| Score: |
Week 1. |
Measurement and Description – chapters 1 and 2 |
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| <1 point> |
1 |
Measurement issues. Data, even numerically coded variables, can be one of 4 levels – |
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nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, as |
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this impact the kind of analysis we can do with the data. For example, descriptive statistics |
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such as means can only be done on interval or ratio level data. |
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Please list under each label, the variables in our data set that belong in each group. |
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Nominal |
Ordinal |
Interval |
Ratio |
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b. |
For each variable that you did not call ratio, why did you make that decision? |
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| <1 point> |
2 |
The first step in analyzing data sets is to find some summary descriptive statistics for key variables. |
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For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males. |
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You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. |
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(the range must be found using the difference between the =max and =min functions with Fx) functions. |
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Note: Place data to the right, if you use Descriptive statistics, place that to the right as well. |
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Salary |
Compa |
Age |
Perf. Rat. |
Service |
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Overall |
Mean |
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Standard Deviation |
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Range |
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Female |
Mean |
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Standard Deviation |
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Range |
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Male |
Mean |
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Standard Deviation |
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Range |
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| <1 point> |
3 |
What is the probability for a: |
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Probability |
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a. Randomly selected person being a male in grade E? |
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b. Randomly selected male being in grade E? |
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Note part b is the same as given a male, what is probabilty of being in grade E? |
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c. Why are the results different? |
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| <1 point> |
4 |
For each group (overall, females, and males) find: |
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Overall |
Female |
Male |
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a. |
The value that cuts off the top 1/3 salary in each group. |
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Hint: can use these Fx functions |
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b. |
The z score for each value: |
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Excel’s standize function |
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c. |
The normal curve probability of exceeding this score: |
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1-normsdist function |
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d. |
What is the empirical probability of being at or exceeding this salary value? |
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e. |
The value that cuts off the top 1/3 compa in each group. |
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f. |
The z score for each value: |
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g. |
The normal curve probability of exceeding this score: |
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h. |
What is the empirical probability of being at or exceeding this compa value? |
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i. |
How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question? |
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| <2 points> |
5. |
What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? |
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What is the difference between the sal and compa measures of pay? |
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Conclusions from looking at salary results: |
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Conclusions from looking at compa results: |
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Do both salary measures show the same results? |
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Can we make any conclusions about equal pay for equal work yet? |
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