Step 2The x test statistic for a goodness of fit follows where f; is the observed frequency for category i, e;expected frequency for category i, and k is the number of categories. The appropriate x has kis the1 degrees offreedom.k= (f, - e)?i = 1The observed and expected frequencies are summarized in the table below.ExpectedFrequencySquaredDifferenceSquared DifferenceDivided by ExpectedObservedDifferenceCategoryFrequency(fj – e;)²eijfij - eij(fj – @j)²fjeijFrequency(-60)23,6003,600A20802080 = -60%D80В1608080204040010The sum of the final column will be the x test statistic. Thus, the test statistic is x=There are k= 3 categories, so the degrees of freedom is k – 1 =Cubri4Cli (a) Use the p-value approach.A goodness of fit test will be conducted for the following hypotheses. This type of test is always an upper tailtest. The p-value approach will be used, meaning that the given level of significance, a =0.01, will becompared to the area under the curve to the right of the calculated test statistic.0.40, PB = 0.40, and= 0.20Ho:PAH: The population proportions are not pa = 0.40, pB = 0.40, and pcPc= 0.20.The goal of the hypothesis test is to determine if the observed proportions are significantly different from thegiven proportions. A sample size of 200 yielded 20 in category A, 160 in category B, and 20 in category C.Assuming the hypothesized proportions are true, then the expected frequencies in each category will be theproduct of the hypothesized proportions and the sample size.expected frequency = category proportion(sample size)The proportion for category A is assumed to be 0.40. Use the sample size of 200 to find the expectedfrequency for this category, ea•expected frequencycategory proportion(sample size)%DCA =0.400.40 (200)8080The proportion for category B is assumed to be 0.40. Use the sample size of 200 to find the expectedfrequency for this category, eg.expected frequencycategory proportion(sample size)eB =0.400.40 (200)8080The proportion for category C is assumed to be 0.20. Use the sample size of 200 to find the expectedfrequency for this category, ec:expected frequencycategory proportion(sample size)%Dec = (0.200.20)(200)4040

χ2 test is popularly known as the test of goodness of fit for the reason that it enables us to…

Expected Frequency Squared Difference Squared Difference Divided by Expected Observed Difference Category Frequency fij - eij (fj – @;)² ej Frequency (-60)2 : 3,600 3,600 A 20 80 20 – 80 = -60 80 В 160 80 80 C 20 40 400 10 The sum of the final column will be the x test statistic. Thus, the test statistic is x %3D There are k = 3 categories, so the degrees of freedom is k – 1 =

Expected Frequency Squared Difference Squared Difference Divided by Expected Observed Difference Category Frequency fij - eij (fj – @;)² ej Frequency (-60)2 : 3,600 3,600 A 20 80 20 – 80 = -60 80 В 160 80 80 C 20 40 400 10 The sum of the final column will be the x test statistic. Thus, the test statistic is x %3D There are k = 3 categories, so the degrees of freedom is k – 1 =

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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Right arm 102 101 94 79 79 Left arm 175 169 182 146 144 If the sample data were to result in the scatterplot shown here, what is the value of the linear correlation coefficient r?
Can you help me with d?
Correlation
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