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Full data setThe following data represent the heights and weights of variousbaseball playersHeight, x (inches)PlayerNateDerekMarkWeight, y (pounds)PlayerGregRandyJoshPatHeight, x (inches)Weight, y (pounds)73207701837273701982227719774208Jon19371202(a) Compute the least-squares regression line.y 4.5x-125(Do not round until the final answer. Then round to three decimal places as needed.)Compute the correlation coefficient.r0.9050(Do not round until the final answer. Then round to four decimal places as needed.)(b) Remove the value corresponding to Randy and recompute the least-squares regression line.-(Do not round until the final answer. Then round to three decimal places as needed.)Remove the value corresponding to Randy and recompute the correlation coefficient.r=(Do not round until the final answer. Then round to four decimal places as needed.)(c) Do you think that Randy is an influential observation?YesO No

Question
Full data set
The following data represent the heights and weights of various
baseball players
Height, x (inches)
Player
Nate
Derek
Mark
Weight, y (pounds)
Player
Greg
Randy
Josh
Pat
Height, x (inches)
Weight, y (pounds)
73
207
70
183
72
73
70
198
222
77
197
74
208
Jon
193
71
202
(a) Compute the least-squares regression line.
y 4.5x-
125
(Do not round until the final answer. Then round to three decimal places as needed.)
Compute the correlation coefficient.
r0.9050
(Do not round until the final answer. Then round to four decimal places as needed.)
(b) Remove the value corresponding to Randy and recompute the least-squares regression line.
-
(Do not round until the final answer. Then round to three decimal places as needed.)
Remove the value corresponding to Randy and recompute the correlation coefficient.
r=
(Do not round until the final answer. Then round to four decimal places as needed.)
(c) Do you think that Randy is an influential observation?
Yes
O No
help_outline

Image Transcriptionclose

Full data set The following data represent the heights and weights of various baseball players Height, x (inches) Player Nate Derek Mark Weight, y (pounds) Player Greg Randy Josh Pat Height, x (inches) Weight, y (pounds) 73 207 70 183 72 73 70 198 222 77 197 74 208 Jon 193 71 202 (a) Compute the least-squares regression line. y 4.5x- 125 (Do not round until the final answer. Then round to three decimal places as needed.) Compute the correlation coefficient. r0.9050 (Do not round until the final answer. Then round to four decimal places as needed.) (b) Remove the value corresponding to Randy and recompute the least-squares regression line. - (Do not round until the final answer. Then round to three decimal places as needed.) Remove the value corresponding to Randy and recompute the correlation coefficient. r= (Do not round until the final answer. Then round to four decimal places as needed.) (c) Do you think that Randy is an influential observation? Yes O No

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check_circleAnswer
Step 1

Populate excel cells as shown below.

B
D
1 height
weight
2
73
207
3
72
198
4
73
197
5
70
193
6
70
183
77
7
222
74
208
71
202
10
11
help_outline

Image Transcriptionclose

B D 1 height weight 2 73 207 3 72 198 4 73 197 5 70 193 6 70 183 77 7 222 74 208 71 202 10 11

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Step 2

Go to Data >Data Analysis> Regression

Fill in input output range.

Answer

y= 4.447x -121.659

 

Data Analysis
?
X
nalysis Tools
OK
Regression Statistics
Multiple R 0.909458
R Square 0.827114
Adjusted R 0.792537
Covariance
Descriptive Statistics
Exponential Smoothing
F-Test Two-Sample for Variances
Fourier Analysis
Histogram
Moving Average
Randon Number Generation
Rank and Percentile
Regression
Cancel
Help
Standard E 5.583797
Observatic
7
Regression
Input
OK
Input y Range
SC52SCS
ANOVA
Cancel
Input X Range
SB525859
df
Regression
MS
SS
gnificance F
Help
Labels
Constant is Zere
1 745.8203 745.8203 23.92076 0.00451
Contidence Level
%
95
Residual
5 155.8939 31.17879
Output options
Total
6 901.7143
ODutput Range
sos9
u
O New Worksheet Py
ad
O New Workbook
Residuals
Coefficientsandard Err
P-value
Lower 95%Upper 95%ower 95.09pper 95.0%
t Stat
Besiduals
Standardized Residuals
| Resigual Plots
une Fit Plots
Intercept -121.659 65.88848 -1.84644 0.124116 -291.031 47.71264 -291.031 47.71264
Normal Probability
Hormal Probabiity Plots
73
4.44697 0.909236 4.890886 0.00451 2.109704 6.784235 2.109704 6.784235
help_outline

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Data Analysis ? X nalysis Tools OK Regression Statistics Multiple R 0.909458 R Square 0.827114 Adjusted R 0.792537 Covariance Descriptive Statistics Exponential Smoothing F-Test Two-Sample for Variances Fourier Analysis Histogram Moving Average Randon Number Generation Rank and Percentile Regression Cancel Help Standard E 5.583797 Observatic 7 Regression Input OK Input y Range SC52SCS ANOVA Cancel Input X Range SB525859 df Regression MS SS gnificance F Help Labels Constant is Zere 1 745.8203 745.8203 23.92076 0.00451 Contidence Level % 95 Residual 5 155.8939 31.17879 Output options Total 6 901.7143 ODutput Range sos9 u O New Worksheet Py ad O New Workbook Residuals Coefficientsandard Err P-value Lower 95%Upper 95%ower 95.09pper 95.0% t Stat Besiduals Standardized Residuals | Resigual Plots une Fit Plots Intercept -121.659 65.88848 -1.84644 0.124116 -291.031 47.71264 -291.031 47.71264 Normal Probability Hormal Probabiity Plots 73 4.44697 0.909236 4.890886 0.00451 2.109704 6.784235 2.109704 6.784235

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Step 3

Correlation coefficient can be obtained using C...

A
B
C
1 Player
height
73
weight
207
198
2 Nate
3 Derek
72
4 Mark
73
197
5 Jon
70
193
6 Greg
7 Randy
70
183
77
222
8 Josh
74
208
9 Pat
71
202
10
CORREL(B2:B9,C2:C9)|
11
12
13
14
A
1 Player
2 Nate
3 Derek
4 Mark
height
weight
73
207
72
198
73
197
5 Jon
70
193
6 Greg
7 Randy
8 Josh
70
183
77
222
74
208
9 Pat
71
202
10
0.9050
11
12
13
help_outline

Image Transcriptionclose

A B C 1 Player height 73 weight 207 198 2 Nate 3 Derek 72 4 Mark 73 197 5 Jon 70 193 6 Greg 7 Randy 70 183 77 222 8 Josh 74 208 9 Pat 71 202 10 CORREL(B2:B9,C2:C9)| 11 12 13 14 A 1 Player 2 Nate 3 Derek 4 Mark height weight 73 207 72 198 73 197 5 Jon 70 193 6 Greg 7 Randy 8 Josh 70 183 77 222 74 208 9 Pat 71 202 10 0.9050 11 12 13

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