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

QNT 275 All Participations Link

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

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?

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

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?

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