Do men and women have similar college majors? Researchers ask a group of college students about their majors. They also group the students by their gender. They want to determine if the distribution of college majors is the same for men and women. A chi-square Homogeneity Test at the 1% significance level is performed. (a) The null and alternative hypotheses are:H0: The distribution of the two populations are the same.Ha: The distribution of the two populations are not the same.(b) χ20=16.0(c) χ20.01=9.210 (d) What conclusions can be made? Select all that apply. Select all that apply: We should reject H0. We should not reject H0. At the 1% significance level, there is sufficient evidence to conclude that the distributions of the two populations are not the same. At the 1% significance level, there is not sufficient evidence to conclude that the distributions of the two populations are not the same.

From the provided information, The chi square test statistic is 16.0 and the chi square critical…

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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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Do men and women have similar college majors? Researchers ask a group of college students about their majors. They also group the students by their gender. They want to determine if the distribution of college majors is the same for men and women. A chi-square Homogeneity Test at the 1% significance level is performed.

(a) The null and alternative hypotheses are:

H0: The distribution of the two populations are the same.
Ha: The distribution of the two populations are not the same.

(b) χ20=16.0

(c) χ20.01=9.210

(d) What conclusions can be made? Select all that apply.

Select all that apply:

We should reject H0.
We should not reject H0.
At the 1% significance level, there is sufficient evidence to conclude that the distributions of the two populations are not the same.
At the 1% significance level, there is not sufficient evidence to conclude that the distributions of the two populations are not the same.

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