Consider the StatKey output below that provides a bootstrap distribution for a difference in proportions: Bootstrap Dotplot of p - p2 Original Sample 125 Left TallTo Tal Kight Tall Group Count Sample Size samples- 4000 Proportion mean-4118 Group 1 21 86 0.244 std. error-0.06s 100 Group 2 36 99 0.364 Group 1- Group 2 -15 n/a -0.119 75 Bootstrap Sample 50 Group Count Sample Proportion Size 25 Group 1 30 86 0.349 Group 2 37 99 0.374 Group 1- Group 2 -7 n/a -0.025 -0.30 -0.25 -0.20 -0.15 L0.10 -0.05 0.00 0.05 0.10 -0.118 Using all the information provided, we should use a N(µ= [Select] ( Select ) ) distribution to approximate the bootstrap distribution for this situation.

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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Consider the StatKey output below that provides a bootstrap distribution for a difference in
proportions:
Bootstrap Dotplot of ê1- ê2
Original Sample
125
OLeft TalO Two TallKight Tall
Sample
Proportion
Size
samples - 4000
Group Count
mean --0.118
Group 1
21
86
0.244
std. error-0.068
100
Group 2
36
99
0.364
Group 1-
Group 2
-15
n/a
-0.119
75
Bootstrap Sample
50
Sample
Proportion
Size
Group Count
25
Group 1
30
86
0.349
Group 2
37
99
0.374
Group 1-
Group 2
-7
n/a
-0.025
-0.30
-0.25
-0.20
-0.15
L0.10
-0.05
0.00
0.05
0.10
-0.118
Using all the information provided, we should use a N(µ= [Select ]
[ Select ]
) distribution to approximate the bootstrap distribution for this
situation.
Transcribed Image Text:Consider the StatKey output below that provides a bootstrap distribution for a difference in proportions: Bootstrap Dotplot of ê1- ê2 Original Sample 125 OLeft TalO Two TallKight Tall Sample Proportion Size samples - 4000 Group Count mean --0.118 Group 1 21 86 0.244 std. error-0.068 100 Group 2 36 99 0.364 Group 1- Group 2 -15 n/a -0.119 75 Bootstrap Sample 50 Sample Proportion Size Group Count 25 Group 1 30 86 0.349 Group 2 37 99 0.374 Group 1- Group 2 -7 n/a -0.025 -0.30 -0.25 -0.20 -0.15 L0.10 -0.05 0.00 0.05 0.10 -0.118 Using all the information provided, we should use a N(µ= [Select ] [ Select ] ) distribution to approximate the bootstrap distribution for this situation.
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