Final Project

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DataAnalysisExampleHumanResourcesData.xlsx
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TableOfContents

Table of contents with hyperlinks for this document
Excluding standard worksheets that come with the original data
Sheet name Purpose
NotesOnDataPrep!A1 Tips and tricks for students in doing data analysis in Excel
SalaryPivotTable!A1 Using a histogram of salary to compare other variables in terms of chunks of salary
DescriptiveStatsForFrequency!A1 Example of producing descriptive stats for chunks of a numeric variable (grouping, frequency table as 'categories')
VariableDescriptiveStatsPHStat!A1 Example of descriptive stats produced by PHStat and then edited, items removed that are not needed
Correlations!A1 Instructor reference for how all variables are inter-related
RegressionAge!A1 Example of regression output highighting output to pay attention to
SPSSRegressionAllEnter!A1 Instructor reference - regressing salary on all independent variables to discern stongest, independent predictors
PivotTableCreatePercentPolygon!A1 Example of comparing distributions between two categories with different number of cases or different scales, i.e., version of percent polygon
Analysis results
Gender univariate descriptive statistics
GenderAnalysis!A1 Gender/Salary; Gender/Job Grade Classification analysis; Gender/other independent variables
Salary histogram, distribution
Compare gender/salary descriptive statistics
GenderCompareDescriptives!A1 Comparison Table gender descriptive statistics in terms of all variables. This might be something worth doing.
EthnicitySalaryAnalysis!A1 Ethnicity/Salary analysis
OptionalEthnicitySalaryAnalysis!A1 Optional ethnicity/salary analysis - distribution of ethnicity over chunks of salary, percent polygon
EthnicityJGClassAnalysis!A1 Ethnicity/Job Grade Classification analysis
AgeSalaryAnalysis!A1 Age/Salary analysis
AgeJobGradeClassAnalysis!A1 Age/Job grade classification analysis
YearsWorkedSalaryAnalysis!A1 Years worked/Salary analysis
Years worked/Job grade classification analysis
Relationship between endogenous variables
Job grade classification/Salary analysis
Relationship between independent variables
PercentPolygonGenderYearsWorked!A1 Compare years worked distribution by gender; Example of comparing distributions between two categories with different number of cases or different scales, i.e., version of percent polygon
Standard sheets that come with the data
Variable INFO'!A1 Information on variables
Human Resources DATA'!A1 Data
Cross-Class-Table'!A1
Summary Table'!A1
Histogram!A1
% Polygons 2 Groups'!A1
Freq. & % Distribution'!A1

Variable INFO

TableOfContents!A1
The data are a random sample of 120 responses to a survey conducted by the VP of Human Resources at a large company.
Source: INFO 501 class at Montclair State University
Variables
Salary in thousands of dollars (K)
Age in years
YrsWork in years
JGClass job-grade classification of 1, 3, 5, 7, 9, 11 (lowest skill job to highest skill job)
Ethnicity 1=Minority 0=Not Minority
Gender (Male, Female)
Named ranges created in this worksheet - use these names to address the data more quickly then manually selecting data
Use the name of the range in dialog boxes rather than clicking and dragging ranges.
Example of using names instead of manual ranges:
50681.1320754717 Female salary
56465.671641791 male salary
-10% Percent difference

Human Resources DATA

TableOfContents!A1
Salary Age YrsWork JGClass EthnicityCODE Gender code Ethnicity Gender
$31,200 19 1 3 1 0 Minority Female
$40,400 28 3 3 0 0 Not Minority Female
$42,600 29 3 5 1 0 Minority Female
$39,800 26 2 5 0 0 Not Minority Female
$33,300 22 2 3 1 0 Minority Female
$35,600 28 4 3 0 0 Not Minority Female
$34,200 38 6 3 1 0 Minority Female
$43,600 35 3 5 0 0 Not Minority Female
$37,600 28 5 5 1 0 Minority Female
$34,600 27 6 3 1 0 Minority Female
$37,700 36 1 3 0 0 Not Minority Female
$48,100 28 3 5 0 0 Not Minority Female
$38,900 36 2 5 0 0 Not Minority Female
$46,700 33 10 5 1 0 Minority Female
$58,000 49 3 9 0 0 Not Minority Female
$52,200 38 6 5 0 0 Not Minority Female
$46,500 45 3 7 0 0 Not Minority Female
$52,300 47 2 7 0 0 Not Minority Female
$50,000 30 8 5 0 0 Not Minority Female
$54,200 39 6 7 1 0 Minority Female
$47,000 60 10 5 0 0 Not Minority Female
$57,500 47 3 7 0 0 Not Minority Female
$47,700 62 4 9 0 0 Not Minority Female
$49,000 39 9 5 0 0 Not Minority Female
$70,100 53 5 7 0 0 Not Minority Female
$60,000 57 7 7 0 0 Not Minority Female
$48,600 43 2 7 0 0 Not Minority Female
$57,000 61 5 7 0 0 Not Minority Female
$57,700 33 7 7 0 0 Not Minority Female
$47,800 44 8 7 0 0 Not Minority Female
$47,600 51 3 5 0 0 Not Minority Female
$59,000 49 6 9 0 0 Not Minority Female
$72,000 47 3 7 0 0 Not Minority Female
$43,500 53 7 7 1 0 Minority Female
$70,000 39 12 9 0 0 Not Minority Female
$54,100 48 3 5 0 0 Not Minority Female
$55,500 49 5 5 0 0 Not Minority Female
$60,000 54 6 7 0 0 Not Minority Female
$52,300 48 4 3 0 0 Not Minority Female
$67,000 50 5 7 0 0 Not Minority Female
$58,000 50 15 7 1 0 Minority Female
$38,700 50 3 3 0 0 Not Minority Female
$62,100 51 3 7 0 0 Not Minority Female
$65,500 53 9 9 0 0 Not Minority Female
$43,200 62 3 5 0 0 Not Minority Female
$67,500 57 12 11 1 0 Minority Female
$56,700 56 6 7 0 0 Not Minority Female
$39,600 58 3 5 0 0 Not Minority Female
$39,200 60 14 5 0 0 Not Minority Female
$58,500 61 8 7 0 0 Not Minority Female
$39,800 64 5 5 0 0 Not Minority Female
$67,500 66 2 9 0 0 Not Minority Female
$68,900 67 5 9 0 0 Not Minority Female
$39,600 24 1 5 0 1 Not Minority Male
$33,400 20 2 3 1 1 Minority Male
$42,100 24 2 5 1 1 Minority Male
$54,100 31 1 7 1 1 Minority Male
$46,100 27 4 5 0 1 Not Minority Male
$56,300 39 2 7 1 1 Minority Male
$45,600 37 3 5 1 1 Minority Male
$48,500 35 2 7 1 1 Minority Male
$54,600 30 7 7 0 1 Not Minority Male
$50,100 39 4 7 0 1 Not Minority Male
$47,100 37 6 5 0 1 Not Minority Male
$46,800 40 2 5 0 1 Not Minority Male
$44,100 28 3 7 1 1 Minority Male
$56,100 42 4 7 0 1 Not Minority Male
$37,500 31 5 3 0 1 Not Minority Male
$45,500 33 9 5 0 1 Not Minority Male
$43,500 59 9 7 1 1 Minority Male
$45,000 49 5 7 0 1 Not Minority Male
$67,500 58 7 7 0 1 Not Minority Male
$62,000 54 6 9 1 1 Minority Male
$56,700 41 4 7 0 1 Not Minority Male
$48,100 32 6 3 0 1 Not Minority Male
$45,000 50 2 7 1 1 Minority Male
$50,000 45 5 7 1 1 Minority Male
$75,500 40 12 9 0 1 Not Minority Male
$66,000 56 4 11 1 1 Minority Male
$62,200 40 14 9 1 1 Minority Male
$47,500 59 5 7 1 1 Minority Male
$53,000 56 8 5 0 1 Not Minority Male
$56,700 48 7 7 0 1 Not Minority Male
$54,900 42 3 5 0 1 Not Minority Male
$53,200 38 4 7 1 1 Minority Male
$45,600 36 9 7 1 1 Minority Male
$56,300 49 2 5 0 1 Not Minority Male
$43,300 49 2 3 0 1 Not Minority Male
$46,400 36 5 7 1 1 Minority Male
$64,300 54 3 5 0 1 Not Minority Male
$61,000 36 7 9 0 1 Not Minority Male
$48,100 38 9 7 1 1 Minority Male
$38,600 48 6 5 1 1 Minority Male
$56,000 47 14 7 1 1 Minority Male
$60,500 51 9 7 0 1 Not Minority Male
$64,500 49 7 7 1 1 Minority Male
$52,500 51 9 5 0 1 Not Minority Male
$79,000 52 15 11 0 1 Not Minority Male
$76,500 52 7 9 0 1 Not Minority Male
$60,000 49 9 9 0 1 Not Minority Male
$62,500 54 8 9 1 1 Minority Male
$72,200 55 15 11 1 1 Minority Male
$61,500 56 9 7 0 1 Not Minority Male
$68,700 56 10 9 0 1 Not Minority Male
$82,300 57 15 11 0 1 Not Minority Male
$67,800 57 5 9 0 1 Not Minority Male
$61,000 58 12 7 1 1 Minority Male
$67,800 59 7 9 1 1 Minority Male
$81,100 59 15 11 0 1 Not Minority Male
$45,600 60 16 5 0 1 Not Minority Male
$77,500 62 10 11 0 1 Not Minority Male
$68,000 63 9 9 0 1 Not Minority Male
$73,000 63 15 11 0 1 Not Minority Male
$68,000 68 8 9 0 1 Not Minority Male
$43,200 69 10 5 0 1 Not Minority Male
$76,000 70 9 9 0 1 Not Minority Male
$69,500 71 18 9 0 1 Not Minority Male
$39,900 72 8 5 1 1 Minority Male
$64,200 73 15 9 1 1 Minority Male
$46,500 74 10 5 0 1 Not Minority Male

NotesOnDataPrep

Tips and tricks
1. It will make the student's life easier to create named ranges in the data for the ranges they need. Simply sort, highlight the range, and in the box upper left, type in a name. Use that name in functions and formulas (e.g., quartile(), or descriptive stats - you can use named ranges in PHStat and Data Analysis Toolpack)
2. Note that Pivot tables can provide all descriptive statistics except median, quartiles, IQR. If Zscores indicate that there is an outlier on one side, students should not be using the mean, but as a work around, you can ask them to note that, discuss what it means and then use the mean/SD anyway; OR you can require them to manually create those separately from the pivot table (or don't use a pivot table, use the data analysis toolpack or PHSTat).
3. Instructions for producing a histogram/frequency table with a Pivot Table:
a. Create a pivot table using the numeric variable (age) as the row label
b. Group the row label - Group button on ribbon. Choose chunks in dialog box.
make sure you click in the data, not the header, or the button will be greyed out
Play with the beginning, end value and chunks to make bins common sense, i.e., 1-10, not 1-11
c. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item.
d. To work with data, it is frequently easier to copy pivot table data and paste as - paste as values.
e. Word of warning: If you divide data into subcategories - chunks of salary for women, men - if there are no values for a category, Excel won't list it - you have to manually put a zero in for the value.
4. Getting Excel stuff into Word for a report: It might be easier to paste as a picture object - easier to manipulate.

PercentDifference

Count of Gender Column Labels
Row Labels Female Male Grand Total
3 16.98% 5.97% 10.83%
5 33.96% 25.37% 29.17%
7 33.96% 35.82% 35.00%
9 13.21% 22.39% 18.33%
11 1.89% 10.45% 6.67%
Grand Total 100.00% 100.00% 100.00%
Count of Gender Column Labels
Row Labels Female Male
3 17% 6% 96%
5 34% 25% 29%
7 34% 36% 5%
9 13% 22% 52%
11 2% 10% 139%

Percent difference in Male to female

Proportions

3 5 7 9 11 0.95950920245398763 0.28951115329852861 5.3268765133171914E-2 0.51582278481012667 1.3881278538812787 3 5 7 9 11 3 5 7 9 11

3 5 7 9 11 1

Job Levels

LevelGender

Row Labels Average of Salary
3 38484.6153846154
Female 37555.5555555556
Male 40575
5 46345.7142857143
Female 45250
Male 47505.8823529412
7 54809.5238095238
Female 57194.4444444444
Male 53020.8333333333
9 65740.9090909091
Female 62371.4285714286
Male 67313.3333333333
11 74825
Female 67500
Male 75871.4285714286
Grand Total 53910.8333333333

Average salary of gender on each level

Total

Female Male Female Male Female Male Female Male Female Male 3 5 7 9 11 37555.555555555555 40575 45250 47505.882352941175 57194.444444444445 53020.833333333336 62371.428571428572 67313.333333333328 67500 75871.428571428565

BiVariateDistributionChart

Average of Salary Column Labels
Row Labels Female Male Grand Total
30000-39999 36938.4615384615 37800 37177.7777777778
40000-49999 45878.5714285714 45680 45761.7647058823
50000-59999 55533.3333333333 54321.4285714286 54948.275862069
60000-69999 64812.5 64578.9473684211 64648.1481481481
70000-79999 70700 75671.4285714286 74180
80000-90000 81700 81700
Grand Total 50681.1320754717 56465.671641791 53910.8333333333

Gender average salary comparison by salary level

Female 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 36938.461538461539 4587 8.571428571428 55533.333333333336 64812.5 70700 Male 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 37800 45680 54321.428571428572 64578.947368421053 75671.428571428565 81700

Salary ranges

Average salary

GenderDescriptiveStats

TableOfContents!A1
Gender/salary comparison - descriptive statistics
Run descriptive statistics twice - once with named range "malesalary" and again with "femalesalary", then copy and paste them next to each other
Column1 Column1 Comparing male and female salary <== table title centered across columns
Statistic Male Female <==row headers differentiated from data
Mean 56465.671641791 Mean 50681.1320754717 Mean $ 56,466 $ 50,681 <==number formatting
Standard Error 1474.68546001 Standard Error 1515.0288634913 Standard Error $ 1,475 $ 1,515 <==all statistics that are NOT being used are REMOVED
Median 56000 Median 49000 Median $ 56,000 $ 49,000
Mode 45600 Mode 39800 Standard Deviation $ 12,071 $ 11,030
Standard Deviation 12070.8207177324 Standard Deviation 11029.5766116484 Range $ 48,900 $ 40,800
Sample Variance 145704712.799638 Sample Variance 121651560.232221 Minimum $ 33,400 $ 31,200
Kurtosis -0.8044449928 Kurtosis -0.9313514963 Maximum $ 82,300 $ 72,000
Skewness 0.3244762148 Skewness 0.1887693789 Count 67 53
Range 48900 Range 40800
Minimum 33400 Minimum 31200
Maximum 82300 Maximum 72000
Sum 3783200 Sum 2686100
Count 67 Count 53

GenderDescriptiveStats (2)

TableOfContents!A1
Gender/salary comparison - descriptive statistics
Run descriptive statistics twice - once with named range "malesalary" and again with "femalesalary", then copy and paste them next to each other
Table 1 <== Start with labeleling each table by number, sequentially (charts too - call them "Figure x")
Column1 Column1 Comparing male and female salary <== table title centered across columns or left justified, meaningful, not abstract
Statistic Malea Female <==row headers differentiated from data (bold); lines above and below column headers
Mean 56465.671641791 Mean 50681.1320754717 Count 67 53 <==If you want to show subsets of statistics, use an italicized header, indent following
Standard Error 1474.68546001 Standard Error 1515.0288634913 Measures of central tendency <==indented to show part of type of statistic
Median 56000 Median 49000 Mean $ 56,466 $ 50,681 <==number formatting
Mode 45600 Mode 39800 Median $ 56,000 $ 49,000
Standard Deviation 12070.8207177324 Standard Deviation 11029.5766116484 Measures of central variance 90%
Sample Variance 145704712.799638 Sample Variance 121651560.232221 Standard Deviation $ 12,071 $ 11,030
Kurtosis -0.8044449928 Kurtosis -0.9313514963 Minimum $ 33,400 $ 31,200 0.9375
Skewness 0.3244762148 Skewness 0.1887693789 Maximum $ 82,300 $ 72,000 0.7162162162
Range 48900 Range 40800 Range $ 48,900 $ 40,800 0.9137387481
Minimum 33400 Minimum 31200 Test for outliers
Maximum 82300 Maximum 72000 Zscore of Minimum -1.9 -1.8
Sum 3783200 Sum 2686100 Zscore of Maximum 2.1 1.9
Count 67 Count 53 Source: Random sample of 120 RJCorp employees, June 2015 <==Note: All statistics that are NOT being used are REMOVED
a Notation if needed (superscript used after header "Male" above as an example

SalaryDistributionHistogram

Table of contents
Salary histogram/distribution
Row Labels Count of Salary
30000-39999 18
40000-49999 34
50000-59999 29
60000-69999 27
70000-79999 10
80000-90000 2
Grand Total 120
Row Labels Count of Salary
30-39K 18
40-49K 34
50-59K 29
60-69K 27
70-79K 10
80-89K 2

Histogram of salary

Total 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 18 34 29 27 10 2

Salary levels (in dollars)

Number of employees

Figure 1: Distribution of salaries in RJ Corp

Count of Salary 30-39K 40-49K 50-59K 60-69K 70-79K 80-89K 18 34 29 27 10 2

Salary

Number of employees

GenderDescriptiveStatistics (2

Categorical variable descriptive statistics produced through a pivot table
Pivot table output
Row Labels Count of Gender Count of Gender2
Female 53 44.17% 12%
Male 67 55.83%
Grand Total 120 100.00%
Copy, paste special, paste as a value:
Row Labels Count of Gender Count of Gender2
Female 53 0.4416666667
Male 67 0.5583333333
Grand Total 120 1
Format in an attractive manner by standards of good table formatting (see Chapter 9, or PowerPoint)
Note: I've used format as a table from the Home ribbon, the selected "Convert to Range" button to get rid of special drop downs.
Table 1
Gender descriptive statistics <==Title centered across columns or left justified, bold; meaningful
Gender Count Percent of total <==Column/row headers formatted to distinguish from data, centered
Female 53 44% <==Number formatting used - percentage formatting in this case
Male 67 56%
Grand Total 120 100%
0.2641509434

SalaryDescriptiveStatistics (2

Table of contents
Salary descriptive statistics
Column1 Table 2
Salary descriptive statistics <== table title centered across columns or left justified; meaningful
Mean 53910.8333333333 Statistic Figures <==row / column headers differentiated from data
Standard Error 1088.9229612112 Mean $ 53,911 <==number formatting
Median 53100 Median $ 53,100 <==all statistics that are NOT being used are REMOVED
Mode 48100 Standard Deviation $ 11,929
Standard Deviation 11928.5533848133 Range $ 51,100
Sample Variance 142290385.854342 Minimum $ 31,200
Kurtosis -0.6661524346 Maximum $ 82,300
Skewness 0.3069257671 Count 120
Range 51100
Minimum 31200
Maximum 82300
Sum 6469300
Count 120

Formatted output from Data Analysis Toolpack, Descriptive Statistics function

GenderAgeSalary

Average of Salary Column Labels Average of Salary Column Labels
Row Labels Female Male Row Labels Female Male
<20 $31,200 30000-39999 36938.4615384615 37800
20-29 $39,000 $41,060 40000-49999 45878.5714285714 45680
30-39 $48,564 $49,447 50000-59999 55533.3333333333 54321.4285714286
40-49 $54,873 $54,840 60000-69999 64812.5 64578.9473684211
50-59 $56,638 $63,914 70000-79999 70700 75671.4285714286
60-69 $52,089 $62,550 80000-90000 81700
70-80 $59,220

Comparing gender average salary by age group

Female < 20 20-29 30-39 40-49 50-59 60-69 70-80 31200 39000 48563.63636363636 54872.727272727272 56638.461538461539 52088.888888888891 Male < 20 20-29 30-39 40-49 50-59 60-69 70-80 41060 49446.666666666664 54840 63914.285714285717 62550 59220

Age groups

Average salary

GenderSalaryAvg

Row Labels Average of Salary Count of Salary Percent difference
Female $50,681 53 -10.24%
Male $56,466 67
Grand Total $53,911 120
Gender Average of Salary Count of Salary Percent difference
Female $ 50,681 53 -10%
Male $ 56,466 67

Average of Salary Female Male 50681.132075471702 56465.671641791043

AgeAnalysis

Pivot table producing descriptive statistics for chunks of age (age histogram)
TableOfContents!A1
Row Labels Count of Age Average of Salary StdDev of Salary Min of Salary Max of Salary
<20 1 $31,200 ERROR:#DIV/0! $31,200 $31,200
20-29 13 $39,792 $4,773 $33,300 $48,100
30-39 26 $49,073 $7,724 $34,200 $70,000
40-49 26 $54,854 $8,235 $38,600 $75,500
50-59 34 $61,132 $11,434 $38,700 $82,300
60-69 15 $56,273 $13,295 $39,200 $77,500
70-80 5 $59,220 $15,388 $39,900 $76,000
Grand Total 120 $53,911 $11,929 $31,200 $82,300
Row Labels Count of Age Average of Salary StdDev of Salary Min of Salary Max of Salary range coefficient of variation negative Zscore positive Zscore
15-24 5 $ 35,920 $ 4,670 $ 31,200 $ 42,100 $ 10,900 13% -1.01 1.32
25-34 17 $ 44,888 $ 6,832 $ 34,600 $ 57,700 $ 23,100 15% -1.51 1.88
35-44 26 $ 51,165 $ 9,192 $ 34,200 $ 75,500 $ 41,300 18% -1.85 2.65
45-54 35 $ 56,926 $ 9,876 $ 38,600 $ 79,000 $ 40,400 17% -1.86 2.24
55-64 28 $ 59,293 $ 12,956 $ 39,200 $ 82,300 $ 43,100 22% -1.55 1.78
65-75 9 $ 60,411 $ 13,371 $ 39,900 $ 76,000 $ 36,100 22% -1.53 1.17
Instructions:
1. Create a pivot table using the numeric variable (age) as the row label
2. Group the row label - Group button on ribbon. Choose chunks in dialog box.
3. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item.
4. To work with data, it is frequently easier to copy pivot table data and paste as - paste as values.

copy this, paste as values below

Age Line Fit Plot

Salary 19 28 29 26 22 28 38 35 28 27 36 28 36 33 49 38 45 47 30 39 60 47 62 39 53 57 43 61 33 44 51 49 47 53 39 48 49 54 48 50 50 50 51 53 62 57 56 58 60 61 64 66 67 24 20 24 31 27 39 37 35 30 39 37 40 28 42 31 33 59 49 58 54 41 32 50 45 40 56 40 59 56 48 42 38 36 49 49 36 54 36 38 48 47 51 49 51 52 52 49 54 55 56 56 57 57 58 59 59 60 62 63 63 68 69 70 71 72 73 74 31200 40400 42600 39800 33300 35600 34200 43600 37600 34600 37700 48100 38900 46700 58000 52200 46500 52300 50000 54200 47000 57500 47700 49000 70100 60000 48600 57000 57700 47800 47600 59000 72000 43500 70000 54100 55500 60000 52300 67000 58000 38700 62100 65500 43200 67500 56700 39600 39200 58500 39800 67500 68900 39600 33400 42100 54100 46100 56300 45600 48500 54600 50100 47100 46800 44100 56100 37500 45500 43500 45000 67500 62000 56700 48100 45000 50000 75500 66000 62200 47500 53000 56700 54900 53200 45600 56300 43300 46400 64300 61000 48100 38600 56000 60500 64500 52500 79000 76500 60000 62500 72200 61500 68700 82300 67800 61000 67800 81100 45600 77500 68000 73000 68000 43200 76000 69500 39900 64200 46500 Predicted Salary 19 28 29 26 22 28 38 35 28 27 36 28 36 33 49 38 45 47 30 39 60 47 62 39 53 57 43 61 33 44 51 49 47 53 39 48 49 54 48 50 50 50 51 53 62 57 56 58 60 61 64 66 67 24 20 24 31 27 39 37 35 30 39 37 40 28 42 31 33 59 49 58 54 41 32 50 45 40 56 40 59 56 48 42 38 36 49 49 36 54 36 38 48 47 51 49 51 52 52 49 54 55 56 56 57 57 58 59 59 60 62 63 63 68 69 70 71 72 73 74 1

Age

Salary

Simply create formulas here referencing values to the left

VariableDescriptiveStatsPHStat

TableOfContents!A1
PHStat ouput - Descriptive Statistics for HumanResources.xlsx
Descriptive Summary
Salary Age YrsWork JGClass Ethnicity Gender
Mean $53,911 47 6.47 6.62 0.32 0.56
Median $53,100 49 6 7 0 1
Mode $48,100 49 3 7 0 1
Minimum $31,200 19 1 3 0 0
Maximum $82,300 74 18 11 1 1
Range $51,100 55 17 8 1 1
Variance 142290385.8543 167.7815 15.9485 4.5913 0.2182 0.2487
Standard Deviation $11,929 12.9531 3.9936 2.1427 0.4671 0.4987
Coeff. of Variation 22.13% 27.56% 61.76% 32.38% 147.51% 89.31%
Skewness 0.3069 -0.0986 0.8545 0.1834 0.7982 -0.2379
Kurtosis -0.6662 -0.7283 0.0532 -0.5082 -1.3862 -1.9766
Count 120 120 120 120 120 120
Standard Error 1088.9230 1.1824 0.3646 0.1956 0.0426 0.0455
Descriptive statistics summary
Salary Gender
Mean $53,911 0.56
Median $53,100 1
Mode $48,100 1
Minimum $31,200 0
Maximum $82,300 1
Range $51,100 1
Standard Deviation $11,929 0.4987
Coeff. of Variation 22.13% 89.31%
Count 120 120

Students should get rid of anything that is not covered in the course and they don't understand in the output.

Tables should have headers differentiated, number formatting done, centered data.

GenderAnalysis

TableOfContents!A1
Analysis of varibles in terms of gender via pivot table
Row Labels Count of Gender Percent Average of Salary StdDev of Salary Average of Age Average of YrsWork Average of JGClass Average of EthnicityCODE
Female 53 44.17% $50,681 $11,030 45.3 5.3 6.0 0.2
Male 67 55.83% $56,466 $12,071 48.3 7.4 7.1 0.4
Grand Total 120 100.00% $53,911 $11,929 47.0 6.5 6.6 0.3
Instructions:
1. Create a pivot table using the categorical variable (gender) as the row label
2. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item.
3. To work with data, it is frequently easier to copy pivot table data and paste as - paste as values.
Descriptive Statistics
Endogenous variables Other independent variables
Salary JGClass Age YrsWork EthnicityCODE
Female Male Female Male Female Male Female Male Female Male
Mean $ 50,681 $ 56,466 5.98 7.12 45.34 48.31 5.30 7.39 0.21 0.40
Standard Error $ 1,515 $ 1,475 0.27 0.26 1.72 1.61 0.45 0.53 0.06 0.06
Median $ 49,000 $ 56,000 5 7 48 49 5 7 0 0
Mode $ 39,800 $ 45,600 5 7 28 49 3 9 0 0
Standard Deviation $ 11,030 $ 12,071 1.99 2.14 12.55 13.21 3.24 4.30 0.41 0.49
Range $ 40,800 $ 48,900 8 8 48 54 14 17 1 1
Minimum $ 31,200 $ 33,400 3 3 19 20 1 1 0 0
Maximum $ 72,000 $ 82,300 11 11 67 74 15 18 1 1
Count 53 67 53 67 53 67 53 67 53 67
Coefficient of variance 22% 21% 33% 30% 28% 27% 61% 58% 197% 123%
Zscore negative $ (1.77) $ (1.91) -1.50 -1.92 -2.10 -2.14 -1.33 -1.48 -0.51 -0.82
Zscore positive $ 1.93 $ 2.14 2.53 1.81 1.73 1.94 2.99 2.47 1.94 1.21
Quartile 1 $ 40,400 $ 46,250 5 5 36 38 3 4 na na
Quartile 3 $ 58,000 $ 65,250 7 9 54 54 7 9 na na
Inter Quartile Range $ 17,600 $ 19,000 2 4 18 16 4 5 na na
Note: I created special named ranges in the data to make it easier - e.g., SalaryFemale, SalaryMale

SalaryPivotTable

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Analysis of variables in terms of chunks of salary
Row Labels Count of Salary Average of Age Average of EthnicityCODE Average of Gender code Average of YrsWork Average of JGClass
30000-39999 18 38.17 0.44 0.28 4.22 4.00
40000-49999 34 43.65 0.38 0.59 5.59 5.71
50000-59999 29 44.93 0.24 0.48 5.55 6.45
60000-69999 27 56.19 0.33 0.70 8.00 8.33
70000-79999 10 53.30 0.10 0.70 10.30 9.40
80000-89999 2 58.00 0.00 1.00 15.00 11.00
Grand Total 120 47 0.3166666667 0.5583333333 6.4666666667 6.6166666667
Instructions:
1. Create a pivot table using the numeric variable (age) as the row label
2. Group the row label - Group button on ribbon. Choose chunks in dialog box.
3. Add anything you want to the Values box. Add items multiple times to get multiple stats about the same item.
4. To work with data, it is frequently easier to copy pivot table data and paste as - paste as values.

GenderCompareDescriptives

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Table comparing descriptve statistics for all variables in terms of gender
Salary Age YrsWork JGClass EthnicityCODE
Female Male Female Male Female Male Female Male Female Male
Mean $ 50,681 $ 56,466 45.3 48.3 5.3 7.4 6.0 7.1 0.2 0.4
Standard Error $ 1,515 $ 1,475 1.7 1.6 0.4 0.5 0.3 0.3 0.1 0.1
Median $ 49,000 $ 56,000 48 49 5 7 5 7 0 0
Mode $ 39,800 $ 45,600 28 49 3 9 5 7 0 0
Standard Deviation $ 11,030 $ 12,071 12.5 13.2 3.2 4.3 2.0 2.1 0.4 0.5
Sample Variance 121651560.232221 145704712.799638 157.3824383164 174.5517865219 10.5224963716 18.513794663 3.9419448476 4.591587517 0.1676342525 0.2442333786
Kurtosis -0.9313514963 -0.8044449928 -0.92511818 -0.6818428647 1.0936677151 -0.4368448489 -0.4548349394 -0.5676589436 0.2105423988 -1.8936805556
Skewness 0.1887693789 0.3244762148 -0.2357663046 -0.0428389974 1.1727443433 0.5747426633 0.2109272442 0.1068805146 1.4846023258 0.4046946723
Range $ 40,800 $ 48,900 48 54 14 17 8 8 1 1
Minimum $ 31,200 $ 33,400 19 20 1 1 3 3 0 0
Maximum $ 72,000 $ 82,300 67 74 15 18 11 11 1 1
Sum 2686100 3783200 2403 3237 281 495 317 477 11 27
Count 53 67 53 67 53 67 53 67 53 67
Male
Salary Age YrsWork JGClass EthnicityCODE
Mean 56465.671641791 Mean 48.3134328358 Mean 7.3880597015 Mean 7.1194029851 Mean 0.4029850746
Standard Error 1474.68546001 Standard Error 1.6140788534 Standard Error 0.5256665231 Standard Error 0.2617845621 Standard Error 0.0603761071
Median 56000 Median 49 Median 7 Median 7 Median 0
Mode 45600 Mode 49 Mode 9 Mode 7 Mode 0
Standard Deviation 12070.8207177324 Standard Deviation 13.211804817 Standard Deviation 4.3027659317 Standard Deviation 2.1427989913 Standard Deviation 0.4941997355
Sample Variance 145704712.799638 Sample Variance 174.5517865219 Sample Variance 18.513794663 Sample Variance 4.591587517 Sample Variance 0.2442333786
Kurtosis -0.8044449928 Kurtosis -0.6818428647 Kurtosis -0.4368448489 Kurtosis -0.5676589436 Kurtosis -1.8936805556
Skewness 0.3244762148 Skewness -0.0428389974 Skewness 0.5747426633 Skewness 0.1068805146 Skewness 0.4046946723
Range 48900 Range 54 Range 17 Range 8 Range 1
Minimum 33400 Minimum 20 Minimum 1 Minimum 3 Minimum 0
Maximum 82300 Maximum 74 Maximum 18 Maximum 11 Maximum 1
Sum 3783200 Sum 3237 Sum 495 Sum 477 Sum 27
Count 67 Count 67 Count 67 Count 67 Count 67

PivotTableCreatePercentPolygon

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Pivot table used to create percent polygon - comparing percents of males vs. females in terms of chunks of age
Row Labels Count of Age Count of Age2
Female 53 44.17% Female Male
<20 1 1.89% 15-24 3.77% 4.48%
20-29 8 15.09% 25-34 18.87% 10.45%
30-39 11 20.75% 35-44 18.87% 23.88%
40-49 11 20.75% 45-54 33.96% 25.37%
50-59 13 24.53% 55-64 20.75% 25.37%
60-69 9 16.98% 65-75 3.77% 10.45%
Male 67 55.83%
20-29 5 7.46%
30-39 15 22.39%
40-49 15 22.39%
50-59 21 31.34%
60-69 6 8.96%
70-80 5 7.46%
Grand Total 120 100.00%
Instructions
1. Pivot table created using gender and then age as row labels
2. Group age row labels
3. Create a count column (not necessary)
4. Drag age again to the values box.
5. Chage values - click Show Values As, choose Percent Of Parent Row Total
6. Copy data, paste as values, then create a line chart with that
- you will have to check the row labels - if there are no values in a chunk, Excel will not show the chunk. Simply type it in manually and insert a value of zero.
Comparing counts of gender by bins of age
male female
<20 0 1
20-34 10 11
35-49 25 19
50-64 25 20
65-80 7 2
Female 15-24 25-34 35-44 45-54 55-64 65-75 3.7735849056603772E-2 0.18867924528301888 0.18867924528301888 0.33962264150943394 0.20754716981132076 3.7735849056603772E-2 Male 15-24 25-34 35-44 45-54 55-64 65-75 4.4776119402985072E-2 0.1044776119402985 0.23880597014925373 0.2537313432835821 0.2537313432835821 0.1044776119402985 Female 15-24 25-34 35-44 45-54 55-64 65-75 3.7735849056603772E-2 0.18867924528301888 0.18867924528301888 0.33962264150943394 0.20754716981132076 3.7735849056603772E-2 Male 15-24 25-34 35-44 45-54 55-64 65-75 4.4776119402985072E-2 0.1044776119402985 0.23880597014925373 0.2537313432835821 0.2537313432835821 0.1044776119402985

Comparing gender by age

male < 20 20-34 35-49 50-64 65-80 0 10 25 25 7 female < 20 20-34 35-49 50-64 65-80 1 11 19 20 2

PercentPolygonGenderYearsWorked

TableOfContents!A1
Compare distributions of male vs. female in terms of years worked
Row Labels Count of YrsWork Count of YrsWork2 Years worked Male Female
Female 53 44.17% 1-3 0.2089552239 0.4150943396
1-4 25 47.17% 4-6 0.2388059701 0.320754717
5-8 20 37.74% 7-9 0.3134328358 0.1509433962
9-12 6 11.32% 10-12 0.0895522388 0.0754716981
13-16 2 3.77% 13-15 0.1194029851 0.0377358491
Male 67 55.83% 16-18 0.0298507463 0
1-4 20 29.85%
5-8 21 31.34%
9-12 16 23.88%
13-16 9 13.43%
17-20 1 1.49%
Grand Total 120 100.00%
Instructions
1. Pivot table created using gender and then age as row labels
2. Group age row labels
3. Create a count column (not necessary)
4. Drag age again to the values box.
5. Chage values - click Show Values As, choose Percent Of Parent Row Total
6. Copy data, paste as values, then create a line chart with that
- you will have to check the row labels - if there are no values in a chunk, Excel will not show the chunk. Simply type it in manually and insert a value of zero.
years worked male female
1-4 20 25 30% 47%
5-8 21 20 31% 38%
9-12 16 6 24% 11%
13-16 9 2 13% 4%
17-20 1 0 1% 0%
total 67 53

Comparing percents in years worked by gender

Male 1-3 4-6 7-9 10-12 13-15 16-18 0.20895522388059701 0.23880597014925373 0.31343283582089554 8.9552238805970144E-2 0.11940298507462686 2.9850746268656716E-2 Female 1-3 4-6 7-9 10-12 13-15 16-18 0.41509433962264153 0.32075471698113206 0.15094339622641509 7.5471698113207544E-2 3.7735849056603772E-2 0

% Of Each Age Category per Age Grouping

Male 1-3 4-6 7-9 10-12 13-15 16-18 0.20895522388059701 0.23880597014925373 0.31343283582089554 8.9552238805970144E-2 0.11940 298507462686 2.9850746268656716E-2 Female 1-3 4-6 7-9 10-12 13-15 16-18 0.41509433962264153 0.32075471698113206 0.15094339622641509 7.5471698113207544E-2 3.7735849056603772E-2 0

Years worked

Overall percentage

Comparing counts in years worked by gender

male 1-4 5-8 9-12 13-16 17-20 20 21 16 9 1 female 1-4 5-8 9-12 13-16 17-20 25 20 6 2 0

EthnicitySalaryAnalysis

TableOfContents!A1
Ethnicity and salary
Row Labels Count of Ethnicity Average of Salary StdDev of Salary2 Max of Salary Min of Salary2
Minority 38 $50,097 $11,216 72200 31200
Not Minority 82 $55,678 $11,899 82300 35600
Grand Total 120 $53,911 $11,929 82300 31200
Row Labels Count of Ethnicity Average of Salary StdDev of Salary2 Max of Salary Min of Salary2
Minority 38 $50,097 $11,216 $72,200 $31,200
Not Minority 82 $55,678 $11,899 $82,300 $35,600
Grand Total 120 $53,911 $11,929 $82,300 $31,200
Non Minority Minority
Coefficient of variance 21% 22%
Zscore negative -1.6873 -1.6849
Zscore positive 2.2372 1.9706
Range $46,700 $41,000

OptionalEthnicitySalaryAnalysis

TableOfContents!A1
Copy, Paste Values below:
Optional Ethnicity Salary Analiysis - percent polygon Note: For minority, a row label is missing because there is no data,
you need to manually add that and input a value of zero
Column Labels
Count of Ethnicity Count of Ethnicity2 Minority Non-Minority
Row Labels Minority Not Minority Minority Not Minority 30000-39999 21% 12%
30000-39999 8 10 21.05% 12.20% 40000-49999 34% 26%
40000-49999 13 21 34.21% 25.61% 50000-59999 18% 27%
50000-59999 7 22 18.42% 26.83% 60000-69999 24% 22%
60000-69999 9 18 23.68% 21.95% 70000-79999 3% 11%
70000-79999 1 9 2.63% 10.98% 80000-90000 0% 2%
80000-90000 2 0.00% 2.44%
Minority Non-minority
30000-39999 8 10
40000-49999 13 21
50000-59999 7 22
60000-69999 9 18
70000-79999 1 9
80000-90000 0 2

Comparing % of non/minority by bins of salary

Minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 0.21052631578947367 0.34210526315789475 0.18421052631578946 0.23684210526315788 2.6315789473684209E-2 0 Non-Minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 0.12195121951219512 0.25609756097560976 0.26829268292682928 0.21951219512195122 0.10975609756097561 2.4390243902439025E-2

Comparing non/minority counts by bins of salary

Minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 8 13 7 9 1 0 Non-minority 30000-39999 40000-49999 50000-59999 60000-69999 70000-79999 80000-90000 10 21 22 18 9 2

EthnicityJGClassAnalysis

TableOfContents!A1
Ethnicity/Job Grade Classification analysis
Row Labels Count of JGClass Average of JGClass4 StdDev of JGClass3 Min of JGClass2 Max of JGClass
Minority 38 6.6842105263 2.1573916077 3 11
Not Minority 82 6.5853658537 2.1485063403 3 11
Grand Total 120 6.6166666667 2.1427357575 3 11
Non Minority Minority
Coefficient of variance 33% 32%
Zscore negative -1.6688 2.0547
Zscore positive 2.0547 -1.6688
Range 8 8

YearsWorkedSalaryAnalysis

YearsWorkedSalaryAnalysis!A1
Years worked and salary
Row Labels Count of YrsWork Average of Salary4 StdDev of Salary2 Max of Salary Min of Salary Average of JGClass Average of EthnicityCODE Average of Gender code
1-4 45 $ 48,476 $ 9,496 $ 72,000 $ 31,200 5.67 0.29 0.44
5-8 41 $ 54,300 $ 10,909 $ 76,500 $ 34,200 6.61 0.34 0.51
9-12 22 $ 58,150 $ 11,703 $ 77,500 $ 43,200 7.36 0.27 0.73
13-16 11 $ 64,800 $ 14,319 $ 82,300 $ 39,200 8.82 0.45 0.82
17-20 1 $ 69,500 ERROR:#DIV/0! $ 69,500 $ 69,500 9.00 0.00 1.00
Grand Total 120 $ 53,911 $ 11,929 $ 82,300 $ 31,200 6.62 0.32 0.56
Row Labels Count of YrsWork Average of Salary3 StdDev of Salary2 Max of Salary Min of Salary
1-4 45 48475.5555555556 9496.0794674711 72000 31200
5-8 41 54300 10908.8954527945 76500 34200
9-12 22 58150 11702.6553763299 77500 43200
13-16 11 64800 14319.1480193481 82300 39200
17-20 1 69500 ERROR:#DIV/0! 69500 69500
1-4 5-8 9-12 13-16 17-20
Coefficient of variance 20% 20% 20% 22% ERROR:#DIV/0!
Zscore negative
Zscore positive
Range

AgeSalaryAnalysis

TableOfContents!A1
Age/Salary Analysis
Age and Salary Age and other variables
Row Labels Count of Age Average of Salary4 StdDev of Salary3 Max of Salary2 Min of Salary Average of YrsWork Average of Gender code Average of JGClass
<20 1 $ 31,200 ERROR:#DIV/0! $ 31,200 $ 31,200 1 0 3
20-29 13 $ 39,792 $ 4,773 $ 48,100 $ 33,300 3.0769230769 0.3846153846 4.3846153846
30-39 26 $ 49,073 $ 7,724 $ 70,000 $ 34,200 5.7307692308 0.5769230769 5.8461538462
40-49 26 $ 54,854 $ 8,235 $ 75,500 $ 38,600 5.3076923077 0.5769230769 6.6153846154
50-59 34 $ 61,132 $ 11,434 $ 82,300 $ 38,700 7.7647058824 0.6176470588 7.7058823529
60-69 15 $ 56,273 $ 13,295 $ 77,500 $ 39,200 8.2666666667 0.4 7.4
70-80 5 $ 59,220 $ 15,388 $ 76,000 $ 39,900 12 1 7.4
Grand Total 120 $ 53,911 $ 11,929 $ 82,300 $ 31,200 6.5 0.6 6.6
<20 20-34 35-49 50-64 65-80
Coefficient of variance
Zscore negative
Zscore positive
Range

AgeJobGradeClassAnalysis

TableOfContents!A1
Age and Job Grade Classification analysis
Row Labels Count of JGClass Average of JGClass5 StdDev of JGClass4 Max of JGClass3 Min of JGClass2
<20 1 3 ERROR:#DIV/0! 3 3
20-29 13 4.3846153846 1.2608503439 7 3
30-39 26 5.8461538462 1.7132964178 9 3
40-49 26 6.6153846154 1.6988684017 9 3
50-59 34 7.7058823529 2.0820941056 11 3
60-69 15 7.4 2.2928460169 11 5
70-80 5 7.4 2.19089023 9 5
Grand Total 120 6.6166666667 2.1427357575 11 3
<20 3 ERROR:#DIV/0! 3 3
20-34 4.7142857143 1.45405836 7 3
35-49 6.4090909091 1.702504063 9 3
50-64 7.5777777778 2.1583757455 11 3
65-80 7.6666666667 2 9 5
<20 20-34 35-49 50-64 65-80
Coefficient of variance
Zscore negative
Zscore positive
Range
Row Labels Count of JGClass Average of JGClass5 StdDev of JGClass4 Max of JGClass3 Min of JGClass2
<20 1 3 ERROR:#DIV/0! 3 3
20-34 21 4.7142857143 1.45405836 7 3
35-49 44 6.4090909091 1.702504063 9 3
50-64 45 7.5777777778 2.1583757455 11 3
65-80 9 7.6666666667 2 9 5
Grand Total 120 6.6166666667 2.1427357575 11 3

DataCopy

TableOfContents!A1
Salary Age YrsWork JGClass EthnicityCODE Gender code Ethnicity Gender
31200 19 1 3 1 0 Minority Female
40400 28 3 3 0 0 Not Minority Female
42600 29 3 5 1 0 Minority Female
39800 26 2 5 0 0 Not Minority Female
33300 22 2 3 1 0 Minority Female
35600 28 4 3 0 0 Not Minority Female
34200 38 6 3 1 0 Minority Female
43600 35 3 5 0 0 Not Minority Female
37600 28 5 5 1 0 Minority Female
34600 27 6 3 1 0 Minority Female
37700 36 1 3 0 0 Not Minority Female
48100 28 3 5 0 0 Not Minority Female
38900 36 2 5 0 0 Not Minority Female
46700 33 10 5 1 0 Minority Female
58000 49 3 9 0 0 Not Minority Female
52200 38 6 5 0 0 Not Minority Female
46500 45 3 7 0 0 Not Minority Female
52300 47 2 7 0 0 Not Minority Female
50000 30 8 5 0 0 Not Minority Female
54200 39 6 7 1 0 Minority Female
47000 60 10 5 0 0 Not Minority Female
57500 47 3 7 0 0 Not Minority Female
47700 62 4 9 0 0 Not Minority Female
49000 39 9 5 0 0 Not Minority Female
70100 53 5 7 0 0 Not Minority Female
60000 57 7 7 0 0 Not Minority Female
48600 43 2 7 0 0 Not Minority Female
57000 61 5 7 0 0 Not Minority Female
57700 33 7 7 0 0 Not Minority Female
47800 44 8 7 0 0 Not Minority Female
47600 51 3 5 0 0 Not Minority Female
59000 49 6 9 0 0 Not Minority Female
72000 47 3 7 0 0 Not Minority Female
43500 53 7 7 1 0 Minority Female
70000 39 12 9 0 0 Not Minority Female
54100 48 3 5 0 0 Not Minority Female
55500 49 5 5 0 0 Not Minority Female
60000 54 6 7 0 0 Not Minority Female
52300 48 4 3 0 0 Not Minority Female
67000 50 5 7 0 0 Not Minority Female
58000 50 15 7 1 0 Minority Female
38700 50 3 3 0 0 Not Minority Female
62100 51 3 7 0 0 Not Minority Female
65500 53 9 9 0 0 Not Minority Female
43200 62 3 5 0 0 Not Minority Female
67500 57 12 11 1 0 Minority Female
56700 56 6 7 0 0 Not Minority Female
39600 58 3 5 0 0 Not Minority Female
39200 60 14 5 0 0 Not Minority Female
58500 61 8 7 0 0 Not Minority Female
39800 64 5 5 0 0 Not Minority Female
67500 66 2 9 0 0 Not Minority Female
68900 67 5 9 0 0 Not Minority Female
39600 24 1 5 0 1 Not Minority Male
33400 20 2 3 1 1 Minority Male
42100 24 2 5 1 1 Minority Male
54100 31 1 7 1 1 Minority Male
46100 27 4 5 0 1 Not Minority Male
56300 39 2 7 1 1 Minority Male
45600 37 3 5 1 1 Minority Male
48500 35 2 7 1 1 Minority Male
54600 30 7 7 0 1 Not Minority Male
50100 39 4 7 0 1 Not Minority Male
47100 37 6 5 0 1 Not Minority Male
46800 40 2 5 0 1 Not Minority Male
44100 28 3 7 1 1 Minority Male
56100 42 4 7 0 1 Not Minority Male
37500 31 5 3 0 1 Not Minority Male
45500 33 9 5 0 1 Not Minority Male
43500 59 9 7 1 1 Minority Male
45000 49 5 7 0 1 Not Minority Male
67500 58 7 7 0 1 Not Minority Male
62000 54 6 9 1 1 Minority Male
56700 41 4 7 0 1 Not Minority Male
48100 32 6 3 0 1 Not Minority Male
45000 50 2 7 1 1 Minority Male
50000 45 5 7 1 1 Minority Male
75500 40 12 9 0 1 Not Minority Male
66000 56 4 11 1 1 Minority Male
62200 40 14 9 1 1 Minority Male
47500 59 5 7 1 1 Minority Male
53000 56 8 5 0 1 Not Minority Male
56700 48 7 7 0 1 Not Minority Male
54900 42 3 5 0 1 Not Minority Male
53200 38 4 7 1 1 Minority Male
45600 36 9 7 1 1 Minority Male
56300 49 2 5 0 1 Not Minority Male
43300 49 2 3 0 1 Not Minority Male
46400 36 5 7 1 1 Minority Male
64300 54 3 5 0 1 Not Minority Male
61000 36 7 9 0 1 Not Minority Male
48100 38 9 7 1 1 Minority Male
38600 48 6 5 1 1 Minority Male
56000 47 14 7 1 1 Minority Male
60500 51 9 7 0 1 Not Minority Male
64500 49 7 7 1 1 Minority Male
52500 51 9 5 0 1 Not Minority Male
79000 52 15 11 0 1 Not Minority Male
76500 52 7 9 0 1 Not Minority Male
60000 49 9 9 0 1 Not Minority Male
62500 54 8 9 1 1 Minority Male
72200 55 15 11 1 1 Minority Male
61500 56 9 7 0 1 Not Minority Male
68700 56 10 9 0 1 Not Minority Male
82300 57 15 11 0 1 Not Minority Male
67800 57 5 9 0 1 Not Minority Male
61000 58 12 7 1 1 Minority Male
67800 59 7 9 1 1 Minority Male
81100 59 15 11 0 1 Not Minority Male
45600 60 16 5 0 1 Not Minority Male
77500 62 10 11 0 1 Not Minority Male
68000 63 9 9 0 1 Not Minority Male
73000 63 15 11 0 1 Not Minority Male
68000 68 8 9 0 1 Not Minority Male
43200 69 10 5 0 1 Not Minority Male
76000 70 9 9 0 1 Not Minority Male
69500 71 18 9 0 1 Not Minority Male
39900 72 8 5 1 1 Minority Male
64200 73 15 9 1 1 Minority Male
46500 74 10 5 0 1 Not Minority Male

Cross-Class-Table

TableOfContents!A1
Cross Classification Table
Count of Ethnicity Gender
Ethnicity Female Male Grand Total
Minority 11 27 38
Not Minority 42 40 82
Grand Total 53 67 120

Summary Table

TableOfContents!A1
One-Way Summary Table
Count of Ethnicity
Ethnicity Total
Minority 38
Not Minority 82
Grand Total 120

Bar Chart

Bar Chart

Total Minority Not Minority 38 82

Ethnicity

Histogram

TableOfContents!A1

Freq. & % Distribution

TableOfContents!A1
Frequency Distribution for Salary
bins midpts Frequency Percentage
29999.9 0 0.0%
39999.9 35000 18 15.0%
49999.9 45000 34 28.3%
59999.9 55000 29 24.2%
69999.9 65000 27 22.5%
79999.9 75000 10 8.3%
89999.9 85000 2 1.7%
Total 120 100.0%

% Polygons 2 Groups

TableOfContents!A1

SideBySide Bar Chart

Side-By-Side Chart

Female Minority Not Minority 11 42 Male Minority Not Minority 27 40