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Score:Week 2Testing means – T-tests Q3 In questions 2 and 3, be sure to include the null and alternate hypotheses you will…

Score:Week 2Testing means – T-tests Q3 In questions 2 and 3, be sure to include the null and alternate hypotheses you will…

Daniel Kevins

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Score:	Week 2	Testing means – T-tests								Q3
		In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.						Ho	Female	Male	Female
		In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.						45	34	1.017	1.096
								45	41	0.870	1.025
<1 point>	1	Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.						45	23	1.157	1.000
		(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value — see column S)						45	22	0.979	0.956
		Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?						45	23	1.134	1.000
		Males			Females			45	42	1.149	1.050
		Ho: Mean salary = 45			Ho: Mean salary = 45			45	24	1.052	1.043
		Ha: Mean salary =/= 45			Ha: Mean salary =/= 45			45	24	1.175	1.043
								45	69	1.043	1.210
		Note: While the results both below are actually from Excel’s t-Test: Two-Sample Assuming Unequal Variances,						45	36	1.134	1.161
		having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome – we are tricking Excel into doing a one sample test for us.						45	34	1.043	1.096
			Male	Ho		Female	Ho	45	57	1.000	1.187
		Mean	52	45	Mean	38	45	45	23	1.074	1.000
		Variance	316	0	Variance	334.6666667	0	45	50	1.020	1.041
		Observations	25	25	Observations	25	25	45	24	0.903	1.043
		Hypothesized Mean Difference	0		Hypothesized Mean Difference	0		45	75	1.122	1.119
		df	24		df	24		45	24	0.903	1.043
		t Stat	1.968903827		t Stat	-1.913206357		45	24	0.982	1.043
		P(T<=t) one-tail	0.03030785		P(T<=t) one-tail	0.033862118		45	23	1.086	1.000
		t Critical one-tail	1.71088208		t Critical one-tail	1.71088208		45	22	1.075	0.956
		P(T<=t) two-tail	0.060615701		P(T<=t) two-tail	0.067724237		45	35	1.052	1.129
		t Critical two-tail	2.063898562		t Critical two-tail	2.063898562		45	24	1.140	1.043
		Conclusion: Do not reject Ho; mean equals 45			Conclusion: Do not reject Ho; mean equals 45			45	77	1.087	1.149
		Is this a 1 or 2 tail test?			Is this a 1 or 2 tail test?
		– why?			– why?
		P-value is:			P-value is:			45	55	1.052	1.145
		Is P-value > 0.05?			Is P-value > 0.05?			45	65	1.157	1.140
		Why do we not reject Ho?			Why do we not reject Ho?
	Interpretation:




<1 point>	2	Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
		(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)

		Ho:
		Ha:
		Test to use:
		Place B43 in Outcome range box.















		P-value is:
		Is P-value < 0.05?
		Reject or do not reject Ho:
	If the null hypothesis was rejected, what is the effect size value:
		Meaning of effect size measure:

		Interpretation:

	b.	Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality? Why?


<1 point>	3	Based on our sample data set, can the male and female compas in the population be equal to each other? (Another 2-sample t-test.)

		Ho:
		Ha:
		Statistical test to use:
		Place B75 in Outcome range box.















		What is the p-value:
		Is P-value < 0.05?
		Reject or do not reject Ho:
	If the null hypothesis was rejected, what is the effect size value:
		Meaning of effect size measure:

		Interpretation:

<1 point>	4	Since performance is often a factor in pay levels, is the average Performance Rating the same for both genders?

		Ho:
		Ha:

		Test to use:

		Place B106 in Outcome range box.
















			What is the p-value:
			Is P-value < 0.05?
			Do we REJ or Not reject the null?
		If the null hypothesis was rejected, what is the effect size value:
			Meaning of effect size measure:

			Interpretation:



<2 points>	5	If the salary and compa mean tests in questions 2 and 3 provide different results about male and female salary equality,
		which would be more appropriate to use in answering the question about salary equity? Why?
		What are your conclusions about equal pay at this point?

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