Applied Regression Modeling

3rd edition

Iain Pardoe

A concise, comprehensive treatment of the application of statistical regression analysis suitable for undergraduate and graduate students. Covers simple linear regression, multiple linear regression, model building, and advanced regression topics.

by Iain P
on July 30, 2021

Answer the following questions based on Section 5.2.4: Regression pitfalls - excluding important predictors.

Consider the following data:

X1 0.8 0.6 0.8 0.2 0.5 1.0 0.3 0.1 0.2 0.5
X2 0.3 0.6 0.6 0.6 0.8 0.7 0.2 0.1 0.9 0.4
Y 0.5 0.6 0.5 0.5 0.7 0.7 0.3 0.2 0.7 0.4
Use the data to answer the following questions.

1. 

Use statistical software to fit a simple linear regression model with response variable, Y, and predictor variable, X1. Obtain the two-tailed p-value for the regression parameter for X1. Round your answer to 4 decimal places.

2. 

Interpret the p-value from Q1 assuming a significance level of 0.05.

3. 

Use statistical software to fit a multiple linear regression model with response variable, Y, and predictor variables, X1 and X2. Obtain the two-tailed p-value for the regression parameter for X1. Round your answer to 4 decimal places.

4. 

Interpret the p-value from Q3 assuming a significance level of 0.05.

5. 

Compare s and adjusted R2 for the multiple linear regression model with X1 and X2 (from Q3) in relation to the simple linear regression model with X1 only (from Q1).

6. 

True or false? Predictor X1 is insufficient by itself to account for the variation in Y and we must include X2 in the model to properly account for the variation in Y.

Categories:

No Category

Tags:

No Tag

Comments are closed