Top Posters
Since Sunday
5
k
4
c
4
M
3
t
3
i
3
B
3
k
3
m
3
c
3
o
3
l
3
New Topic  
salonijainnn salonijainnn
wrote...
Posts: 145
Rep: 0 0
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.


Use the ANOVA table to compute and interpret R2.

▸ 0.298
About 30% of the variability in foot lengths in this sample is explained by the person's forearm length.

▸ 0.206
About 21% of the variability in foot lengths in this sample is explained by the person's forearm length.

▸ 0.546
About 55% of the variability in foot lengths in this sample is explained by the person's forearm length.

▸ 0.454
About 45% of the variability in foot lengths in this sample is explained by the person's forearm length.
Textbook 
Statistics: Unlocking the Power of Data

Statistics: Unlocking the Power of Data


Edition: 3rd
Authors:
Read 14 times
1 Reply
Replies
Answer verified by a subject expert
durandaldurandal
wrote...
Posts: 140
Rep: 0 0
3 weeks ago
Sign in or Sign up in seconds to unlock everything for free
More solutions for this book are available here
1

Related Topics

salonijainnn Author
wrote...

3 weeks ago
Thank you, thank you, thank you!
wrote...

Yesterday
This helped my grade so much Perfect
wrote...

2 hours ago
This calls for a celebration Person Raising Both Hands in Celebration
New Topic      
Explore
Post your homework questions and get free online help from our incredible volunteers
  960 People Browsing
Related Images
  
 821
  
 442
  
 5605
Your Opinion

Previous poll results: How often do you eat-out per week?