You wish to test the following claim (HaHa) at a significance level of α=0.10α=0.10. For the context of this problem, μd=PostTest−PreTestμd=PostTest-PreTest where the first data set represents a pre-test and the second data set represents a post-test. (Each row represents the pre and post test scores for an individual. Be careful when you enter your data and specify what your μ1μ1 and μ2μ2 are so that the differences are computed correctly.) Ho:μd=0Ho:μd=0 Ha:μd≠0Ha:μd≠0 You believe the population of difference scores is normally distributed, but you do not know the standard deviation. You obtain the following sample of data: pre-test post-test 39 56 24.3 14.2 56.8 121.8 46.5 62.3 40.8 128 28.2 63.3 51.7 134.4 35.8 -30 48.6 26.7 63.7 109.7 30.1 119.7 49.8 11.3 23.3 124.8 50.2 58.2 53.4 74.1 48.6 69.3 56.3 66.9 35.8 57.7 41.9 83 What is the test statistic for this sample? test statistic = (Report answer accurate to 4 decimal places.) What is the p-value for this sample? p-value = (Report answer accurate to 4 decimal places.) The p-value is... less than (or equal to) αα greater than αα This test statistic leads to a decision to... reject the null accept the null fail to reject the null As such, the final conclusion is that... There is sufficient evidence to warrant rejection of the claim that the mean difference of post-test from pre-test is not equal to 0. There is not sufficient evidence to warrant rejection of the claim that the mean difference of post-test from pre-test is not equal to 0. The sample data support the claim that the mean difference of post-test from pre-test is not equal to 0. There is not sufficient sample evidence to support the claim that the mean difference of post-test from pre-test is not equal to 0.

Name: You wish to test the following claim (HaHa) at a significance level of
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Description: You wish to test the following claim (HaHa) at a significance level of α=0.10α=0.10. For the context of this problem, μd=PostTest−PreTestμd=PostTest-PreTest where the first data set represents a pre-test and the second data set represents a post-test

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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You wish to test the following claim (HaHa) at a significance level of α=0.10α=0.10. For the context of this problem, μd=PostTest−PreTestμd=PostTest-PreTest where the first data set represents a pre-test and the second data set represents a post-test. (Each row represents the pre and post test scores for an individual. Be careful when you enter your data and specify what your μ1μ1 and μ2μ2 are so that the differences are computed correctly.)

Ho:μd=0Ho:μd=0
Ha:μd≠0Ha:μd≠0

You believe the population of difference scores is normally distributed, but you do not know the standard deviation. You obtain the following sample of data:

pre-test	post-test
39	56
24.3	14.2
56.8	121.8
46.5	62.3
40.8	128
28.2	63.3
51.7	134.4
35.8	-30
48.6	26.7
63.7	109.7
30.1	119.7
49.8	11.3
23.3	124.8
50.2	58.2
53.4	74.1
48.6	69.3
56.3	66.9
35.8	57.7
41.9	83

What is the test statistic for this sample?
test statistic = (Report answer accurate to 4 decimal places.)

What is the p-value for this sample?
p-value = (Report answer accurate to 4 decimal places.)

The p-value is...

less than (or equal to) αα
greater than αα

This test statistic leads to a decision to...

reject the null
accept the null
fail to reject the null

As such, the final conclusion is that...

There is sufficient evidence to warrant rejection of the claim that the mean difference of post-test from pre-test is not equal to 0.
There is not sufficient evidence to warrant rejection of the claim that the mean difference of post-test from pre-test is not equal to 0.
The sample data support the claim that the mean difference of post-test from pre-test is not equal to 0.
There is not sufficient sample evidence to support the claim that the mean difference of post-test from pre-test is not equal to 0.

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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