Correlations/Linear & Multiple Regression

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Correlations/Linear & Multiple Regression

Note that this discussion is due on Day 6. Although the initial post is due on Day 6, you are encouraged to start working on it early as it includes work in Excel. Prior to beginning work on this assignment, read Chapter 10.

Complete Problem 50 in Chapter 10 on page 477.

10-55) A golf club manufacturer is trying to determine how the price of a set of clubs affects the demand for clubs. The filP10_50.xlsx contains the price of a set of clubs and the monthly sales.

  1. Assume the only factor influencing monthly sales is price. Fit the following three curves to these data: linear (Y = a + bX), exponential (Y = abX), and multiplicative (Y = aXb). Which equation fits the data best?
  2. Interpret your best-fitting equation.
  3. Using the best-fitting equation, predict sales during a month in which the price is $470.

In the discussion area, attach the Excel document showing work.

Partial Solution 

Based on my computations, the best-fitting equation is the  Exponential Regression Equation (Y = abX).  Of the three models, the exponential equation has the largest corresponding R2   (coefficient of determination). R-squared indicates the percentage of the variance in the dependent variable that the independent variable explains collectively (Albright & Winston,2017)

R2   for the linear equation = 0.9 (this static indicates that 90% of the variation in demand for clubs is due to variation  in price.

R2   for the multiplicative equation = 0.9487 (this indicates that  in this model, 94.87% of the variation in demand is due to variation in price.

R2   for the exponential equation = 0.999….Read more


Albright, S. C, & Winston, W. L. (2017). Business analytics: Data analysis and decision making (6th ed.). Retrieved from:

Correlations/Linear & Multiple Regression