{"id":127762,"date":"2018-02-20T23:50:30","date_gmt":"2018-02-20T23:50:30","guid":{"rendered":"https:\/\/writemyessayfree.com\/statistic-for-managers-5"},"modified":"2017-08-16T06:22:34","modified_gmt":"2017-08-16T06:22:34","slug":"statistic-for-managers-5","status":"publish","type":"post","link":"https:\/\/www.benedictsol.com\/blogs\/statistic-for-managers-5\/","title":{"rendered":"Statistic for Managers"},"content":{"rendered":"
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The outline is set on excel spread sheet, just need assistance with imputing the functions and data on the master sheet, to be able to answer the data analysis: Do Males make more the Females. Can negotiate a fee if interested. <\/p>\n

1 The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Since the assignment problems will
focus mostly on the compa-ratios, we need to find the mean, standard deviations, and range for our groups: Males, Females, and Overall. <\/p>\n

Males Females Overall
Means Means Means
standard deviations standard deviations standard deviations
Range Range Range <\/p>\n

Data values used: Discriptive Statistic; to show how results are formed. For Examples cell should have an \u201c=XX\u201d formula in them showing the value of the Discriptive statistic table.
if you choose to use generate each value using fx functions,then each function should be inside the cell, along with location of data value in the cell. <\/p>\n

If you use Descriptive statistics, Place the output table in row 1 of a column to the right. <\/p>\n

2. Probability
A key issue in comparing data sets is to see if they are distributed\/shaped the same. At this point we can do this
by looking at the probabilities that males and females are distributed in the same way for a grade levels. <\/p>\n

Empirical Probability: What is the probability for a: <\/p>\n

a. Randomly selected person being in grade E or above?
b. Randomly selected person being a male in grade E or above?
c. Randomly selected male being in grade E or above?
d. Why are the results different? <\/p>\n

3. Normal Curve based probability: For each group (overall, females, males), what are the values for each question below?
A. Make sure your answer cells show the Excel function and cell location of the data used.
Overall <\/p>\n

Female <\/p>\n

Male <\/p>\n

B. How do you interpret the relationship between the data sets? What does this suggest about our equal pay for equal work question? <\/p>\n

4. Based on our sample data set, can the male and female compa-ratios in the population be equal to each other? <\/p>\n

A. First, we need to determine if these two groups have equal variances, in order to decide which t-test to use. <\/p>\n

What is the data input ranged used for this question: decide equal variance? <\/p>\n

Step 1: Ho:
Ha:
Step 2: Decision Rule:
Step 3: Statistical test:
Why?
Step 4: Conduct the test \u2013 place cell B77 in the output location box. <\/p>\n

Step 5: Conclusion and Interpretation
What is the p-value: <\/p>\n

Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail test)? What is your decision: REJ or NOT reject the null?
What does this result say about our question of variance equality? <\/p>\n

B Are male and female average compa-ratios equal?
(Regardless of the outcome of the above F-test, assume equal variances for this test.) <\/p>\n

What is your decision: REJ or NOT reject the null?
Step 1: Ho:
Ha:
Step 2: Decision Rule:
Step 3: Statistical test:
Why?
4. Conduct the test \u2013 place cell B109 in the output location box. <\/p>\n

Step 5: Conclusion and Interpretation
What is the p-value: <\/p>\n

Is the P-value < 0.05 (for a one tail test) or 0.025 (for a two tail test)?
What, besides the p-value, needs to be considered with a one tail test? <\/p>\n

Decision: Reject or do not reject Ho? <\/p>\n

What does your decision on rejecting the null hypothesis mean? <\/p>\n

If the null hypothesis was rejected, calculate the effect size value: <\/p>\n

If the effect size was calculated, what doe the result mean in terms of why the null hypothesis was rejected? <\/p>\n

What does the result of this test tell us about our question on salary equality? <\/p>\n

6 Considering both the salary information in the lectures and your compa-ratio information, what conclusions can you reach about equal pay for equal work? <\/p>\n

Why \u2013 what statistical results support this conclusion? <\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

The outline is set on excel spread sheet, just need assistance with imputing the functions and data on the master sheet, to be able to answer the data analysis: Do Males make more the Females. Can negotiate a fee if Read More …<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-127762","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/127762","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/comments?post=127762"}],"version-history":[{"count":0,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/127762\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/media?parent=127762"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/categories?post=127762"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/tags?post=127762"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}