Multiple Regression Exercise

A decision was made to analyze the macroeconomic aggregate data to determine the extent to which durable goods sales may be expected to change in relation to changes in the unemployment rate, changes in the inflation rate, and changes in the GNP. The results of this analysis will provide the managers of the appliance chain with information that will improve the quality of their decisions related to inventory stocking level decisions.

The principal statistical analysis procedure which was used in assessing the relationship between changes in durable goods sales (as the dependent variable) and changes in the unemployment rate, the rate of inflation, and the GNP (as independent variables) was multiple regression analysis. The predicting equation resulting from this multiple regression analysis was as follows:

Durable goods sales = -5.119423 - 3.237005(unemp rate) - 1.209123(infla rate) + 1.586942(GNP).

Charts illustrating the relationships between the dependent variable and each of the three independent variables are presented in the Appendix on pages 6-7.

This research examined the relationship between durable goods sales, as a dependent varia

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n annual basis. Although 13 years of data were collected, the measurement of year-to-year changes in the data sets limited the time points in the analysis to 12. Multiple regression analysis involves the analysis of the relationship between one dependent variable and two or more explanatory, or independent, variables. The statistical concepts of both regression and correlation are valuable tools. Regression coefficients permit the projection of movements in one variable based on movement in another variable or in a set of other variables. Correlation coefficients establish both the strength of relationships between variables, and the nature of such relationships·positive or negative. What a correlation coefficient does not do, however, is to establish a causal relationship between the variables. The multiple regression formula is y = a + bx + cx, and so forth depending upon the number of explanatory variables included in the analysis. In this equation, y is the dependent variable value for a time period, a is the estimated variable value for time zero (the constant), b, c, and any other representations of explanatory variables are the change in the variable value per time unit, and x is the time unit. The basic data for d
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Some common words found in the essay are:
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Approximate Word count = 1257
Approximate Pages = 5 (250 words per page)

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