A study by Ruback & Juieng (1997) included information on the gender of drivers and the number of passengers each driver had in the car. Using this dataset, a t-test was conducted to compare the number of passengers across male and female drivers and the results (from SAS) are shown below. (a) Compute and interpret the effect size for the difference (between genders) in the number of nogg on
A study by Ruback & Juieng (1997) included information on the gender of drivers and the number of passengers each driver had in the car. Using this dataset, a t-test was conducted to compare the number of passengers across male and female drivers and the results (from SAS) are shown below. (a) Compute and interpret the effect size for the difference (between genders) in the number of nogg on
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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
Transcribed Image Text:A study by Ruback & Juieng (1997) included information on the gender of drivers and the number of
passengers each driver had in the car. Using this dataset, a t-test was conducted to compare the number
of passengers across male and female drivers and the results (from SAS) are shown below.
(a) Compute and interpret the effect size for the difference (between genders) in the number of
passengers.
(b) Fully interpret the results of the t-test, assuming homogeneity of variance.

Transcribed Image Text:The TTEST Procedure
Variable:
passgrs
gender
N
Mean
Std Dev
Std Err
Minimum
Маximum
female
103
0.6796
0.8309
0.0819
3.0000
male
97
1.1031
1.0654
0.1082
7.0000
Diff (1-2)
-0.4235
0.9519
0.1347
gender
Method
Mean
95% CL Mean
Std Dev
95% CL Std Dev
female
0.6796
0.5172
0.8420
0.8309
0.7309
0.9630
male
1.1031
0.8884
1.3178
1.0654
0.9337
1.2408
Diff (1-2)
Diff (1-2)
Рooled
-0.4235
-0.6891
-0.1579
0.9519
0.8666
1.0558
Satterthwaite
-0.4235
-0.6912
-0.1558
Method
Variances
DF
t Value
Pr > |t|
Pooled
Equal
198
-3.14
0.0019
Satterthwaite
Unequal
181.45
-3.12
0.0021
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