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Book Info
- About
- Preface
- Contents
- Buy
- Companion Sites
- Corrections
- Author
Learning Resources
Teaching Resources
- Presentation Slides
- Instructor’s Manual
Software Info
- Data Desk
- EViews
- JMP
- Minitab
- R
- SAS Code
- SAS Studio
- SPSS
- Stata
- Statistica
Download Data

© 2019

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.

Answer the following questions based on Section 2.5: Model interpretation.

Consider the following data:

X	1.1	1.4	1.8	2.5	3.2
Y	3.8	5.7	6.9	8.1	10.0

Use statistical software to fit a simple linear regression model with response variable, Y, and predictor variable, X. Use the fitted model to answer the following questions.

1.

What conclusion do we draw from the slope p-value of 0.004 at a 5% significance level?

There is a significant linear association between Y and X.

There is an insignificant linear association between Y and X.

There is a significant non-linear association between Y and X.

There is an insignificant non-linear association between Y and X.

None

2.

How do we interpret the estimated slope parameter, b₁-hat = 2.7?

We expect Y to be 2.7 when X = 0.

We expect Y to increase by 2.7 units for each 1-unit increase in X.

We expect X to be 2.7 when Y = 0.

We expect X to increase by 2.7 units for each 1-unit increase in Y.

None

3.

How do we interpret the regression standard error, s = 0.5654?

56.54% of the variation in Y can be accounted for by a linear association between Y and X.

56.54% of the variation in X can be accounted for by a linear association between Y and X.

We can expect to predict Y to an accuracy of approximately ± 0.57 (at a 95% confidence level).

We can expect to predict Y to an accuracy of approximately ± 1.13 (at a 95% confidence level).

None

4.

How do we interpret the coefficient of determination, R² = 0.9566?

95.66% of the variation in Y can be accounted for by a linear association between Y and X.

95.66% of the variation in X can be accounted for by a linear association between Y and X.

We can expect to predict Y to an accuracy of approximately ± 0.96 (at a 95% confidence level).

We can expect to predict Y to an accuracy of approximately ± 1.91 (at a 95% confidence level).

None

5.

Which of the following plots provides a useful graphical representation of the model?

A scatterplot with Y on the vertical axis and X on the horizontal axis, plotted points with a positive, linear trend, and a zig-zag line joining up the points.

A scatterplot with Y on the vertical axis and X on the horizontal axis, plotted points with no pattern or linear trend, and a horizontal line going through the point-cloud.

A scatterplot with Y on the vertical axis and X on the horizontal axis, plotted points with a positive, linear trend, and a straight line going through the point-cloud.

A scatterplot with X on the vertical axis and Y on the horizontal axis, plotted points with a positive, linear trend, and a straight line going through the point-cloud.

None

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