Practical Management Science
6th Edition
ISBN: 9781337671989
Author: WINSTON
Publisher: Cengage
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Chapter 14.2, Problem 1P
The file P14_01.xlsx contains data on 100 consumers who drink beer. Some of them prefer light beer, and others prefer regular beer. A major beer producer believes that the following variables might be useful in discriminating between these two groups: gender, marital status, annual income level, and age.
- a. Use logistic regression to classify the consumers on the basis of these explanatory variables. How successful is it? Which variables appear to be most important in the classification?
- b. Consider a new customer: Male, Married, Income $42,000, Age 47. Use the logistic regression equation to estimate the probability that this customer prefers Regular. How would you classify this person?
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Please help me with thirdly part 1,2, and 3
Collect sufficient data about any two quantitative variables (logically correlated)
using reasonable sets of values (At least 15 values).
Use Microsoft Excel and answer the following:
Firstly: Determine the independent variable and the dependent variable.
Secondly: Make the correlation analysis for the quantitative variables and
determine the correlation coefficient, and interpret it.
Thirdly: Do the regression analysis and answer the following questions:
1. What is the type of regression in your example?
2. Find the sample size.
3. Find the degrees of freedom.
4. Is your model valid? Why?
5. Find the value of the coefficient of determination, and interpret it.
6. Is the independent variable having a significant effect on the dependent
variable? Why?
7. Write down the regression line equation.
8. Estimate the value of ŷ if the value of X is 20.
Paraphrase this one. Analyze and elaborate in 85 words. Regression analysis data also helps you understand whether the relationship between two different variables can give way to potential business opportunities.
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- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?arrow_forwardThe file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.arrow_forwardAn antique collector believes that the price received for a particular item increases with its age and with the number of bidders. The file P13_14.xlsx contains data on these three variables for 32 recently auctioned comparable items. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Is the antique collector correct in believing that the price received for the item increases with its age and with the number of bidders? Interpret the standard error of estimate and the R-square value for these data.arrow_forward
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