Problem 14.43 page 600
Problem 43 page 192
The owner of a moving company would like to develop a model of predicting
the labor hours that is required to complete a move by using the number
of cubic feet moved and whether there is an elevator in the apartment building.
He has collected data for 36 moves . The data are stored in the table
Hours Feet Elevator
24.00 545 Yes
13.50 400 Yes
26.25 562 No
25.00 540 No
9.00 220 Yes
20.00 344 Yes
22.00 569 Yes
11.25 340 Yes
50.00 900 Yes
12.00 285 Yes
38.75 865 Yes
40.00 831 Yes
19.50 344 Yes
18.00 360 Yes
28.00 750 Yes
27.00 650 Yes
21.00 415 No
15.00 275 Yes
25.00 557 Yes
45.00 1028 Yes
29.00 793 Yes
21.00 523 Yes
22.00 564 Yes
16.50 312 Yes
37.00 757 No
32.00 600 No
34.00 796 Yes
25.00 577 Yes
31.00 500 Yes
24.00 695 Yes
40.00 1054 Yes
27.00 486 Yes
18.00 442 Yes
62.50 1249 No
53.75 995 Yes
79.50 1397 No
a. State the multiple regression equation.
b. Interpret the meaning of the slopes b1 and b2, in this problem.
c. Interpret the meaning of the regression coefficient b0.
d. Predict the mean labor hours for moving 500 cubic feet in the apartment building that has an elevator
e. Compute the coefficient of multiple determination, r^2 and interpret its meaning.
f. Compute the adjusted r^2.
g. Construct a 95% confidence interval estimate of the population slopes.
h. At the 0.05 level of significance, determine whether each independent variable
makes a significant contribution to the regression model.
Perform t-test for the slopes of the regression equation.
i. Add an interaction term to the model and at the 0.05 level of significance,
determine whether it makes significant contribution to the model.
Please see the attachments.
Please note that this is not a hand in ...
The solution provides step by step method for the calculation of multiple regression analysis. Formula for the calculation and Interpretations of the results are also included.
Develop a regression model for movie demand
I have also attached a sample xls file that contains the 5 steps referred to in the word doc. The data is located in the movie.xls file.
Develop a regression model for movie demand. Follow the five steps outlined in the sample XLS file.
? Accomplish a correlation table and scatter plots with trend lines of all of the variable data: price, DVD rental price, DVD purchase price, popcorn price, and income versus demand.
? Do a linear regression of all of the data.
? Based on your interpretation of your trend line outcomes, do a data transformation of the variables and regression.
? Write out the regression equations for the linear and transformed models.
? Use your equations to calculate predicted demand values.
? Analyze the coefficients in your model using the confidence level test of p-values. Are they statistically different from zero based on passing the 95% confidence level test?
? Analyze the models overall based on Sig F values. Are they statistically different from zero based on passing the 95% confidence level test?
? How well do the models capture the variability in demand based on their R Square values?