Question

Find a real economic or business example to support why a linear regression model is limited in its power to project into the future.

The example must support your position and include at least one graph using Microsoft Word’s chart feature.

The graph should include:

a. Labels on both axes

b. A title

c. A legend

d. A caption listing the source of the data

Answer 1

● We will use Credit Approval Data to validate our point.The data is available from the UCI Machine Learning Repository.

● This analysis demonstrates the analytic technique to examine one of the flaw in regression technique to project into future, we discussed in point 1q. This is a data on one company’s decision to approve or deny credit card applications.

Through the analysis we will take four effect to justify our point. The four factors all positively affect the analysis outcome.

The main influencing factor here is:

Debt,

Years employed,

Credit score, and

Income level.

Other variables such as age, sex, or ethnicity did not have an influence on whether the application was denied.

From the boxplots, we can see the distribution is different between the variables. Income has the least amount of variance because the boxes are tightly grouped about the mean. By examining the histograms we can see that the data is skewed to the right meaning the median is less than the mean. (USE the histogram and its description mainly for final answer).