is a histogram "Like" chart that records raw numbers of a variable. The purpose of the Stem and Leaf Diagram is to show a clear picture of the Frequency Distribution of data. The Stem consists of the left most digits and Leaf consisting of the last digit.

What is an example of a Stem and Leaf Diagram?

QNT 275 Week 2 participation The Z-score

Select a specific year from the Wisconsin Water Table, and find the Z-score for the associated data point (water discharge)

Year Peak Discharge

1957 1,120

1958 2,380

1959 886

1960 1,420

1961 1,480

1962 1,200

1963 657

1964 1,280

1965 1,640

1966 1,280

1967 1,740

1968 1,380

(source: U.S. Department of the Interior, Geological Survey, Water Resources Divisions, Estimating Magnitude and Frequency of Floods in Wisconsin, 1971. p. 77. I19.2:W75)

The Z-Score

Team

It is not until we augment the Standard Deviation with a Z-score the standard deviation makes sense, such as the case with the Wisconsin Water Table. A Z-Score is a statistical formula that tells us where a specific data point falls on the bell curve in relationship to the mean/mode/median of the bell curve which is the exact middle of the curve. The Z-score tells us how many standard deviations a specific data point falls above OR below the middle of the curve (+ or -

Let's look at the Wisconsin Water Table to explain this process:

In order to get the Z-score for the year 1960. All you need to do is find the Standard deviation and the mean for the Wisconsin Water table (which we have just done in Week 1).

Xbar or μ = 1,371.91

s or σ = 436

Your observation is X which for 1960 is 1,420. Insert in the following formula:

z= X - μ

σ

.11 = 1,420 - 1,371.91

436

The Z-score falls .11 standard deviations ABOVE the mean. Look how close the amount of water discharge was for 1960 as compared to the mean of the water discharge (1,371.91). It is so close it practically kisses the mean.

This Z-score tells us that the water output for the year 1960 was just slightly greater (not by much) than the average discharge of all the years combined. Does this make sense?

QNT 275 Week 2 participation The City of Westwood

Using the T-test for one sample populations, what can you infer about the sample population based on your calculations?

In 1974, the City of Westwood was experiencing problems with their utilities. Home owners were complaining that their appliances were being blown because their homes were receiving too much voltage. The city says each home receives between 121 to 122 volts. The city was 95% confident that they are correct. Analyze the data and determine if they are correct or if the home owners had a legitimate complaint. Use 121 as the population mean

They sample 15 homes and measure the voltage:

Location Number Voltage

1 121.5

2 120.9

3 124.6

4 125.8

5 123.5

6 125.1

7 123.7

8 124.5

9 120.4

10 120.3

11 121.2

12 120.8

13 124.1

14 123

15 124.6

City of Westwood

Team

I am going to give you a kick start by showing you the first steps of the 5 step hypothesis testing process and the beginning of the T-test

1. Research Question:

R1: Are the homes in the City of Westwood receiving between 121 and 122 Volts?

2. Hypothesis Statement

H1: Homes in the City of Westwood ARE receiving between 121 and 122 Volts

Ha: Homes in the City of Westwood ARE NOT Receiving between 121 and 122 Volts

3. Confidence Level

We are 95% confident that are main hypothesis statement is TRUE

95% level of confidence equates to a +/-2.145 (Go to T-table, find the .05/95% column, your Degrees of Freedom = N -1 or 15-1 =14 you find the T-critical of +/-2.145

Step #4: Apply the T-test

T=Xbar-µ

s/√n

Xbar = 122.9333 (which you must calculate yourself)

U = 121

S = 1.8953 (which you must calculate yourself)

N = 15

122.9333 - 121

1.8953/√15 = ?

Now you calculate the rest, what is the T-calculation?

QNT 275 Week 3 participation Essentials of Business Statistics, Ch. 4

Thoughts on this Chapter (By Hermis)

Team

This was an interesting chapter, I think the use of data from space to discuss the conversion and inference of data. Any time you have raw data, you can convert the data into a numerical set and apply a statistical technique to it in order to understand how the sample population is behaving. Are you interested in understanding if life on another planet exists? If "yes" the formula you can use is called Drake's Equation. This equation is a guesstimate and tells the researcher the probability of life on another planet based on radio waves received from space.

Let's take this down to earth to something more applicable. You can use statistical probability to determine the probability of something occurring. There are 3 major types of probability. They are continuous probability, discrete probability and the probability of independent events.

Of the 3 probabilities, which probability represents the role of dice, or a quarter?

The Probability of Independent Events (Example by Hermis)

Team

Check out this scenario I created this example (step - by step) on how to solve a Probability of Independent Events. What is one workplace scenario that you could apply the Probability of Independent events to?

QNT 275 Week 3 participation Essentials of Business Statistics, Ch. 5

Binomial Probability Distribution

Risk Neutrality and Risk Aversion

QNT 275 Week 3 participation Essentials of Business Statistics, Ch. 6

One of my Favorite Chapters! (Normal Distribution)

Team

This is one of my favorite aspects of statistics. It is called Normal Distribution and it is at the epicenter of statistical and hypothesis testing. I am going to explain Normal Distribution in more detail below and provide you with a lot of fun examples for you to walk through that illustrate how to find the Area of Curve which is directly associated to Normal Distribution:

What is Normal in the world of statistics?

You may be wondering this on a daily basis while in this class? Not really!

Under the normal curve, our distribution of scores is called the Empirical Rule and it is as follows:

The Robust Empirical Rule:

68% of all observations are within 1 standard deviation away from the mean

95% of all observations are within 1.96 standard deviations away from the mean

95.44% of all observations are within 2 standard deviations away from the mean

99% of all observations are within 2.57 standard deviations away from the mean

99.7% of all observations are within 3 standard deviations away from the mean

The normally distributed shaped bell curve is a purely theoretical concept in which the horizontal (X) axis represents all possible values of a variable (such as the Z-calculated). Examples include the Wisconsin Water table Z-score for the year 1960 (.11) that we covered in Week 2. You can see an this example under the Learning Activity called "The Z-score" . You can plot these points along the horizontal (X) axis of the bell curve and they will fall within a range. As you can see the 1960 Z-score of .11 is within 1 standard deviation of the mean (to the right).

The vertical (Y) axis of the scores tells us the probability of these values occurring. Look at the empirical rule, the Z-score for the year 1960 (.11) is within 68% of all observations being 1 standard deviation away from the mean. For the Z-calculated of -29, the scores are probably 99.99999999% of all observations. It is really hard to exactly determine the % of observations since we can go into infinity of Z-calculated and on the bell curve. Infinity of the Z-calculated will always be possible because our Bell Curve vertical (Y) axis NEVER touches our horizontal (X) axis. It just grows increasingly closer but the naked eye will never be able to distinguish a separation of the 2 axis. Hence, a Z-calculated of -29 is probably 99.9999999% of all scores away from the mean. It is impossible for me (or you) to determine this % since we do not have sophisticated software to tell us the exact % of all observations that this particular score falls under.

Normal distribution of the bell curve ties into our discussions about significance and non-significance for hypothesis testing which we will have next week. Essentially, we are determining if our observations are normal and if our observations are not normal. As you may recall, a relationship (of variables) is created by comparing our OBSERVED (behavior) calculation to our EXPECTED (behavior) critical.

Properties of the Normal Distribution are:

1. The normal distribution curve is bell shaped.

2. The mean (mode or median) is in the middle of the distribution of the bell curve (measurements of central tendency).

3. Both the left side of the curve and the right side of the curve are equal and symmetric.

4. The normal distribution curve has only one mode.

5. The curve is continuous, the Y-axis (vertical) and the X-axis (horizontal) will never meet and your observed calculations can run into infinity. It will be possible to have observed scores be 99.9999999999% or greater of all observations being x amount of standard deviations away from the mean.

6. For each value of X there is a value for Y.

7. The total area under the curve (left side negative + right side positive) will always equal 1 or 100%

8. The areas under the normally distributed bell curve follow the robust empirical rule

Now, let's determine the area under the bell curve that are particular Z-calculation or Z-score falls under. This means we want to know the % that our particular Z-score covers on the bell curve. I like to refer to it as the real estate the Z-score covers on the Bell curve.

As an example, say we calculated a Z-score of 2.34. Let us find the area between the mean (which is the middle of the curve and always denoted by a 0) Z = 0 and Z = 2.34. To determine the area under the bell curve go to your Normal Distribution Table located in your book (appendix). You need to go the Z-column and find 2.3 and then .04 in the top row, you should see .4904. This is where the column and the row meet in the table. The answer is .4904. Hence the area is .4904 or 49%.

This means that the area under the curve that the Z-score of 2.34 covers is 49%. Any questions?

QNT 275 Week 3 participation Probability and Conditional Probability in Business Decision

Watch the "Probability and Conditional Probability in Business Decision-Making" video.

Consider the following as you watch:

Define probability.

Provide three examples of probability.

Define conditional probability.

Provide three examples of conditional probability.

Describe what questions you might ask to determine probability of risks when starting your own company.

Provide at least three questions.

QNT 275 Week 3 participation Random Variable

Watch the "Random Variable" video.

QNT 275 Week 3 participation Final Exam Preparation

Week 3 Practice Problem #1

Team

Below is a problem/question that you may see on the Quiz or the Final:

Mutually exclusive events ______________.

What does the term "Mutually Exclusive" mean? Well it means that the events are completely unrelated. Hence you would answer with:

· do not share common outcomes

· may share common outcomes

· do not contain all possible outcomes

· contain all possible outcomes

What are your thoughts on Mutually Exclusive as it relates to Probability in the Week 3 Material? Do you think I am correct on my answer?

Week 3 Practice Problem #2

Team

Below is a problem that you may see on the Final Exam or on the Quiz. It is on one of my favorite topics which is Frequency Distribution (Table) and The Normal Distribution:

Frequency distributions may be used to describe which of the following types of data?

· Nominal and interval data only

· Nominal, ordinal, interval, and ratio data

· Nominal and ordinal data only

· Nominal, ordinal, and interval data only

Basically, a Frequency Distribution Table is a table that illustrates the frequency that an answer was provided. Hence it is Categorical in Nature and You can Rank Your Data in the Frequency Distribution Table. With that stated, if I have Categorical Data and Ranked Data, which Levels of Measurement apply to these types of Measurements? (Trick Question)

Okay, let's make it a bit harder. Why would Interval data not apply to a Frequency Distribution?

Week 3 Practice Problem #3

Team

Below is a problem that you may see on the Final Exam or on the Quiz. It deals with the concepts of Independent and Dependent variables which are present in ALL Hypothesis Statements:

What is the name of the variable that's used to predict another variable?

· Response

· Standard error of the estimate

· Coefficient of determination

· Explanatory

Okay, I am giving away too many answers. Why is it the Explanatory Variable and Is the Explanatory Variable associated to the Independent or the Dependent Variable?

Week 3 Practice Problem #4

Team

Below is a Problem that you may see on the Final Exam or the Quiz. It deals with the concepts of the Continuous Probability, which is the probability of an event occurring and is associated to Normal Distribution:

A hedge fund returns on average 26% per year with a standard deviation of 12%. Using the empirical rule, approximate the probability the fund returns over 50% next year.

The answer is 2.6% WHY? How would you come up with this answer? You must use the concept of Continuous Probability and Normal Distribution to find your answer

QNT 275 Week 4 participation Essentials of Business Statistics, Ch. 1

One of my Favorite Topics (levels of Measurement)

Team 275

A quick review before the Final Exam

In this class, the independent variable will 95% of the time be a nominal level of measurement. The dependent variable will be an interval level of measurement.

Here is a great cheat sheet explanation that I created:

Our independent variable and dependent variables will be broken down into 1 of 3 categories: Nominal, Ordinal and Interval

Nominal (Independent variable) is categorical in nature and is solely qualitative (non-number related). They are categories such as Hair Color:

Blonde or Brunette or Red-Head.

Ordinal (one way to measure the dependent variable) is data that is placed in an order. A great example is grade point average. Nigel(2.0), Jackie W. (2.5), Jackie H. (3.2), John (3.7) and Ronnie (4.0) (lowest to highest GPA)

Interval (one way to measure the dependent variable) is data that is equal intervals along a continuum. Temperature thermostat is the best way to show interval data. Numbers can move up and down with no set pattern.The Hermis rule is: Interval data can always be converted to Ordinal data

Thoughts on This Chapter

Team

I enjoyed this chapter, I thought it was pretty interesting. In particular, I enjoyed the section on qualitative versus quantitative data. As you are aware, quantitative data is data that is numerical in nature. On the other hand, qualitative data is non-numeric data. It is words that constitute a data set. For example,

Colors of the rainbow:

Purple

Green

Yellow

Orange

Red

THe quantitative data can be measured and categorized. What are the levels of measurement for quantitative data?

QNT 275 Week 4 participation Essentials of Business Statistics, Ch. 3

Review section 3.7 of Ch. 3: "Covariance and Correlation," in Essentials of Business Statistics

QNT 275 Week 4 participation Essentials of Business Statistics, Ch. 12

Thoughts on Regression Slope Standard Error

Standard Error of Slope =

The standard error of slope is used to calculate the T-Statistic (The T-test). In this case, the T-statistic (The T-Test) is used to determine whether or not your Regression Line is Statistically Significant. In other words, does your Regression Line enable you to accept or reject your Hypothesis Statement regarding the relationship between the 2 variables. If the Regression Line is Statistically Significant, then we have to reject our Hypothesis Statement because there is something happening within the data that is outside of normal behavior.

The standard error of the slope is minimized when we have a large sample, which means the more accurate our calculation is about the true relationship between the 2 variables. This is all tied into the Least Squares Regression Line which the video discusses. Thousands of lines can be drawn through the data points. However, only one line best illustrates a true fit and best describes the relationship/strength of the variables. This one line is called the Least Squares Regression Line, it is the line that has the minimal amount of error within its' slope (Seb).

I have discussed the Linear Regression Coefficient in other posts this week (AKA correlation coefficient). This Coefficient is the one numeric that best illustrates the strength of the relationship between 2 variables.

Strength of the relationship exists between -1 to 0 to +1. The closer this numeric/coefficient comes to 0, the less likely a relationship exists between 2 variables. The closer the numeric/coefficient is to +1 the stronger a positive relationship exists between 2 variables.

What happens when the numeric/coefficient is closer to -1, again what kind of relationship is it?

Regression Line and slope (A quick thought and question)

The steepness or angle of the Regression Line is identified by "b". Slope is "b" and the Y Intercept is "a".

You will calculate slope by taking any 2 points on the line and dividing the rise (vertical distance) between them by the run (horizontal distance) between them. Also known as Rise over Run (Rise/Run)

In what direction does the Line Slope in a Positive Relationship?

QNT 275 Week 4 participation Essentials of Business Statistics, Ch. 13

Dummy Variables

QNT 275 Week 4 participation Correlation and Causality

Watch the "Correlation and Causality" video.

Define a correlation and provide an example

Define causality?

Explain if correlation causes causality.

Provide evidence with an example.

Note: All Khan Academy content is available for free at www.khanacademy.org.

QNT 275 Week 4 participation Using Time Series to Analyze Business Trends

Watch the "Using Time Series to Analyze Business Trends" video.

Identify the four components of time series.

Provide an example of each component and how it might be used in business

Explain if this data becomes obsolete over time.

QNT 275 Week 4 participation Career Connection Discussion: Forecasting

Discuss the following:

Explain what you have learned throughout this course with probability and how you think that will affect your forecasting decisions in your current or future company.

Predict how you will use probability in your current or future company?

Thoughts on Forecasting

A great application of Forecasting is a statistical process that enables us (as researchers) to predict a future quantity. When an organization uses historical data that tracks sales, by day, by week, by month and by Quarter, understanding the projection's "peaks and valleys" of sales productivity helps your organization determine requirements as it relates to allocating resources in order to maximize profitability. Forecasting also enables The Federal Reserve to predict Inflation Rates.

What is another classic example of forecasting?

How to Survive a Zombie Apocalypse

Team

As you know by now, I really like Zombies and have been a fan of the Genre long before it became "hip" to like Zombies. Check out the following study (article) below. Grad Students from Cornell University conducted a Statistical Modeling Exercise that combines such statistical techniques as Forecasting, probability and Logistic Regression in order to determine the safest place to be and to hold out in order to survive a Zombie Outbreak.

It is a very interesting read and details how they used various statistical techniques to identify the best location if a Zombie outbreak did occur. http://www.smithsonianmag.com/smart-news/statisticians-reveal-best-place-wait-out-zombie-apocalypse-180954426/?no-i

QNT 275 Week 4 participation Final Exam Review

Week 4 Practice Problem #1

Team

Below is a problem that you may see on the Final Exam or the Quiz:

Which of the following violates the assumptions of regression analysis?

· The error term does not have the normal distribution.

· The error term has a constant variance.

· The error term is uncorrelated with an explanatory variable.

· The error term has a zero mean.

I chose the 1st option because there are 4 assumptions of Regression Analysis and the 1st option does not align to the 4 assumptions:

1. There is a linear relationship between the Independent and the Dependent Variable

2. There is a statistical indepedendence of errors

3. Homoscedasticity of the errors which means there is a constant variance

4. The Error does follow the Normal Distribution

What does the term Homoscedasticity mean?

Week 4 Practice Problem #2

Team

Below is a problem that you may see on the Final Exam or on the Quiz

In the sample regression equation what is ?

· The value of y when x = 0

· The y-intercept

· The predicted value of y, given a specific x value

· The slope of the equation

Why would I have chosen the 3rd option? What is the relationship between X (the Independent Variable) and Y (the Dependent Variable)?

Week 4 Practice Problem #3

Team

Below is a problem that you may see on the Final or on the Quiz

What type of relationship is indicated in the scatterplot?

· A positive linear or curvilinear relationship

· A negative linear relationship

· No relationship

· A negative curvilinear relationship

I picked the 1st option, because a positive relationship is illustrated on a Scatter Plot when the data points move from the bottom left to the upper right. What would a Negative Relationship look like if it was illustrated?

Week 4 Practice Problem #4

Team

Below is a problem that you may see on the Final or on the Quiz:

Serial correlation is typically observed in:

· sparse data

· cross-sectional data

· time series data

· outliers

Why would I have selected Time Series Data?

QNT 275 Week 5 participation Presenting Data Effectively, Ch. 5

Presenting Data Effectively

QNT 275 Week 5 participation Presenting Data Effectively, Ch. 6

Final Thoughts

Presenting Data Effectively

QNT 275 Week 5 participation Managing the Data Deluge

Watch the "Managing the Data Deluge" video.

Define a data management plan?

Provide an example of good data and an example of bad data.

Explain the following: Other than collecting primary data where might you search for secondary data?

QNT 275 Week 5 participation Instinct, Debate, and Data Make the Best Decisions

Watch the "Instinct, Debate, and Data Make the Best Decisions" video.

Identify how to make the best decisions,

Provide an example.

Explain whether data the only element used in decision making.

Justify whether instinct should play a role in decision-making.

QNT 275 Week 5 participation Interpret and Communicate Data Analysis Results

Watch the "Interpret and Communicate Data Analysis Results" video.

Describe how data can be interpreted differently.

Explain how and why data is scrubbed.

Describe three different ways to communicate data.

Thoughts on this Video (By Hermis)

Team

This video was pretty good, they are describing the 5 Step Hypothesis Testing process. In fact, any and all collection of data, scrubbing of data, analyzing the data, and inferring a conclusion from the data. In order to ultimately make a decision on how to move forward to solve the problem. The video brings up a good point regarding how to present your conclusions. It is really important to present your conclusions as objectively as possible as you do not want to risk offending your management team/clients as this video had discussed

QNT 275 Week 5 participation PowerPoint® 2013 Essential Training

Watch the instructional video by clicking on the Lynda.com® link.

Watch all of the tutorials from "PowerPoint® 2013 Essential Training

https://uopcourses.com/category/qnt-275-participations/

QNT/275

STATISTICS FOR DECISION MAKING

The Latest Version A+ Study Guide

**********************************************

QNT 275 All Participations Link

https://uopcourses.com/category/qnt-275-participations/

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QNT 275 Week 1 participation Essentials of Business Statistics, Ch. 1

Inferential Versus Descriptive Statistics (A thought )

Team

I like the material in this chapter, I have always found it an enjoyable read. Simply put, Descriptive statistics is a form of statistics that use Graphs, Charts and simple mathematical techniques to summarize how the sample population is behaving. In fact, you can add a brief Executive Summary to the graphs, charts and simple mathematical techniques to articulate a message to the intended audience.

On the other hand, Inferential Statistics are the use of statistical techniques to draw an inference on how the sample population is behaving. It is through one simple Numeric, that a researcher can determine what is going on with the sample population, identify their behavior, identify patterns in the behavior and forecast future behavior. Inferential Statistical techniques include (but not limited to): Z-score, Z-test, T-test (and all forms of the T-test). ANOVA, Chi-Square and Regression Analysis.

In this class, we are going to learn about both Inferential and Descriptive statistics. Any questions or thoughts?

QNT 275 Week 1 participation Essentials of Business Statistics, Ch. 2

Adidas and Net Sales Increases (a question from one of your classmates and thoughts by Hermis)

Team

One of your classmates asked the following regarding Example 2.1 (Adidas Sales)

"Chapter 2 Tabular and Graphical Methods, specifically the example regarding the net sales over a 10 year period for Adidas. How would a business looking at the data and viewing the the net sales and seeing growth, but seeing a decline in the percentage of sales view the numbers as positive or negative? Specifically would a drop in the percentage say in Europe mean that they should focus more efforts such as more marketing in that area to increase the sales? Or would they view the decline as other regions grew more causing the loss in the percentage of sales and in turn focus on what would appear to be an emerging market in regions such as Asia or Latin America where they were able to steal 17% of the overall net sales from Europe and North America? After 10 years does the decline in North America and Europe overall percentage of Adidas sales say more about the efforts in those areas or more about the rise of Asia and Latin America efforts?"

This is a very good question and always goes to the cruxed of statistics as viewing the glass as "half full versus half empty". This table uses a math term called a Proportion. A Proportion is a number that exists between 0 and +1.0. It is calculated by dividing the number of subjects/data points with a specific characteristic by the total number of subjects or sample size. For example (simple), If you are graduating 100 students, and 10 are accounting majors then 10/100 = 10% would be accounting majors.

As you can see from the chart, total sales has increased (overall based on hard dollars) for the 4 regions combined over the 10 year period. However, based on a Proportion, sales in Europe and the US has actually declined when compared to the other 2 regions of Asia and Latin America. Which tells Adidas that their Asian and Latin American presence is growing but US and European is declining. This type of data helps a firm identify their weaknesses and shore-up resources and efforts in order to improve performance. If the organization choses to do so, as Adidas may want to focus more energy on emerging markets such as Asia and Latin America. They may view Europe and the US as mature, over-saturated markets with way too much competition.

This reminds me a lot of McDonald's Corporation. I bet if we had data that showed the same 4 regions, over the same period of time, the results would most likely be the same with a decline in the US but growth in other parts of the world. McDonald's acknowledges that the vast majority of their sales comes from International stores.

What are your thoughts?

QNT 275 Week 1 participation Presenting Data Effectively, Ch. 1

Read pp. 1-13 of Chapter 1: "The Justification for Presenting Data Effectively" in Presenting Data Effectively.

Presenting Data Effectively

Effective Data Presentation

QNT 275 Week 1 participation Presenting Data Effectively, Ch. 2

Read pp. 21-39 of Chapter 2: "Graphics" in Presenting Data Effectively.

Image vs. No Image

How images impact

QNT 275 Week 1 participation The Role of Statistics in Business Decision-Making

Watch the "The Role of Statistics in Business Decision-Making" video.

Consider the following as you watch:

Describe probability theory.

Explain how sampling theory relates to probability theory.

Differentiate between a population and a sample.

Explain, based on the video, why statistics is important in decision-making.

Interesting Article About the Relationship Between Gender and Career

http://www.npr.org/blogs/money/2014/10/28/359419934/who-studies-what-men-women-and-college-majors?utm_source=facebook.com&utm_medium=social&utm_campaign=npr&utm_term=nprnews&utm_content=2055

I believe you all will find this article and the various statistics interesting

Thoughts

I really like this introductory activity. Frankly, when most people here the word Statistics they freak out a bit (understatement). However, if you dig deeper, you will find that we all use statistical data on a daily basis. In fact, everything that you interact with regularly. Statistics drive local, State and Federal legislation, consumer product development and offering, marketing, internet research, internet advertising etc.

Statistics helps consumer product producers, government officials, marketers to understand the needs of their market and constituency. If they have data that clearly illustrates a picture of how their population is behaving, they can then customize their product and service offering to the market place. All statistical models enable researchers to understand how the population is behaving so they can proceed with a solution.

Based on your reading of this activity, what is an example of the application of statistics in your life?

QNT 275 Week 1 participation Final Exam Preparation

Review weekly topics and objectives in preparation for the final exam.

Discuss any questions or clarifications you need with the class.

Final Exam Sample Question #3 for Week 1

Team

Check out the following question that you may see on the Final:

The two branches of the study of statistics are generally referred to asdescriptive and inferential statistics

What are the differences between Descriptive and Inferential Statistics?

Final Exam Sample Question #1 for Week 1

Team

Below is a sample question that you may see on a quiz or on the final exam based on this Week's Material

1. The study of statistics can be defined as:

Simply put Statistics is the study of collecting, analyzing, presenting, and interpreting data. What are your thoughts? Would you agree with my assessment?

Final Exam Sample Question #4 for Week 1

Team

Here is another question that you may see on a quiz or the final:

Which of the following is a quantitative variable?

House Prices

House Age

House Size

All of the Above

In this case, I would select all the above. This is because quantitative variables are numeric in nature. Which in this case, every variable that you see listed applies to a quantitative variable. What are your thoughts?

Final Exam Sample Question #2 for Week 1

Team

Below is another sample question that you could see on a quiz or on the Final:

The manager of a retail shoe store has applied statistics to analyze sales, purchasing, and data and reached the conclusion that the store could reduce costs by reducing inventory of very small and very large sizes, and substituting an online ordering service to drop-ship rare sizes directly from suppliers. How should the manager communicate those results to the store owner?

Probably the best action for the manager would be to summarize the recommendations, supported by visual representation of the data and statistics. What are your thoughts on what his/her action to be?

QNT 275 Week 1 participation Da' Hermis Burger Joint

Tell me the Mean Mode and Median for the following data: The "Hermis Burger Joint" is a real greasy joint but they make great fried chicken, waffles and apple pie (mmmm.....Pie)! You lika da Yeero?!

If you are ever on Roselle Rd. in Schaumburg Il....check out Country Doughnuts....best doughnuts east of the Mississippi!

The Hermis Burger Joint pays its 5 hourly employees $6, $3, $5, $7 and $6 per hour.

What is the Mean Mode and Median of the data.

QNT 275 Week 1 participation The Wisconsin Water Table

What is the Variance, Standard Deviation and Mean of the data below?

Wisconsin Water Table

This is a great example of a real table that you can find in academic journals or government periodicals.

All you have to do is find the mean, standard deviation and variance. We are going to be using this very same table for Z-score as well:

The following table presents a data file that lists the peak discharge from a hydroelectric project in

Wisconsin for the years 1957 to 1968. The variable peak discharge has an interval level of measurement.

Year Peak Discharge

1957 1,120

1958 2,380

1959 886

1960 1,420

1961 1,480

1962 1,200

1963 657

1964 1,280

1965 1,640

1966 1,280

1967 1,740

1968 1,380

(source: U.S. Department of the Interior, Geological Survey, Water Resources Divisions, Estimating Magnitude and Frequency of Floods in Wisconsin, 1971. p. 77. I19.2:W75)

QNT 275 Week 2 participation Essentials of Business Statistics, Ch. 3

The Median & Mode

QNT 275 Week 2 participation Essentials of Business Statistics, Ch. 7

Sampling

QNT 275 Week 2 participation Presenting Data Effectively, Ch. 2

Read pp. 40-57 of Chapter 2: "Graphics" in Presenting Data Effectively.

Bar Charts

Images

QNT 275 Week 2 participation Obtaining a Suitable Sample

Watch the "Obtaining a Suitable Sample" video.

Consider the following as you watch:

Discuss the difference between convenience and random sampling.

Explain how you can obtain a sample size representative of a population.

Provide three examples.

Explain why a sample is used in place of the population.

Thoughts on Sampling

Team

I liked this Video, it provided a very good over-view of the basics of research design. In order to execute on a research initiative that is going to apply the concepts of statistical methods, you need to collect a sample population which will represent the Overall/Universal population to which your study is examining. Your Ultimate goal, you want to eliminate the bias (sampling error) as much as possible to come up with a general conclusion about the expected behavior of the overall population based on the observed behavior of your sample population.

There are several ways you can select your sample

1. Simple Random: A sampling procedure allowing for the equal and independent chance of subjects being selected as part of the sample. This is a very important process as we will be using it to apply to all the statistical formulas. You put names in a hat and pick your sample

2. Non-probability: A sampling procedure when the likelihood of selecting any one member of the population is unknown. Not much difference between this procedure and the one above. However, you do not know the percentage of the population that you are examining.

3. Systematic sampling: A random sampling procedure where increment determine who becomes a part of the sample.

4. Stratified sample: The process of selecting a sample that represents different groups of the population.

5. Cluster sampling: A probability sampling procedure where units of subjects are selected rather than the subjects themselves.

6. Convenience sample: Represents a captive audience.

Any questions on these techniques?

QNT 275 Week 2 participation Sampling, Surveying, and Data Analysis

Great Summary

Sampling, Surveying, and Data Analysis

QNT 275 Week 2 participation Measuring Central Tendency and Variability

Watch the "Measuring Central Tendency and Variability" video.

Consider the following as you watch:

Define the three standard measurements of the central tendency and provide example of each.

Explain when you might want to use a median number.

Provide an example.

Define a standard deviation and what a standard deviation shows, provide an example

Measurement of Disperion

Team

The Measurement of Dispersion is made up of 3 mathematical techniques, the standard deviation, variance and the range. Of the 3, the standard deviation is the most accurate numeric. The purpose of the Measurement of Dispersion is to tell a researcher how close or far data points are from each other. That is it! Which means that with the Measurement of Dispersion alone, we really do not know much about the sample population.

It is not until we augment the Standard Deviation with a Z-Score, that we are able to infer a conclusion on how the sample population is behaving. The Z-Score is the most basic of statistical formulas that fall under the umbrella of Inferential Statistics. The Z-Score is the gateway to other, more sophisticated formulas such as the Z-Test, the T-test (all forms of T-Test), the ANOVA and the Regression Analysis (just to name a few).

What is a Z-score?

QNT 275 Week 2 participation Statistics Intro: Mean, Median, and Mode

Watch the "Statistics Intro: Mean, Median, and Mode" video.

Consider the following as you watch:

Explain how mean is calculated.

Explain how a median is calculated with an even number list.

Define an array.

Provide an example.

Define a mode.

Identify if you can have more than one modal number in a set of data.

Note: All Khan Academy content is available for free at www.khanacademy.org.

QNT 275 Week 2 participation Finding the Range and Mid-Range

Watch the "Finding the Range and Mid-Range" video.

Consider the following as you watch:

Define a range and provide two examples.

Calculate a mid-range and provide two examples.

Note: All Khan Academy content is available for free at www.khanacademy.org.

QNT 275 Week 2 participation Range, Variance, and Standard Deviation as Measures of Dispersion

Watch the "Range, Variance, and Standard Deviation as Measures of Dispersion" video.

Note: All Khan Academy content is available for free at www.khanacademy.org.

Standard deviation

QNT 275 Week 2 participation Psychology Research in Context: Measuring Validity

Watch the "Measuring Validity" video.

measuring valididty

QNT 275 Week 2 participation Final Exam Preparation

Week 2 Practice Problem #1

Team

Here is a very good problem that you may see on either the quiz or the final:

A company wants to estimate the mean price of oil over the past 10 years. What kind of data does the company need?

In my opinion, the data that is required is Time Series data. As Time Series data is data that illustrates a pattern of behavior over a period of time. WHat are your thoughts?

Week 2 Practice Problem #2

Team

Below is a problem that you may see on either your quiz or your final exam.

Bias can occur in sampling. Bias refers to

· the division of the population into overlapping groups

· the tendency of a sample statistic to systematically over-or underestimate a population parameter

· the creation of strata, which are proportional to the stratum's size

· the use of cluster sampling instead of stratified random sampling

In my opinion, the correct answer is in Orange. Bias is the difference between the expected value of the sample population versus the population parameter the statistic is estimating. Simply put, Bias is error to which you get that puts your final calculation either higher or lower than what the actual calculation is supposed to be to best represent the overall population.

Week 2 Practice Problem #4

Team

Below is a problem that you could see on either the quiz or the Final Exam:

What is (are) the most widely used measure(s) of dispersion?

The answer is Variance and Standard Deviation. The only other measure of dispersion left is Range and Range is not commonly used to describe the spread of data.

WHY?

Week 2 Practice Problem #5

Team

Here is a sample problem that you may see on the Final Exam:

A stem-and-leaf diagram is constructed by separating each value of a data set into two parts. What are these parts:

· Stem consisting of the leftmost digits and leaf consisting of the last digit

· Stem consisting of the last digit and leaf consisting of the leftmost digits

· Stem consisting of the leftmost digits and leaf consisting of the second digit

· Stem consisting of the second digit and leaf consisting of the last digit

A Stem and Leaf Diagram is a histogram "Like" chart that records raw numbers of a variable. The purpose of the Stem and Leaf Diagram is to show a clear picture of the Frequency Distribution of data. The Stem consists of the left most digits and Leaf consisting of the last digit.

What is an example of a Stem and Leaf Diagram?

QNT 275 Week 2 participation The Z-score

Select a specific year from the Wisconsin Water Table, and find the Z-score for the associated data point (water discharge)

Year Peak Discharge

1957 1,120

1958 2,380

1959 886

1960 1,420

1961 1,480

1962 1,200

1963 657

1964 1,280

1965 1,640

1966 1,280

1967 1,740

1968 1,380

(source: U.S. Department of the Interior, Geological Survey, Water Resources Divisions, Estimating Magnitude and Frequency of Floods in Wisconsin, 1971. p. 77. I19.2:W75)

The Z-Score

Team

It is not until we augment the Standard Deviation with a Z-score the standard deviation makes sense, such as the case with the Wisconsin Water Table. A Z-Score is a statistical formula that tells us where a specific data point falls on the bell curve in relationship to the mean/mode/median of the bell curve which is the exact middle of the curve. The Z-score tells us how many standard deviations a specific data point falls above OR below the middle of the curve (+ or -

Let's look at the Wisconsin Water Table to explain this process:

In order to get the Z-score for the year 1960. All you need to do is find the Standard deviation and the mean for the Wisconsin Water table (which we have just done in Week 1).

Xbar or μ = 1,371.91

s or σ = 436

Your observation is X which for 1960 is 1,420. Insert in the following formula:

z= X - μ

σ

.11 = 1,420 - 1,371.91

436

The Z-score falls .11 standard deviations ABOVE the mean. Look how close the amount of water discharge was for 1960 as compared to the mean of the water discharge (1,371.91). It is so close it practically kisses the mean.

This Z-score tells us that the water output for the year 1960 was just slightly greater (not by much) than the average discharge of all the years combined. Does this make sense?

QNT 275 Week 2 participation The City of Westwood

Using the T-test for one sample populations, what can you infer about the sample population based on your calculations?

In 1974, the City of Westwood was experiencing problems with their utilities. Home owners were complaining that their appliances were being blown because their homes were receiving too much voltage. The city says each home receives between 121 to 122 volts. The city was 95% confident that they are correct. Analyze the data and determine if they are correct or if the home owners had a legitimate complaint. Use 121 as the population mean

They sample 15 homes and measure the voltage:

Location Number Voltage

1 121.5

2 120.9

3 124.6

4 125.8

5 123.5

6 125.1

7 123.7

8 124.5

9 120.4

10 120.3

11 121.2

12 120.8

13 124.1

14 123

15 124.6

City of Westwood

Team

I am going to give you a kick start by showing you the first steps of the 5 step hypothesis testing process and the beginning of the T-test

1. Research Question:

R1: Are the homes in the City of Westwood receiving between 121 and 122 Volts?

2. Hypothesis Statement

H1: Homes in the City of Westwood ARE receiving between 121 and 122 Volts

Ha: Homes in the City of Westwood ARE NOT Receiving between 121 and 122 Volts

3. Confidence Level

We are 95% confident that are main hypothesis statement is TRUE

95% level of confidence equates to a +/-2.145 (Go to T-table, find the .05/95% column, your Degrees of Freedom = N -1 or 15-1 =14 you find the T-critical of +/-2.145

Step #4: Apply the T-test

T=Xbar-µ

s/√n

Xbar = 122.9333 (which you must calculate yourself)

U = 121

S = 1.8953 (which you must calculate yourself)

N = 15

122.9333 - 121

1.8953/√15 = ?

Now you calculate the rest, what is the T-calculation?

QNT 275 Week 3 participation Essentials of Business Statistics, Ch. 4

Thoughts on this Chapter (By Hermis)

Team

This was an interesting chapter, I think the use of data from space to discuss the conversion and inference of data. Any time you have raw data, you can convert the data into a numerical set and apply a statistical technique to it in order to understand how the sample population is behaving. Are you interested in understanding if life on another planet exists? If "yes" the formula you can use is called Drake's Equation. This equation is a guesstimate and tells the researcher the probability of life on another planet based on radio waves received from space.

Let's take this down to earth to something more applicable. You can use statistical probability to determine the probability of something occurring. There are 3 major types of probability. They are continuous probability, discrete probability and the probability of independent events.

Of the 3 probabilities, which probability represents the role of dice, or a quarter?

The Probability of Independent Events (Example by Hermis)

Team

Check out this scenario I created this example (step - by step) on how to solve a Probability of Independent Events. What is one workplace scenario that you could apply the Probability of Independent events to?

QNT 275 Week 3 participation Essentials of Business Statistics, Ch. 5

Binomial Probability Distribution

Risk Neutrality and Risk Aversion

QNT 275 Week 3 participation Essentials of Business Statistics, Ch. 6

One of my Favorite Chapters! (Normal Distribution)

Team

This is one of my favorite aspects of statistics. It is called Normal Distribution and it is at the epicenter of statistical and hypothesis testing. I am going to explain Normal Distribution in more detail below and provide you with a lot of fun examples for you to walk through that illustrate how to find the Area of Curve which is directly associated to Normal Distribution:

What is Normal in the world of statistics?

You may be wondering this on a daily basis while in this class? Not really!

Under the normal curve, our distribution of scores is called the Empirical Rule and it is as follows:

The Robust Empirical Rule:

68% of all observations are within 1 standard deviation away from the mean

95% of all observations are within 1.96 standard deviations away from the mean

95.44% of all observations are within 2 standard deviations away from the mean

99% of all observations are within 2.57 standard deviations away from the mean

99.7% of all observations are within 3 standard deviations away from the mean

The normally distributed shaped bell curve is a purely theoretical concept in which the horizontal (X) axis represents all possible values of a variable (such as the Z-calculated). Examples include the Wisconsin Water table Z-score for the year 1960 (.11) that we covered in Week 2. You can see an this example under the Learning Activity called "The Z-score" . You can plot these points along the horizontal (X) axis of the bell curve and they will fall within a range. As you can see the 1960 Z-score of .11 is within 1 standard deviation of the mean (to the right).

The vertical (Y) axis of the scores tells us the probability of these values occurring. Look at the empirical rule, the Z-score for the year 1960 (.11) is within 68% of all observations being 1 standard deviation away from the mean. For the Z-calculated of -29, the scores are probably 99.99999999% of all observations. It is really hard to exactly determine the % of observations since we can go into infinity of Z-calculated and on the bell curve. Infinity of the Z-calculated will always be possible because our Bell Curve vertical (Y) axis NEVER touches our horizontal (X) axis. It just grows increasingly closer but the naked eye will never be able to distinguish a separation of the 2 axis. Hence, a Z-calculated of -29 is probably 99.9999999% of all scores away from the mean. It is impossible for me (or you) to determine this % since we do not have sophisticated software to tell us the exact % of all observations that this particular score falls under.

Normal distribution of the bell curve ties into our discussions about significance and non-significance for hypothesis testing which we will have next week. Essentially, we are determining if our observations are normal and if our observations are not normal. As you may recall, a relationship (of variables) is created by comparing our OBSERVED (behavior) calculation to our EXPECTED (behavior) critical.

Properties of the Normal Distribution are:

1. The normal distribution curve is bell shaped.

2. The mean (mode or median) is in the middle of the distribution of the bell curve (measurements of central tendency).

3. Both the left side of the curve and the right side of the curve are equal and symmetric.

4. The normal distribution curve has only one mode.

5. The curve is continuous, the Y-axis (vertical) and the X-axis (horizontal) will never meet and your observed calculations can run into infinity. It will be possible to have observed scores be 99.9999999999% or greater of all observations being x amount of standard deviations away from the mean.

6. For each value of X there is a value for Y.

7. The total area under the curve (left side negative + right side positive) will always equal 1 or 100%

8. The areas under the normally distributed bell curve follow the robust empirical rule

Now, let's determine the area under the bell curve that are particular Z-calculation or Z-score falls under. This means we want to know the % that our particular Z-score covers on the bell curve. I like to refer to it as the real estate the Z-score covers on the Bell curve.

As an example, say we calculated a Z-score of 2.34. Let us find the area between the mean (which is the middle of the curve and always denoted by a 0) Z = 0 and Z = 2.34. To determine the area under the bell curve go to your Normal Distribution Table located in your book (appendix). You need to go the Z-column and find 2.3 and then .04 in the top row, you should see .4904. This is where the column and the row meet in the table. The answer is .4904. Hence the area is .4904 or 49%.

This means that the area under the curve that the Z-score of 2.34 covers is 49%. Any questions?

QNT 275 Week 3 participation Probability and Conditional Probability in Business Decision

Watch the "Probability and Conditional Probability in Business Decision-Making" video.

Consider the following as you watch:

Define probability.

Provide three examples of probability.

Define conditional probability.

Provide three examples of conditional probability.

Describe what questions you might ask to determine probability of risks when starting your own company.

Provide at least three questions.

QNT 275 Week 3 participation Random Variable

Watch the "Random Variable" video.

QNT 275 Week 3 participation Final Exam Preparation

Week 3 Practice Problem #1

Team

Below is a problem/question that you may see on the Quiz or the Final:

Mutually exclusive events ______________.

What does the term "Mutually Exclusive" mean? Well it means that the events are completely unrelated. Hence you would answer with:

· do not share common outcomes

· may share common outcomes

· do not contain all possible outcomes

· contain all possible outcomes

What are your thoughts on Mutually Exclusive as it relates to Probability in the Week 3 Material? Do you think I am correct on my answer?

Week 3 Practice Problem #2

Team

Below is a problem that you may see on the Final Exam or on the Quiz. It is on one of my favorite topics which is Frequency Distribution (Table) and The Normal Distribution:

Frequency distributions may be used to describe which of the following types of data?

· Nominal and interval data only

· Nominal, ordinal, interval, and ratio data

· Nominal and ordinal data only

· Nominal, ordinal, and interval data only

Basically, a Frequency Distribution Table is a table that illustrates the frequency that an answer was provided. Hence it is Categorical in Nature and You can Rank Your Data in the Frequency Distribution Table. With that stated, if I have Categorical Data and Ranked Data, which Levels of Measurement apply to these types of Measurements? (Trick Question)

Okay, let's make it a bit harder. Why would Interval data not apply to a Frequency Distribution?

Week 3 Practice Problem #3

Team

Below is a problem that you may see on the Final Exam or on the Quiz. It deals with the concepts of Independent and Dependent variables which are present in ALL Hypothesis Statements:

What is the name of the variable that's used to predict another variable?

· Response

· Standard error of the estimate

· Coefficient of determination

· Explanatory

Okay, I am giving away too many answers. Why is it the Explanatory Variable and Is the Explanatory Variable associated to the Independent or the Dependent Variable?

Week 3 Practice Problem #4

Team

Below is a Problem that you may see on the Final Exam or the Quiz. It deals with the concepts of the Continuous Probability, which is the probability of an event occurring and is associated to Normal Distribution:

A hedge fund returns on average 26% per year with a standard deviation of 12%. Using the empirical rule, approximate the probability the fund returns over 50% next year.

The answer is 2.6% WHY? How would you come up with this answer? You must use the concept of Continuous Probability and Normal Distribution to find your answer

QNT 275 Week 4 participation Essentials of Business Statistics, Ch. 1

One of my Favorite Topics (levels of Measurement)

Team 275

A quick review before the Final Exam

In this class, the independent variable will 95% of the time be a nominal level of measurement. The dependent variable will be an interval level of measurement.

Here is a great cheat sheet explanation that I created:

Our independent variable and dependent variables will be broken down into 1 of 3 categories: Nominal, Ordinal and Interval

Nominal (Independent variable) is categorical in nature and is solely qualitative (non-number related). They are categories such as Hair Color:

Blonde or Brunette or Red-Head.

Ordinal (one way to measure the dependent variable) is data that is placed in an order. A great example is grade point average. Nigel(2.0), Jackie W. (2.5), Jackie H. (3.2), John (3.7) and Ronnie (4.0) (lowest to highest GPA)

Interval (one way to measure the dependent variable) is data that is equal intervals along a continuum. Temperature thermostat is the best way to show interval data. Numbers can move up and down with no set pattern.The Hermis rule is: Interval data can always be converted to Ordinal data

Thoughts on This Chapter

Team

I enjoyed this chapter, I thought it was pretty interesting. In particular, I enjoyed the section on qualitative versus quantitative data. As you are aware, quantitative data is data that is numerical in nature. On the other hand, qualitative data is non-numeric data. It is words that constitute a data set. For example,

Colors of the rainbow:

Purple

Green

Yellow

Orange

Red

THe quantitative data can be measured and categorized. What are the levels of measurement for quantitative data?

QNT 275 Week 4 participation Essentials of Business Statistics, Ch. 3

Review section 3.7 of Ch. 3: "Covariance and Correlation," in Essentials of Business Statistics

QNT 275 Week 4 participation Essentials of Business Statistics, Ch. 12

Thoughts on Regression Slope Standard Error

Standard Error of Slope =

The standard error of slope is used to calculate the T-Statistic (The T-test). In this case, the T-statistic (The T-Test) is used to determine whether or not your Regression Line is Statistically Significant. In other words, does your Regression Line enable you to accept or reject your Hypothesis Statement regarding the relationship between the 2 variables. If the Regression Line is Statistically Significant, then we have to reject our Hypothesis Statement because there is something happening within the data that is outside of normal behavior.

The standard error of the slope is minimized when we have a large sample, which means the more accurate our calculation is about the true relationship between the 2 variables. This is all tied into the Least Squares Regression Line which the video discusses. Thousands of lines can be drawn through the data points. However, only one line best illustrates a true fit and best describes the relationship/strength of the variables. This one line is called the Least Squares Regression Line, it is the line that has the minimal amount of error within its' slope (Seb).

I have discussed the Linear Regression Coefficient in other posts this week (AKA correlation coefficient). This Coefficient is the one numeric that best illustrates the strength of the relationship between 2 variables.

Strength of the relationship exists between -1 to 0 to +1. The closer this numeric/coefficient comes to 0, the less likely a relationship exists between 2 variables. The closer the numeric/coefficient is to +1 the stronger a positive relationship exists between 2 variables.

What happens when the numeric/coefficient is closer to -1, again what kind of relationship is it?

Regression Line and slope (A quick thought and question)

The steepness or angle of the Regression Line is identified by "b". Slope is "b" and the Y Intercept is "a".

You will calculate slope by taking any 2 points on the line and dividing the rise (vertical distance) between them by the run (horizontal distance) between them. Also known as Rise over Run (Rise/Run)

In what direction does the Line Slope in a Positive Relationship?

QNT 275 Week 4 participation Essentials of Business Statistics, Ch. 13

Dummy Variables

QNT 275 Week 4 participation Correlation and Causality

Watch the "Correlation and Causality" video.

Define a correlation and provide an example

Define causality?

Explain if correlation causes causality.

Provide evidence with an example.

Note: All Khan Academy content is available for free at www.khanacademy.org.

QNT 275 Week 4 participation Using Time Series to Analyze Business Trends

Watch the "Using Time Series to Analyze Business Trends" video.

Identify the four components of time series.

Provide an example of each component and how it might be used in business

Explain if this data becomes obsolete over time.

QNT 275 Week 4 participation Career Connection Discussion: Forecasting

Discuss the following:

Explain what you have learned throughout this course with probability and how you think that will affect your forecasting decisions in your current or future company.

Predict how you will use probability in your current or future company?

Thoughts on Forecasting

A great application of Forecasting is a statistical process that enables us (as researchers) to predict a future quantity. When an organization uses historical data that tracks sales, by day, by week, by month and by Quarter, understanding the projection's "peaks and valleys" of sales productivity helps your organization determine requirements as it relates to allocating resources in order to maximize profitability. Forecasting also enables The Federal Reserve to predict Inflation Rates.

What is another classic example of forecasting?

How to Survive a Zombie Apocalypse

Team

As you know by now, I really like Zombies and have been a fan of the Genre long before it became "hip" to like Zombies. Check out the following study (article) below. Grad Students from Cornell University conducted a Statistical Modeling Exercise that combines such statistical techniques as Forecasting, probability and Logistic Regression in order to determine the safest place to be and to hold out in order to survive a Zombie Outbreak.

It is a very interesting read and details how they used various statistical techniques to identify the best location if a Zombie outbreak did occur. http://www.smithsonianmag.com/smart-news/statisticians-reveal-best-place-wait-out-zombie-apocalypse-180954426/?no-i

QNT 275 Week 4 participation Final Exam Review

Week 4 Practice Problem #1

Team

Below is a problem that you may see on the Final Exam or the Quiz:

Which of the following violates the assumptions of regression analysis?

· The error term does not have the normal distribution.

· The error term has a constant variance.

· The error term is uncorrelated with an explanatory variable.

· The error term has a zero mean.

I chose the 1st option because there are 4 assumptions of Regression Analysis and the 1st option does not align to the 4 assumptions:

1. There is a linear relationship between the Independent and the Dependent Variable

2. There is a statistical indepedendence of errors

3. Homoscedasticity of the errors which means there is a constant variance

4. The Error does follow the Normal Distribution

What does the term Homoscedasticity mean?

Week 4 Practice Problem #2

Team

Below is a problem that you may see on the Final Exam or on the Quiz

In the sample regression equation what is ?

· The value of y when x = 0

· The y-intercept

· The predicted value of y, given a specific x value

· The slope of the equation

Why would I have chosen the 3rd option? What is the relationship between X (the Independent Variable) and Y (the Dependent Variable)?

Week 4 Practice Problem #3

Team

Below is a problem that you may see on the Final or on the Quiz

What type of relationship is indicated in the scatterplot?

· A positive linear or curvilinear relationship

· A negative linear relationship

· No relationship

· A negative curvilinear relationship

I picked the 1st option, because a positive relationship is illustrated on a Scatter Plot when the data points move from the bottom left to the upper right. What would a Negative Relationship look like if it was illustrated?

Week 4 Practice Problem #4

Team

Below is a problem that you may see on the Final or on the Quiz:

Serial correlation is typically observed in:

· sparse data

· cross-sectional data

· time series data

· outliers

Why would I have selected Time Series Data?

QNT 275 Week 5 participation Presenting Data Effectively, Ch. 5

Presenting Data Effectively

QNT 275 Week 5 participation Presenting Data Effectively, Ch. 6

Final Thoughts

Presenting Data Effectively

QNT 275 Week 5 participation Managing the Data Deluge

Watch the "Managing the Data Deluge" video.

Define a data management plan?

Provide an example of good data and an example of bad data.

Explain the following: Other than collecting primary data where might you search for secondary data?

QNT 275 Week 5 participation Instinct, Debate, and Data Make the Best Decisions

Watch the "Instinct, Debate, and Data Make the Best Decisions" video.

Identify how to make the best decisions,

Provide an example.

Explain whether data the only element used in decision making.

Justify whether instinct should play a role in decision-making.

QNT 275 Week 5 participation Interpret and Communicate Data Analysis Results

Watch the "Interpret and Communicate Data Analysis Results" video.

Describe how data can be interpreted differently.

Explain how and why data is scrubbed.

Describe three different ways to communicate data.

Thoughts on this Video (By Hermis)

Team

This video was pretty good, they are describing the 5 Step Hypothesis Testing process. In fact, any and all collection of data, scrubbing of data, analyzing the data, and inferring a conclusion from the data. In order to ultimately make a decision on how to move forward to solve the problem. The video brings up a good point regarding how to present your conclusions. It is really important to present your conclusions as objectively as possible as you do not want to risk offending your management team/clients as this video had discussed

QNT 275 Week 5 participation PowerPoint® 2013 Essential Training

Watch the instructional video by clicking on the Lynda.com® link.

Watch all of the tutorials from "PowerPoint® 2013 Essential Training

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