| Score: | Week 1. | Measurement and Description - chapters 1 and 2 |
| <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? |
rude-1
w1_-_assignment_sheet.xlsx
