M4A1 Solutions Focused Decision Making

Research Question: How effective is the simple linear regression model when predicting sales?

The simple linear regression model takes several values and plots them on a x- and y-axis scatter diagram, approximated by a straight line. “To construct the scatter plot, each value of y is plotted against its corresponding value of x. If the y values tend to increase or decrease in a straight-line fashion as the x values increase, and if there is a scattering of the (x, y) points around the straight line, then it is reasonable to describe the relationship between y and x by using the simple linear regression model” (Bowerman, O’Connell, & Murphree, 2009, p. 515). There are several issues when using a linear regression model to measure sales:

1. The values that are used by be inaccurate. The sales that have been generated may be incomplete, over or under exaggerated, or simply lost.

2. The values that may be presented are just estimates. The sales numbers my be inaccurate and confuse the data on the regression model.

3. The regression model only shows possibility of sales, not actual sales. The approximated straight line represents where sales should be. Sales shown are hardly ever on the line.

Solutions to these issues with the linear regression model can be fixed. Using the same x- and y-axis concept, it can be resolved by:

1. Receiving concrete numbers and a more realistic regression line. Having duel regression lines will create a more idealistic look and show equilibrium where sales need to be and where they are going.

2. Use a bar graph or line graph instead. These are easier to follow and still use the x- and y-axis format.

3. Using the alternative graphs, compare sales from two different sales periods. It can be seen where they were and where they are now. Then, a better idea of where sales may go next can be seen.

Resources

Argosy University. (2015). Solutions-Focused Decision Making. Module 4 Lectures. Retrieved from:...