Earlier this semester, we learned to use 2-Sample-TTest to compare the population means of two independent populations. One-way ANOVA is more powerful because it could compare the population means of three or more independent populations. However, use of one-way ANOVA also requires the assumption that the populations have the same variance. In this exercise, we will compare and contrast One-way ANOVA and 2-Sample-TTest. With the 2-Sample- TTEST, we will first choose 'No' for the pooled variances option, then re-run the test while choosing 'Yes' for the pooled variances option. Use the following data to complete these tasks. Depending on if you choose to do this by hand or using code, use the data format that suits you best LONG form on top or WIDE form on bottom. Treatment Response One 5.6 One 4.7 One 3.6 3.9 5.2 4.3 4.1 4.8 3.4 2.3 4.4 One One One One One Two Two Two Two Two Two Two Two 5.6 4.7 Treatment Treatment One Two 3.4 2.3 4.4 3.5 3.7 5.8 2.8 4.7 3.6 3.9 5.2 4.3 4.1 4.8 3.5 3.7 5.8 2.8 4.7 1. Use One-way ANOVA to test whether the population means are equal (H₁: M₁ M₂) Test Statistics = (Please show your answer to 3 decimal places.) p-value= p-value= (Please show your answer to 4 decimal places) 2. Use 2-Sample-TTest without 'pooled' variances to test whether the population means are equal ( H₁:₁₂) Test Statistics = (Please show your answer to 3 decimal places.) (Please show your answer to 4 decimal places.) 3. Use 2-Sample TTest with 'pooled' variances to test whether the population means are equal ( H₁: M₁ M₂) Test Statistics = p-value= (Please show your answer to 3 decimal places.) (Please show your answer to 4 decimal places.)
Earlier this semester, we learned to use 2-Sample-TTest to compare the population means of two independent populations. One-way ANOVA is more powerful because it could compare the population means of three or more independent populations. However, use of one-way ANOVA also requires the assumption that the populations have the same variance. In this exercise, we will compare and contrast One-way ANOVA and 2-Sample-TTest. With the 2-Sample- TTEST, we will first choose 'No' for the pooled variances option, then re-run the test while choosing 'Yes' for the pooled variances option. Use the following data to complete these tasks. Depending on if you choose to do this by hand or using code, use the data format that suits you best LONG form on top or WIDE form on bottom. Treatment Response One 5.6 One 4.7 One 3.6 3.9 5.2 4.3 4.1 4.8 3.4 2.3 4.4 One One One One One Two Two Two Two Two Two Two Two 5.6 4.7 Treatment Treatment One Two 3.4 2.3 4.4 3.5 3.7 5.8 2.8 4.7 3.6 3.9 5.2 4.3 4.1 4.8 3.5 3.7 5.8 2.8 4.7 1. Use One-way ANOVA to test whether the population means are equal (H₁: M₁ M₂) Test Statistics = (Please show your answer to 3 decimal places.) p-value= p-value= (Please show your answer to 4 decimal places) 2. Use 2-Sample-TTest without 'pooled' variances to test whether the population means are equal ( H₁:₁₂) Test Statistics = (Please show your answer to 3 decimal places.) (Please show your answer to 4 decimal places.) 3. Use 2-Sample TTest with 'pooled' variances to test whether the population means are equal ( H₁: M₁ M₂) Test Statistics = p-value= (Please show your answer to 3 decimal places.) (Please show your answer to 4 decimal places.)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:Earlier this semester, we learned to use 2-Sample-TTest to compare the population means of two
independent populations. One-way ANOVA is more powerful because it could compare the population means
of three or more independent populations. However, use of one-way ANOVA also requires the assumption
that the populations have the same variance.
In this exercise, we will compare and contrast One-way ANOVA and 2-Sample-TTest. With the 2-Sample-
TTEST, we will first choose 'No' for the pooled variances option, then re-run the test while choosing 'Yes' for
the pooled variances option.
Use the following data to complete these tasks. Depending on if you choose to do this by hand or using
code, use the data format that suits you best - LONG form on top or WIDE form on bottom.
Treatment
One
One
One
One
One
One
One
One
Two
Two
Two
Two
Two
Two
Two
Two
Treatment Treatment
One
Two
5.6
4.7
3.6
3.9
5.2
4.3
4.1
4.8
Response
5.6
4.7
3.6
3.9
5.2
4.3
4.1
4.8
3.4
2.3
4.4
3.5
3.7
5.8
2.8
4.7
p-value=
3.4
2.3
4.4
1. Use One-way ANOVA to test whether the population means are equal (H₁: M₁ M₂)
Test Statistics =
(Please show your answer to 3 decimal places.)
p-value =
3.5
3.7
5.8
2.8
4.7
p-value=
2. Use 2-Sample-TTest without 'pooled' variances to test whether the population means are equal (
H₁:1₁ #1₂)
Test Statistics =
(Please show your answer to 4 decimal places)
(Please show your answer to 3 decimal places.)
(Please show your answer to 4 decimal places.)
3. Use 2-Sample TTest with 'pooled' variances to test whether the population means are equal (
H₁: M₁ M₂)
Test Statistics =
(Please show your answer to 3 decimal places.)
(Please show your answer to 4 decimal places.)
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