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 anddetermine 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 dependentvariable? Why?7. Write down the regression line equation.8. Estimate the value of ŷ if the value of X is 20.

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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

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

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.4: Multiple Regression Models

Problem 14P: An antique collector believes that the price received for a particular item increases with its age...

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A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?
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?
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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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