Question

Detail one instance in which regression analysis can be used in a business application. Explain what insights can be gained, limitations that must be considered, and outline one case example used in real life.

Answer 1

1.)Predictive analytics i.e. forecasting future opportunities and risks is the most prominent application of regression analysis in business. Demand analysis, for instance, predicts the number of items which a consumer will probably purchase

2.) Operation Efficiency: Regression models can also be used to optimize business processes. A factory manager, for example, can create a statistical model to understand the impact of oven temperature on the shelf life of the cookies baked in those ovens.

3.) Supporting Decisions: Businesses today are overloaded with data on finances, operations and customer purchases. Increasingly, executives are now leaning on data analytics to make informed business decisions thus eliminating the intuition and gut feel.

Case Study Example:

A nonprofit home healthcare agency has asked “a consultant” whether its CEO is fairly paid relative to the marketplace for similar agencies. The Agency has supplied a database to the consultant, who also has his own survey database of CEO pay.

This case will demonstrate how regression data can be used to answer this question. David Wudyka, SPHR, MBA, BSIE, is the Managing Principal and founder of Westminster Associates in Wrentham, Massachusetts. His tips came during a recent webinar sponsored by BLR

In this case, the CEO’s rate is $79.50 per hour (or $165,360 per year). The Agency’s revenue is $25M. Is the CEO fairly paid? The survey of nonprofit home health- care agency pay levels shows the pay levels in the table below.

This case will demonstrate how regression data can be used to answer this question.

The independent variable is “Revenue”; the dependent variable is “CEO base pay.”

Now statistical models are built on the data provided,

here it is

Perform . Regression Analysis on this dataset,

Our results after regression analysis are:

1. r² = .9075 = .9 (Good! It means that 90% of the variation in the dependent variable of CEO Base Pay can be “explained” by the independent
2. The “y-intercept” value (“b” in straight-line equation) = 39.378
3. The “coefficient” value is 1.47417E-06. This translates to .00000147417 from scientific notation.

Sample Output Calculation

At $25M Revenue (your company’s revenue), the calculation is as follows:

Constant $39.38 + .00000147417 x 25,000,000
$39.38 + $36.85 = $76.23 (annualized = $158,558)

Is the CEO appropriately paid? Her rate is $79.50. Therefore, it appears that her base pay rate is “in the market.” What’s “in the market”? Use a tolerance range of +/- 5 percent to 10 percent.

What if her performance is lagging? You probably would not make an adjustment; however, we don’t know about the performance levels of all the others in the data sample.

This is a buisness case example where regression analysis is hevaily used