Attached is an Excel spreadsheet, listing data for the Acme Widget company, specifically their salary ranges broken down by sex (male/female). Use that data to investigate the claim that women in that company make less money, on average, than men.

Note that the spreadsheet contains the data twice, for your convenience. Once it is separated into "male" and "female" data, suitable for drawing conclusion for each set separately. Then the data appears in one long set, male and female data together, which is suitable for creating contingency tables. should report that looks and reads as professional as possible. - 1 pgs - make sure to:

• Mention some general facts about data set, such as where you got it from, whether it's a sample or the entire population, and what the sample size is.
• Next, you should describe the variables you are interested in, and list some of their statistics, such as how many men and women are in your data, total sample size, type of variable, etc.
• You should then formulate your hypothesis and supply data to support it. For example, you could compute mean, mode, and/or median for your data, if apropriate. Depending on whether they are similar or different for the male and female portion of your data, different hypothesis would be supported. But note that comparing two means alone does not prove any conclusive relationship between them.
• You need to compute a contingency table with the associated p-value to support your conclusions, as described in section 4. Only this p-value can be used to determine whether there is a relationship between two variables! As is often the case, you could us Excel or StatCrunch (use "Stat | Tables | Contingency | with Data") to find the p-value, with StatCrunch being the easier method.

As to what I consider a "professional" report: anything that (1) has a suitable introduction, (2) makes a clear statement as to what you are claiming, (3) has data/statistics to support your claim, (4) includesa chart, table, or diagram to visualize your claim(s), and (5) has a concise conclusion. Note that it's completely easy to "copy-and-paste" tables or charts, so do not fill up your report with charts that you really do not need for your argument. Only add relevant chartsto your report.

Note also that the "salary" appears in two columns, once with actual labels in the "Salary Level" column, and the other one with a corresponding " Salary Code". You can use the column with the labels to make "Pivot Table" (Excel) or "Contingency Table" (StatCrunch) and conduct the required Chi-Square test, while you can use the numeric code to compute, for example, the mean salary category (which is not the same as the mean salary!). But, you could just as well ignore the code and work directly with the categories, the Pivot or Contingency Table, and the Chi-Square test.

THE CHART PROVIDED HERE IS THE ONLY THING MY PROFESSOR GAVE US

A small number of employees from the Acme Widget company were chosen at random. The following data was collected from them:
Combined Data		Female Employees		Male Employees
Salary Level	Salary Code	Sex		Salary Level	Salary Code		Salary Level	Salary Code
$10K-20K	1	female		$10K-20K	1		$10K-20K	1
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
$10K-20K	1	female		$10K-20K	1		$20K-30K	2
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