A researcher is examining the relationship between stress levels and performance on a test of cognitive performance. She hypothesizes that stress levels lead to an increase in performance to a point, and then increased stress decreases performance. She tests 10 participants who have the following levels of stress: 10.94 12.76 7.62 8.17 7.83 12.22 9.23 11.17 11.88 8.18 When she tests their levels of mental performance, she finds the following cognitive performance scores (listed in the same participant order as above):  5.24  4.64  4.68 5.04 4.17 6.20  4.54  6.55 5.79  3.17 Perform a linear regression to examine the relationship between these variables. What do the results mean?

Phlebotomy Essentials
6th Edition
ISBN:9781451194524
Author:Ruth McCall, Cathee M. Tankersley MT(ASCP)
Publisher:Ruth McCall, Cathee M. Tankersley MT(ASCP)
Chapter1: Phlebotomy: Past And Present And The Healthcare Setting
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A researcher is examining the relationship between stress levels and performance on a test of cognitive performance. She hypothesizes that stress levels lead to an increase in performance to a point, and then increased stress decreases performance. She tests 10 participants who have the following levels of stress:

10.94

12.76

7.62

8.17

7.83

12.22

9.23

11.17

11.88

8.18

When she tests their levels of mental performance, she finds the following cognitive performance scores (listed in the same participant order as above):

 5.24

 4.64

 4.68

5.04

4.17

6.20

 4.54

 6.55

5.79

 3.17

Perform a linear regression to examine the relationship between these variables. What do the results mean?

Linear Regression
Model Summary - V5.24
Model
R
R
Adjusted R
RMSE
Но
0.000
0.000
0.000
1.057
0.629
0.395
0.309
0.879
ANOVA
Model
Sum of Squares
df
Mean Square
p
Regression
3.535
1
3.535
4.575
0.070
Residual
5.409
7
0.773
Total
8.944
8
Note. The intercept model is omitted, as no meaningful information can be shown.
Coefficients
Model
Unstandardized
Standard Error
Standardized
t
Но
(Intercept)
4.976
0.352
14.117
<.001
H1
(Intercept)
1.830
1.500
1.220
0.262
V10.94
0.318
0.149
0.629
2.139
0.070
Transcribed Image Text:Linear Regression Model Summary - V5.24 Model R R Adjusted R RMSE Но 0.000 0.000 0.000 1.057 0.629 0.395 0.309 0.879 ANOVA Model Sum of Squares df Mean Square p Regression 3.535 1 3.535 4.575 0.070 Residual 5.409 7 0.773 Total 8.944 8 Note. The intercept model is omitted, as no meaningful information can be shown. Coefficients Model Unstandardized Standard Error Standardized t Но (Intercept) 4.976 0.352 14.117 <.001 H1 (Intercept) 1.830 1.500 1.220 0.262 V10.94 0.318 0.149 0.629 2.139 0.070
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