A researcher is studying the effect of ten different variables on a critical measure of business performance. In selecting the best set of independent variables to predict the dependent variable, a forward selection method is used. How are variables selected for inclusion in the model?

A. Smallest p-value

B. Highest increase in the multiple r-squared

C. smallest coefficient

D. Largest p-value

D. Largest p-value

Step-by-step explanation:

P-value assists statistician to know the importance of their result. It assists them in determining the strength of their evidence.

A large P-value which is less than 0.05 depicts that an evidence is week against null hypothesis, therefore the null hypothesis must be accepted.

A small P-value <0.05 depicts a strong evidence against null hypothesis, so the null hypothesis must be rejected.

B. Highest increase in the multiple r-squared

Step-by-step explanation:

Forward selection is a type of stepwise regression which begins with an empty model and adds in variables one by one. In each forward step, you add the one variable that gives the single best improvement to your model.

We know that when more variables are added, r-squared values typically increase with probability 1. Based on this and the above definition, we select the candidate variable that increases r-Squared the most and stop adding variables when none of the remaining variables are significant.

x=half

step-by-step explanation:

y=x+9

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step-by-step explanation: