Ashford University What Have You Learned About Statistics? Final Paper

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The Final Paper provides you with an opportunity to integrate and reflect on what you have learned during the class.

The question to address is: “What have you learned about statistics?” In developing your responses, consider—at a minimum—and discuss the application of each of the course elements in analyzing and making decisions about data (counts and/or measurements).

In your paper,

  • Discuss the following course elements:
    • Descriptive statistics
    • Inferential statistics
    • Hypothesis development and testing
    • Selection of appropriate statistical tests
    • Evaluating statistical results.

The Final Paper

  • Must be three to five double-spaced pages in length (not including title and references pages) and formatted according to APA style as outlined in the Ashford Writing Center (Links to an external site.).
  • Must include a separate title page with the following:
    • Title of paper
    • Student’s name
    • Course name and number
    • Instructor’s name
    • Date submitted
  • Must begin with an introductory paragraph that has a succinct thesis statement.
  • Must address the topic of the paper with critical thought.
  • Must end with a conclusion that reaffirms your thesis.
  • Must use at least three scholarly sources in addition to the course text.
  • Must document all sources in APA style as outlined in the Ashford Writing Center
  • Must include a separate references page that is formatted according to APA style as outlined in the Ashford Writing Center.

Carefully review the Grading Rubric (Links to an external site.) for the criteria that will be used to evaluate your assignment.

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ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Salary Compa- Midpoint ratio 64.3 27.5 35.2 63.4 46.6 72.9 43 22.7 78.1 23.6 23.8 67 40.8 25 24 38.1 68.7 33.2 24.3 34.7 75.7 56.1 24 58.3 23.6 21.9 41.8 74.6 75.4 47.7 23.1 28 61.3 27.7 24 24.1 23.2 58.9 35.7 24.1 1.128 0.888 1.135 1.113 0.970 1.088 1.074 0.989 1.165 1.024 1.036 1.176 1.021 1.085 1.042 0.952 1.205 1.070 1.058 1.120 1.130 1.169 1.044 1.214 1.025 0.953 1.046 1.113 1.126 0.993 1.006 0.904 1.076 0.894 1.044 1.049 1.010 1.034 1.151 1.049 57 31 31 57 48 67 40 23 67 23 23 57 40 23 23 40 57 31 23 31 67 48 23 48 23 23 40 67 67 48 23 31 57 31 23 23 23 57 31 23 Age 34 52 30 42 36 36 32 32 49 30 41 52 30 32 32 44 27 31 32 44 43 48 36 30 41 22 35 44 52 45 29 25 35 26 23 27 22 45 27 24 Performance Service Gender Rating 85 80 75 100 90 70 100 90 100 80 100 95 100 90 80 90 55 80 85 70 95 65 65 75 70 95 80 95 95 90 60 95 90 80 90 75 95 95 90 90 8 7 5 16 16 12 8 9 10 7 19 22 2 12 8 4 3 11 1 16 13 6 6 9 4 2 7 9 5 18 4 4 9 2 4 3 2 11 6 2 0 0 1 0 0 0 1 1 0 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 Raise Degree Gender1 5.7 3.9 3.6 5.5 5.7 4.5 5.7 5.8 4 4.7 4.8 4.5 4.7 6 4.9 5.7 3 5.6 4.6 4.8 6.3 3.8 3.3 3.8 4 6.2 3.9 4.4 5.4 4.3 3.9 5.6 5.5 4.9 5.3 4.3 6.2 4.5 5.5 6.3 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 M M F M M M F F M F F M F F F M F F M F M F F F M F M F M M F M M M F F F M F M 41 42 43 44 45 46 47 48 49 50 46.4 24.9 76 60.6 56.4 60.2 64.6 69.7 60.4 61.4 1.161 1.082 1.135 1.063 1.174 1.057 1.134 1.222 1.059 1.078 40 23 67 57 48 57 57 57 57 57 25 32 42 45 36 39 37 34 41 38 80 100 95 90 95 75 95 90 95 80 5 8 20 16 8 20 5 11 21 12 0 1 1 0 1 0 0 1 0 0 4.3 5.7 5.5 5.2 5.2 3.9 5.5 5.3 6.6 4.6 0 1 0 1 1 1 1 1 0 0 M F F M F M M F M M Grade E B B E D F C A F A A E C A A C E B A B F D A D A A C F F D A B E B A A A E B A Do not manipuilate Data set on this page, copy to another page to make changes The ongoing question that the weekly assignments will focus on is: Are males and females paid the same Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. The column labels in the table mean: ID – Employee sample number Salary – Salary in thousands Age – Age in years Performance Rating - Appraisal rating (employee evaluation scor Service – Years of service (rounded) Gender – 0 = male, 1 = female Midpoint – salary grade midpointRaise – percent of last raise Grade – job/pay grade Degree (0= BS\BA 1 = MS) Gender1 (Male or Female) Compa-ratio - salary divided by midpoint C A F E D E E E E E Week 1: Descriptive Statistics, including Probability While the lectures will examine our equal pay question from the compa-ratio viewpoint, our weekly assignments will examining the issue using the salary measure. The purpose of this assignmnent is two fold: 1. Demonstrate mastery with Excel tools. 2. Develop descriptive statistics to help examine the question. 3. Interpret descriptive outcomes The first issue in examining salary data to determine if we - as a company - are paying males and females equally for descriptive statistics to give us something to make a preliminary decision on whether we have an issue or not. 1 2 3 4 5 Descriptive Statistics: Develop basic descriptive statistics for Salary The first step in analyzing data sets is to find some summary descriptive statistics for key variables. Suggestion: Copy the gender1 and salary columns from the Data tab to columns T and U at the right. Then use Data Sort (by gender1) to get all the male and female salary values grouped together. a. b. Develop a 5-number summary for the overall, male, and female SALARY variable. For full credit, show the excel formulas in each cell rather than simply the numerical answer. Location Measures: comparing Male and Female midpoints to the overall Salary data range. For full credit, show the excel formulas in each cell rather than simply the numerical answer. Using the entire Salary range and the M and F midpoints found in Q2 a. What would each midpoint's percentile rank be in the overall range? b. What is the normal curve z value for each midpoint within overall range? Probability Measures: comparing Male and Female midpoints to the overall Salary data range For full credit, show the excel formulas in each cell rather than simply the numerical answer. Using the entire Salary range and the M and F midpoints found in Q2, find a. The Empirical Probability of equaling or exceeding (=>) that value for b. The Normal curve Prob of => that value for each group Conclusions: What do you make of these results? In comparing the overall, male, and female outcomes, what relationship(s) see, to exist between the data sets? The relationship(s) that I see to exsist between the data sets From the results I see that females outcomes are lower than males. However, the numbers aren't spread out. If we wer to take another random sample, I feel as though the data would be differernt. Each sample of data should match that p From the data found,males and females have almost the same range and standard deviations for compa-ratios, but that female What does this suggest about our equal pay for equal work question? I would suggest that this data using the compa-ratio doesn't help answer our question as to who gets paid more. This is only week one and I would suggest that we look at different measures such as comparing raises, education and Use the Descriptive Statistics function in the Data Analysis tab to develop the descriptive statistics summary for the overall group's overall salary. (Place K19 in output range.) Highlight the mean, sample standard deviation, and range. Using Fx (or formula) functions find the following (be sure to show the formula and not just the value in each cell) asked for salary statistics for each gender: Mean: Sample Standard Deviation: Range: Male =AVERAGE(U2:U26) =STDEV.S(U2:U26) =MAX(U2:U26)-MIN(U2:U26) Max 3rd Q Midpoint 1st Q Min Female =AVERAGE(U27:U51) =STDEV.S(U27:U51) =MAX(U27:U51)-MIN(U27:U51) Overall =MAX(U2:U51) =QUARTILE.EXC(U2:U51,3) =MEDIAN(U2:U51) =QUARTILE.EXC(U2:U51,1) =MIN(U2:U51) Males =MAX(U2:U26) =QUARTILE.EXC(U2:U26,3) =MEDIAN(U2:U26) =QUARTILE.EXC(U2:U26,1) =MIN(U2:U26) Be sure to include findings from this wee Females =MAX(U27:U51) =QUARTILE.EXC(U27:U51,3) =MEDIAN(U27:U51) =QUARTILE.EXC(U27:U51,1) =MIN(U27:U51) Be sure to include findings from this week's lectures as well. Male =PERCENTRANK.EXC(U2:U26,MEDIAN(U2:U26)) =STANDARDIZE(E41,D29,D30) Male =COUNTIF(U2:U26,">="&E41)/50 =1-NORM.S.DIST(0.34,FALSE) Male =PERCENTRANK.EXC(U2:U26,MEDIAN(U2:U26)) =STANDARDIZE(E41,D29,D30) Male =COUNTIF(U2:U26,">="&E41)/50 =1-NORM.S.DIST(0.34,FALSE) Place Excel ou Mean Standard Error Median Mode Standard Deviat Sample Varianc Kurtosis Skewness Range Minimum Maximum Sum Count Female =PERCENTRANK.EXC(U27:U51,MEDIAN(U27:U51)) =STANDARDIZE(E41,E30,E31) Female =COUNTIF(U27:U51,">="&E41)/50 =1-NORM.S.DIST(0.16,FALSE) Place Excel outcome in Cell K19 Column1 Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Use Excel's =PERCENTRANK.EXC function Use Excel's =STANDARDIZE function Show the calculation formula = value/50 or =countif(range,">="&cell)/50 Use "=1-NORM.S.DIST" function 45.334 2.73948097273918 42.4 24 19.3710557275539 375.237799999999 -1.51179649969813 0.219610128596479 56.2 21.9 78.1 2266.7 50 Gender1 M M M M M M M M M M M M M M M M M M M M M M M M M F F F F F F F F F F F F F F F F F F Salary 64.3 27.5 63.4 46.6 72.9 78.1 67 38.1 24.3 75.7 23.6 41.8 75.4 47.7 28 61.3 27.7 58.9 24.1 46.4 60.6 60.2 64.6 60.4 61.4 35.2 43 22.7 23.6 23.8 40.8 25 24 68.7 33.2 34.7 56.1 24 58.3 21.9 74.6 23.1 24 F F F F F F F 24.1 23.2 35.7 24.9 76 56.4 69.7 Week 2: Identifying Significant Differences - part 1 To Ensure full credit for each question, you need to show how you got your results. This involves either showing wh or showing the excel formula in each cell. Be sure to copy the appropriate data columns from the data tab to As with our examination of compa-ratio in the lecture, the first question we have about salary between the genders in What we do, depends upon our findings. 1 As with the compa-ratio lecture example, we want to examine salary variation within the groups - are they a What is the data input ranged used for this question: Sheet1!B1:C51 b c. Which is needed for this question: a one- or two-tail hypothesis statement and test ? Answer: Two tail test Why: The test aims at investigating whether differences are equal or not mea Step 1: Step 2: Step 3: Step 4: Step 5: Ho: Salary variation within groups are equal Ha: Salary variations withing groups are different Significance (Alpha): 0.05 Test Statistic and test: F statistic and F test Why this test? The test involves determining whether variances are equal or not Decision rule: Reject the null when p value is
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Explanation & Answer

Attached.

Running head: STATISTICAL ANALYSIS

Statistics
Course
Institution

1

STATISTICAL ANALYSIS

2
Introduction

Throughout the course I have learned a lot with regards to various concepts in the field
of statistics. Concepts learned include; hypothesis development, collection of data, carrying
out descriptive statistics, selection of appropriate tests to test the hypothesis, carrying out
inferential tests, and interpretation of results. This paper discusses in detail the various
statistical concepts learned.
Concepts Learned
Descriptive statistics
Descriptive statistics include all statistics that is aimed at merely summarizing the
collected data. Descriptive statistics therefore assist in summarizing large amounts of data by
computing specific numbers that reflect the data. Descriptive statistics can be computed for
both categorical and quantitative data. For categorical data, the descriptive statistics that can
be computed are usually frequencies and maybe the mode based on the frequencies generated.
Frequencies assist in compressing down categorical data into various groups. However, for
some categorical data it may be impossible to compute frequencies due to the diverse nature
of responses (Kaur, Stoltzfus & Yellapu, 2018) .
Descriptive statistics are best for summarizing quantitative data. For quantitative data,
the descriptive statistics computed include; measures of central tendency and measures of
dispersion. Measures of central tendency give values around which the data tends to revolve
around. The measures of central tendency include; the mean, median and mode. Measures of
dispersion are values that explain how data deviates from the ce...


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