An analyst wishes to know

An analyst wishes to know

Question 1 
An analyst wishes to know if there is a correlation in share prices for two airlines – Air Canada and West Jet. Determine the correlation coefficient for the data below. Interpret the results of the correlation coefficient.

Question 2 

Calculate the statistical linear regression line for the data below. Interpret the excel output. Use the equation of the line to predict the cost for year 7.

Question 3                                                                                                                         
Starbucks has experienced continued rapid growth in recent years. A financial analyst at their corporate head office wanted to determine if they could predict revenue with a predict model using the number of stores, number of drinks offered and average weekly earnings as potential predictors. Using the data below develop a multiple regression model. Interpret the results.

Question 4                                                                                                                     
A publisher’s information bureau wanted to know if Magazine Advertising Expenditures could be predicted based on household equipment and Supply expenditures. Two models were developed, one using Household Equipment and Supply Expenditures only as a predictor and one using both Household Equipment and Supply Expenditures and (Household Equipment and Supply Expenditures)2. Develop , interpret and compare these models to each other. Which model is better? Do the model results suggest a different model may be required? Why or why not?

Total Magazine Advertising

Exp ($millions)    Household Equipment and Supply

Exp ($millions)    (Household Equipment and Supply

Exp)^2 ($millions)^2

1193      34           1156

2846      65           4225

4668      98           9604

Question 5                                                                                                                A market analyst for a fast food restaurant wanted to determine if the amount spent at restaurant could be predicted based on a customer’s age and gender. Develop the appropriate model using the data below and interpret the results. If a 20 year old male walks into the store what would the model predict the customer will spend?

Question 6

use the data below to develop a model which predicts y. In your model include not only x1 and x2 but also the square of each x variable and the interaction variable of x1 and x2. Interpret the excel output.

          

               Y             X1           X2           X1*X2    X1^2      X2^2

               2002      10           3             30           100        9

               1747      5             14           70           25           196

               1980      8             4             32           64           16

               1902      7             4             28           49           16

               1842      6             7             42           36           49

               1883      7             6             42           49           36

               1697      4             21           84           16           441

               2021      11           4             44           121        16

                                                                                         

Question 7 (8 marks)                                                                   

use both x1 and the log(x1) to develop a model which predicts log(y). Interpret the results. If x1=500 what does the model predict for the value of y?                                                               

               Y             log(Y)     X1           log(X1) 

               20415    4.3099   850        2.9294  

               11631    4.0656   146        2.1644  

               17818    4.2509   521        2.7168  

               15303    4.1848   304        2.4829  

               22487    4.3519   1029      3.0124  

               21988    4.3422   910        2.9590  

               16444    4.2160   242        2.3838  

               13245    4.1221   204        2.3096  

               17567    4.2447   487        2.6875  

               12451    4.0952   192        2.2833  
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