1. For Problem 12, run a multiple regression analysis and evaluate the model by performing and responding to all parts of the "Multiple Regression Analysis Checklist." Also, respond to the stated questions in the problem. Use the Palisade DecisionTools Excel software to perform the regression analysis

Problem 12 from Chapter 13Practical Management Science, 6th Edition:

Suppose you are an analyst for a company that produces four products, and you are trying to decide how much of each product to produce next month. To model this decision problem, you need the unit variable production cost for each product. After some digging, you find the historical data on production levels and costs in the file P13_12.xlsx. Use these data to find estimates of the unit costs you need. You should also find an estimate of the fixed cost of production. Will this be of any use to you in deciding how much of each product to produce? Why or why not?

Data:

These are the results of the regression analysis performed with Palisade DecisionTools:

Multiple Regression Analysis Checklist:

1. What is the analyst trying to determine?
2. What are the variables? List the Dependent and Independent variables.
3. State the null hypothesis, H0, and the alternative hypothesis, H1
4. Run the linear regression. What is the linear equation? Interpret it.
5. Select a level of significance (assume = 0.05)
6. What is the overall statistical significance (i.e., what is the overall p-value)? Reject or not reject H0?
7. Assess the statistical significance for each independent variable. Drop/retain appropriate variables accordingly.
8. Run a correlation analysis and assess the possibility of multicollinearity.
9. Run several regression analyses as needed to derive a final best fit model.
10. Interpret the Adjusted R2 value for the overall model.
11. Interpret the Standard Error value for the overall model.
12. Run the standardized residualscheck for outliers (report discovered outliers)
13. Assess normality in standardized residuals distribution.
14. What is the conclusion of this regression analysis? Is the predictive equation a good one?

Month Units of product 1 Units of product 2 Units of product 3 Units of product 4 Total production cost 1 368 376) 435 451 $73,271 2 6541 531 550 1264 $136,260 3 287 519 404 612 $83,189 4 74: 676 426 571 $71,316 5 315 790 1741 629 $79,429 6 463 468 570 900 $100,897 7 369 624 327 894: $85,651 8 434 796 455: 599 $99,197 9 260 586 428 1029 $92,610 10 438 686 494 1020 $106,545 11 329 734 484 974 $102,1871 12 375 464 358 935 $86,1821 13 303 576 381 987 $85,200 14 198 283 280 684 $64,319 15 451 604 512 928 $116,988 16 349 618 425 1011 $95,426 17 143 621 384 683 $81,205 18 313 567 493 865 $93,629 E H4 B D E F G . H K A 7 StatTools Report 2 B 4 5 5 5 Analysis: Regression Performed By: Date: Saturday, April 2, 2022 Updating: Static Variable: Total production cost 7 Multiple R R R-Square Adjusted R-square Std. Errof Estimate Rous Ignored Outliers 0.9625 0.9265 0.9038 5351.220999 0 0 3 Mwtiple Regression for Totol production cost Summary 0 1 2 3 ANOVA Table Degrees of Freedom Sumof Squares Meanof Squares F p-Value p 4 Explained 4 40.93879976