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Levels

Managers and professionals often pay more attention to the levels of their measures (means, sums, etc.) than to the variation in the data (the dispersion or the probability patterns/distributions that describe the data).  For the measures you identified in Discussion 1, why must dispersion be considered to truly understand what the data is telling us about what we measure/track?  How can we make decisions about outcomes and results if we do not understand the consistency (variation) of the data?  Does looking at the variation in the data give us a different understanding of results?

BUS 308 Statistics for Managers

Paper details:

take provided data and answer the following questions: 3 What is the probability for a: Probability a.       Randomly selected person being a male in grade E? b.      Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E? c. Why are the results different? 4 For each group (overall, females, and males) find: Overall Female Male a. The value that cuts off the top 1/3 salary in each group. b. The z score for each value: c. The normal curve probability of exceeding this score: d. What is the empirical probability of being at or exceeding this salary value? e. The value that cuts off the top 1/3 compa in each group. f. The z score for each value: g. The normal curve probability of exceeding this score: h. What is the empirical probability of being at or exceeding this compa value? i. How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question? 5.      What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? What is the difference between the sal and compa measures of pay? Conclusions from looking at salary results: Conclusions from looking at compa results: Do both salary measures show the same results? Can we make any conclusions about equal pay for equal work yet?

Score:    Week 1.    Measurement and Description – chapters 1 and 2

Problem Set Week One

Week One Assignment will require students to utilize the following resources to complete the assignment. Assignment instructions are contained within the following resources.

All statistical calculations will use the Employee Salary Data Set and Week 1 assignment sheet.

<1 point>    1    Measurement issues.  Data, even numerically coded variables, can be one of 4 levels –
nominal, ordinal, interval, or ratio.  It is important to identify which level a variable is, as
this impact the kind of analysis we can do with the data.  For example, descriptive statistics
such as means can only be done on interval or ratio level data.
Please list under each label, the variables in our data set that belong in each group.
Nominal    Ordinal    Interval    Ratio

b.    For each variable that you did not call ratio, why did you make that decision?

<1 point>    2    The first step in analyzing data sets is to find some summary descriptive statistics for key variables.
For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.
You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions.
(the range must be found using the difference between the =max and =min functions with Fx) functions.
Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.
Salary    Compa    Age    Perf. Rat.    Service
Overall    Mean
Standard Deviation
Range
Female    Mean
Standard Deviation
Range
Male    Mean
Standard Deviation
Range

<1 point>    3    What is the probability for a:                            Probability
a.       Randomly selected person being a male in grade E?
b.      Randomly selected male being in grade E?
Note part b is the same as given a male, what is probabilty of being in grade E?
c.     Why are the results different?

<1 point>    4    For each group (overall, females, and males) find:                                Overall    Female    Male
a.    The value that cuts off the top 1/3 salary in each group.                                            Hint: can use these Fx functions
b.    The z score for each value:                                            Excel’s standize function
c.    The normal curve probability of exceeding this score:                                            1-normsdist function
d.    What is the empirical probability of being at or exceeding this salary value?
e.    The value that cuts off the top 1/3 compa in each group.
f.    The z score for each value:
g.    The normal curve probability of exceeding this score:
h.    What is the empirical probability of being at or exceeding this compa value?
i.    How do you interpret the relationship between the data sets?  What do they mean about our equal pay for equal work question?

<2 points>    5.          What conclusions can you make about the issue of male and female pay equality?  Are all of the results consistent?
What is the difference between the sal and compa measures of pay?

Conclusions from looking at salary results:

Conclusions from looking at compa results:

Do both salary measures show the same results?

Can we make any conclusions about equal pay for equal work yet?

See comments at the right of the data set.
ID    Salary    Compa    Midpoint    Age    Performance Rating    Service    Gender    Raise    Degree    Gender1    Grade
8    23    1.000    23    32    90    9    1    5.8    0    F    A        The ongoing question that the weekly assignments will focus on is:  Are males and females paid the same for equal work (under the Equal Pay Act)?
10    22    0.956    23    30    80    7    1    4.7    0    F    A        Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11    23    1.000    23    41    100    19    1    4.8    0    F    A
14    24    1.043    23    32    90    12    1    6    0    F    A        The column labels in the  table mean:
15    24    1.043    23    32    80    8    1    4.9    0    F    A        ID – Employee sample number             Salary – Salary in thousands
23    23    1.000    23    36    65    6    1    3.3    1    F    A        Age – Age in years            Performance Rating  – Appraisal rating (Employee evaluation score)
26    24    1.043    23    22    95    2    1    6.2    1    F    A        Service – Years of service (rounded)            Gender:   0 = male, 1 = female
31    24    1.043    23    29    60    4    1    3.9    0    F    A        Midpoint – salary grade midpoint                Raise – percent of last raise
35    24    1.043    23    23    90    4    1    5.3    1    F    A        Grade – job/pay grade            Degree (0= BSBA 1 = MS)
36    23    1.000    23    27    75    3    1    4.3    1    F    A        Gender1 (Male or Female)            Compa – salary divided by midpoint
37    22    0.956    23    22    95    2    1    6.2    1    F    A
42    24    1.043    23    32    100    8    1    5.7    0    F    A
3    34    1.096    31    30    75    5    1    3.6    0    F    B
18    36    1.161    31    31    80    11    1    5.6    1    F    B
20    34    1.096    31    44    70    16    1    4.8    1    F    B
39    35    1.129    31    27    90    6    1    5.5    1    F    B
7    41    1.025    40    32    100    8    1    5.7    0    F    C
13    42    1.050    40    30    100    2    1    4.7    1    F    C
22    57    1.187    48    48    65    6    1    3.8    0    F    D
24    50    1.041    48    30    75    9    1    3.8    1    F    D
45    55    1.145    48    36    95    8    1    5.2    0    F    D
17    69    1.210    57    27    55    3    1    3    0    F    E
48    65    1.140    57    34    90    11    1    5.3    1    F    E
28    75    1.119    67    44    95    9    1    4.4    1    F    F
43    77    1.149    67    42    95    20    1    5.5    1    F    F
19    24    1.043    23    32    85    1    0    4.6    1    M    A
25    24    1.043    23    41    70    4    0    4    0    M    A
40    25    1.086    23    24    90    2    0    6.3    0    M    A
2    27    0.870    31    52    80    7    0    3.9    0    M    B
32    28    0.903    31    25    95    4    0    5.6    0    M    B
34    28    0.903    31    26    80    2    0    4.9    1    M    B
16    47    1.175    40    44    90    4    0    5.7    0    M    C
27    40    1.000    40    35    80    7    0    3.9    1    M    C
41    43    1.075    40    25    80    5    0    4.3    0    M    C
5    47    0.979    48    36    90    16    0    5.7    1    M    D
30    49    1.020    48    45    90    18    0    4.3    0    M    D
1    58    1.017    57    34    85    8    0    5.7    0    M    E
4    66    1.157    57    42    100    16    0    5.5    1    M    E
12    60    1.052    57    52    95    22    0    4.5    0    M    E
33    64    1.122    57    35    90    9    0    5.5    1    M    E
38    56    0.982    57    45    95    11    0    4.5    0    M    E
44    60    1.052    57    45    90    16    0    5.2    1    M    E
46    65    1.140    57    39    75    20    0    3.9    1    M    E
47    62    1.087    57    37    95    5    0    5.5    1    M    E
49    60    1.052    57    41    95    21    0    6.6    0    M    E
50    66    1.157    57    38    80    12    0    4.6    0    M    E
6    76    1.134    67    36    70    12    0    4.5    1    M    F
9    77    1.149    67    49    100    10    0    4    1    M    F
21    76    1.134    67    43    95    13    0    6.3    1    M    F
29    72    1.074    67    52    95    5    0    5.4    0    M    F

BUS 308 Statistics for Managers

Paper details:

take provided data and answer the following questions: 3 What is the probability for a: Probability a.       Randomly selected person being a male in grade E? b.      Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E? c. Why are the results different? 4 For each group (overall, females, and males) find: Overall Female Male a. The value that cuts off the top 1/3 salary in each group. b. The z score for each value: c. The normal curve probability of exceeding this score: d. What is the empirical probability of being at or exceeding this salary value? e. The value that cuts off the top 1/3 compa in each group. f. The z score for each value: g. The normal curve probability of exceeding this score: h. What is the empirical probability of being at or exceeding this compa value? i. How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question? 5.      What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? What is the difference between the sal and compa measures of pay? Conclusions from looking at salary results: Conclusions from looking at compa results: Do both salary measures show the same results? Can we make any conclusions about equal pay for equal work yet?

Score:    Week 1.    Measurement and Description – chapters 1 and 2

Problem Set Week One

Week One Assignment will require students to utilize the following resources to complete the assignment. Assignment instructions are contained within the following resources.

All statistical calculations will use the Employee Salary Data Set and Week 1 assignment sheet.

<1 point>    1    Measurement issues.  Data, even numerically coded variables, can be one of 4 levels –
nominal, ordinal, interval, or ratio.  It is important to identify which level a variable is, as
this impact the kind of analysis we can do with the data.  For example, descriptive statistics
such as means can only be done on interval or ratio level data.
Please list under each label, the variables in our data set that belong in each group.
Nominal    Ordinal    Interval    Ratio

b.    For each variable that you did not call ratio, why did you make that decision?

<1 point>    2    The first step in analyzing data sets is to find some summary descriptive statistics for key variables.
For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.
You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions.
(the range must be found using the difference between the =max and =min functions with Fx) functions.
Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.
Salary    Compa    Age    Perf. Rat.    Service
Overall    Mean
Standard Deviation
Range
Female    Mean
Standard Deviation
Range
Male    Mean
Standard Deviation
Range

<1 point>    3    What is the probability for a:                            Probability
a.       Randomly selected person being a male in grade E?
b.      Randomly selected male being in grade E?
Note part b is the same as given a male, what is probabilty of being in grade E?
c.     Why are the results different?

<1 point>    4    For each group (overall, females, and males) find:                                Overall    Female    Male
a.    The value that cuts off the top 1/3 salary in each group.                                            Hint: can use these Fx functions
b.    The z score for each value:                                            Excel’s standize function
c.    The normal curve probability of exceeding this score:                                            1-normsdist function
d.    What is the empirical probability of being at or exceeding this salary value?
e.    The value that cuts off the top 1/3 compa in each group.
f.    The z score for each value:
g.    The normal curve probability of exceeding this score:
h.    What is the empirical probability of being at or exceeding this compa value?
i.    How do you interpret the relationship between the data sets?  What do they mean about our equal pay for equal work question?

<2 points>    5.          What conclusions can you make about the issue of male and female pay equality?  Are all of the results consistent?
What is the difference between the sal and compa measures of pay?

Conclusions from looking at salary results:

Conclusions from looking at compa results:

Do both salary measures show the same results?

Can we make any conclusions about equal pay for equal work yet?

See comments at the right of the data set.
ID    Salary    Compa    Midpoint    Age    Performance Rating    Service    Gender    Raise    Degree    Gender1    Grade
8    23    1.000    23    32    90    9    1    5.8    0    F    A        The ongoing question that the weekly assignments will focus on is:  Are males and females paid the same for equal work (under the Equal Pay Act)?
10    22    0.956    23    30    80    7    1    4.7    0    F    A        Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11    23    1.000    23    41    100    19    1    4.8    0    F    A
14    24    1.043    23    32    90    12    1    6    0    F    A        The column labels in the  table mean:
15    24    1.043    23    32    80    8    1    4.9    0    F    A        ID – Employee sample number             Salary – Salary in thousands
23    23    1.000    23    36    65    6    1    3.3    1    F    A        Age – Age in years            Performance Rating  – Appraisal rating (Employee evaluation score)
26    24    1.043    23    22    95    2    1    6.2    1    F    A        Service – Years of service (rounded)            Gender:   0 = male, 1 = female
31    24    1.043    23    29    60    4    1    3.9    0    F    A        Midpoint – salary grade midpoint                Raise – percent of last raise
35    24    1.043    23    23    90    4    1    5.3    1    F    A        Grade – job/pay grade            Degree (0= BSBA 1 = MS)
36    23    1.000    23    27    75    3    1    4.3    1    F    A        Gender1 (Male or Female)            Compa – salary divided by midpoint
37    22    0.956    23    22    95    2    1    6.2    1    F    A
42    24    1.043    23    32    100    8    1    5.7    0    F    A
3    34    1.096    31    30    75    5    1    3.6    0    F    B
18    36    1.161    31    31    80    11    1    5.6    1    F    B
20    34    1.096    31    44    70    16    1    4.8    1    F    B
39    35    1.129    31    27    90    6    1    5.5    1    F    B
7    41    1.025    40    32    100    8    1    5.7    0    F    C
13    42    1.050    40    30    100    2    1    4.7    1    F    C
22    57    1.187    48    48    65    6    1    3.8    0    F    D
24    50    1.041    48    30    75    9    1    3.8    1    F    D
45    55    1.145    48    36    95    8    1    5.2    0    F    D
17    69    1.210    57    27    55    3    1    3    0    F    E
48    65    1.140    57    34    90    11    1    5.3    1    F    E
28    75    1.119    67    44    95    9    1    4.4    1    F    F
43    77    1.149    67    42    95    20    1    5.5    1    F    F
19    24    1.043    23    32    85    1    0    4.6    1    M    A
25    24    1.043    23    41    70    4    0    4    0    M    A
40    25    1.086    23    24    90    2    0    6.3    0    M    A
2    27    0.870    31    52    80    7    0    3.9    0    M    B
32    28    0.903    31    25    95    4    0    5.6    0    M    B
34    28    0.903    31    26    80    2    0    4.9    1    M    B
16    47    1.175    40    44    90    4    0    5.7    0    M    C
27    40    1.000    40    35    80    7    0    3.9    1    M    C
41    43    1.075    40    25    80    5    0    4.3    0    M    C
5    47    0.979    48    36    90    16    0    5.7    1    M    D
30    49    1.020    48    45    90    18    0    4.3    0    M    D
1    58    1.017    57    34    85    8    0    5.7    0    M    E
4    66    1.157    57    42    100    16    0    5.5    1    M    E
12    60    1.052    57    52    95    22    0    4.5    0    M    E
33    64    1.122    57    35    90    9    0    5.5    1    M    E
38    56    0.982    57    45    95    11    0    4.5    0    M    E
44    60    1.052    57    45    90    16    0    5.2    1    M    E
46    65    1.140    57    39    75    20    0    3.9    1    M    E
47    62    1.087    57    37    95    5    0    5.5    1    M    E
49    60    1.052    57    41    95    21    0    6.6    0    M    E
50    66    1.157    57    38    80    12    0    4.6    0    M    E
6    76    1.134    67    36    70    12    0    4.5    1    M    F
9    77    1.149    67    49    100    10    0    4    1    M    F
21    76    1.134    67    43    95    13    0    6.3    1    M    F
29    72    1.074    67    52    95    5    0    5.4    0    M    F