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# Regression analysis

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27. Administrators at State University believe that decreases in the number of freshman applications that they have experienced are directly related to tuition increases. They have collected the following enrollment and tuition data for the last decade.

Year Freshman Annual
Applications Tuition (\$)
1 6,050 3,600
2 4,060 3,600
3 5,200 4,000
4 4,410 4,400
5 4,380 4,500
6 4,160 5,700
7 3,560 6,000
8 2,970 6,000
9 3,280 7,500
10 3,430 8,000

28. Develop a linear trend line model for the freshman applications data at State University in problem 27.

a. Does this forecast appear to be more or less accurate than the linear regression forecast developed in problem 27? Justify your answer.
b. Compute the correlation coefficient for the linear trend line forecast and explain its meaning.

https://brainmass.com/math/interpolation-extrapolation-and-regression/regression-analysis-153092

#### Solution Summary

Step by step method for regression analysis is discussed here. Regression coefficients, coefficient of determination, scatter diagram and significance of regression model are explained in the solution.

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## Statistics Problems - Regression Analysis, Autocorrelation, Multicollinearity

1. Suppose an appliance manufacturer is doing a regression analysis, using quarterly time-series data, of the factors affecting its sales of appliances. A regression equation was estimated between appliance sales (in dollars) as the dependent variable and disposable personal income and new housing starts as the independent variables. The statistical tests of the model showed large t-values for both independent variables, along with a high r2 value. However, analysis of the residuals indicated that substantial autocorrelation was present.

a. What are some of the possible causes of this autocorrelation?

b. How does this autocorrelation affect the conclusions concerning the significance of the individual explanatory variables and the overall explanatory power of the regression model?

c. Given that a person uses the model for forecasting future appliance sales, how does this autocorrelation affect the accuracy of these forecasts?

d. What techniques might be used to remove this autocorrelation from the model?

2. Suppose the appliance manufacturer discussed in Exercise 1 also developed another model, again using time-series data, where appliance sales was the dependent variable and disposable personal income and retail sales of durable goods were the independent variables. Although the r2 statistic is high, the manufacturer also suspects that serious multicollinearity exists between the two independent variables.

a. In what ways does the presence of this multicollinearity affect the results of the regression analysis?

b. Under what conditions might the presence of multicollinearity cause problems in the use of this regression equation in designing a marketing plan for appliance sales?

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