Discipline of Information Technology - Mathematics and Statistics - Statistics Assignment Help

Assignment Task :

QUESTIONS

Suppose we have a dataset with four predictors: X1= Previous Success (on a scale of 1-100), X2= Experience (on a scale of 1-10), X3= Gender (male/female), X4= Interaction between Previous Success and Gender. The response is the salary (in thousands of dollars) of the person who will be hired for a CIO position in a high-tech company. Suppose we use least squares to fit the multiple linear regression model and we get the following estimates for β0, β1, β2, β3, β4: {16, 40, 0.02, 28, -10}.

1. Predict the salary of a male director with Previous Success=65 and Experience=8.

2. True or false: Even with a very small Previous Success, male CIOs will earn more than female CIOs. Justify your answer.

3.  True or false: since the coefficient for Experience is very small, there is very little evidence that Experience affects the salary. Justify your answer.

4.  If a multiple cubic regression was used, although the actual relationship is linear, would you expect the training residual sum of squares to be higher or lower than that of the multiple linear regression? Justify your response.

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