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britt138 britt138
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3 weeks ago
Use the following to answer the questions below:

Students in a small statistics course wanted to investigate if forearm length (in cm) was useful for predicting foot length (in cm). The data they collected are displayed in the provided scatterplot (with regression), and the computer output from the analysis is provided.

Use three decimal places when reporting the results from any calculations, unless otherwise specified.

The regression equation is Foot (cm) = 9.22 + 0.574 Forearm (cm)
 
PredictorCoefSE CoefTP
Constant9.2164.5212.04 0.066
Forearm (cm)0.57350.15783.630.004
SourceDFSSMSFP
Regression144.31544.31513.200.004
Residual Error1136.9163.356
Total1281.231
Predicted Values for New Observations

Forearm (cm)FitSE Fit95% CI95% PI
28   25.2740.513(24.144, 26.403)(21.086, 29.461)

A scatterplot with a regression line shows the relationship between forearm and foot. The horizontal axis is labeled, Forearm (centimeters) and has markings from 22 to 36 in increments of 2.  The vertical axis is labeled, Foot (centimeters) and has markings from 22 to 31 in increments of 1. A regression line starts from (23, 22.3), increases toward the right, and ends at (36, 30). The dots are randomly scattered throughout the graph, such that a few dots lie above the regression line, a few dots lie below the regression line, and a few dots lie on the regression line. The concentration of the dots is more between the points 26 and 30 on the horizontal axis and between the points, 23 and 25 on the vertical axis. The dots are plotted as follows: (23, 23), (24, 23), (26, 25), (27, 24), (28, 23), (29, 26), (29, 28), (29.5, 26), (29.5, 27), (30, 23), (32, 31), and (36, 29). All values are approximate.


The correlation between foot length and forearm length is 0.7389. Compute and interpret R2 for this regression model.
Textbook 
Statistics: Unlocking the Power of Data

Statistics: Unlocking the Power of Data


Edition: 3rd
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lulllull
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