Refer to exhibit.
You are asked to write a report on how specific variables impact your client’s sales using a data
set provided to you by the client. The data includes 15 variables that the client views as directly
related to sales, and you are restricted to these variables only.
After a preliminary analysis of the data, the following findings were made:
1. Multicollinearity is not an issue among the variables
2. Only three variables—A, B, and C—have significant correlation with sales
You build a linear regression model on the dependent variable of sales with the independent
variables of A, B, and C. The results of the regression are seen in the exhibit.
You cannot request additional datA. what is a way that you could try to increase the R2 of the
model without artificially inflating it?
A.
Create clusters based on the data and use them as model inputs
B.
Force all 15 variables into the model as independent variables
C.
Create interaction variables based only on variables A,B,and C
D.
Break variables A,B,and C into their own univariate models
Explanation:
Sites of interest we’ve a link to.
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where is the sites?
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who can help to explain it?
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Are they referring to N Fold cross validation.
If there is not enough amount of data, they will divide the data into clusters of 2,3..N
Each time one of the of clusters is used as test data and the rest as training data. Then the model is created based on training data and fitted against the test data.
After each test, the results are compared to see how well the model is performing.
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