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Regression Analysis

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How would I compute a simple regression formula using the interest rates and number of housing starts provided below. Also, once I have computed the regression formula, how would answer the following questions???

What is the regression formula that you computed?

What would the approximate number of housing starts be at the following interest rates: 8.5%, 4.5%, 3.7%, 2.3%?

If you were the owner of a business in the housing construction sector and you knew interest rates were going to change; how could you use this information to make better decisions?

Housing Starts in Relation to Interest Rates

Interest Rate Housing Starts

11% 9,000

10% 10,000

9% 24,000

8% 40,000

7% 52,000

6% 65,000

5% 80,000

4% 100,000

3% 130,000

2% 135,000

The attached file is a small article on interest rates that should be used as a reference...

This website is suppose to help compute the regression formula: http://people.hofstra.edu/faculty/Stefan_Waner/newgraph/regressionframes.html

(Use the simple regression equation y = mx + b. NOTE: Enter percentages as .02, .03, .04, etc.)

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Solution Summary

What is the regression formula that you computed?

What would the approximate number of housing starts be at the following interest rates: 8.5%, 4.5%, 3.7%, 2.3%?

If you were the owner of a business in the housing construction sector and you knew interest rates were going to change; how could you use this information to make better decisions?

Housing Starts in Relation to Interest Rates

Interest Rate Housing Starts

11% 9,000

10% 10,000

9% 24,000

8% 40,000

7% 52,000

6% 65,000

5% 80,000

4% 100,000

3% 130,000

2% 135,000

The attached file is a small article on interest rates that should be used as a reference...

This website is suppose to help compute the regression formula: http://people.hofstra.edu/faculty/Stefan_Waner/newgraph/regressionframes.html

(Use the simple regression equation y = mx + b. NOTE: Enter percentages as .02, .03, .04, etc.)

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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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