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# Multiple Regressions Week 4 Assignment Chapter 12 Problem 64 (Math Problem Sample)

Instructions:

For this assignment, you are required to complete Problem 64 (p. 593) in Chapter 12 of your textbook. Use the appropriate Excel file templates from www.cengagebrain.com. Once complete, post your Excel Document in Waypoint. Show all work.
23-64) Let Yt be the sales during month t (in thousands of dollars) for a photography studio, and let Pt be the price charged for portraits during month t. The data are in the file P11_45.xlsx (Links to an external site.)Links to an external site.. Use regression to fit the following model to these data:
Yt = a + b1Yt−1 + b2Pt + et
This equation indicates that last month's sales and the current month's price are explanatory variables. The last term, et, is an error term.
If the price of a portrait during month 21 is \$10, what would you predict for sales in month 21?
Does there appear to be a problem with autocorrelation of the residual? Explain your answer.

source..
Content:

Multiple Regressions: Week_4_Assignment_Chapter_12_Problem 64
Multiple Regressions
Name
Institution
Date

If the price of a portrait during month 21 is \$10, what would you predict for sales in month 21? There is a lagged dependent variable (sales) and this is an autoregressive model or a dynamic model. In the first observation there was no data for the lagged sales lag value in undefined observations is set to \$ 400,000
Yt = a + b1Yt−1 + b2Pt + et]
Y[t] =661350.2732 0.7703 b1 [Yt−1] - 19396.3 b2 [Pt] + e[t]
Y[t)661350.2732 0.7703 b1 [\$907,000] - 19396.3 (10)+ e[t] = \$ 1 166, 085
SUMMARY
Regression Statistics
Multiple R
0.834283846
R Square
0.696029536
0.660268305Standard Error228705.965Observations20
ANOVA

...
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