| 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 - | | | | | | | | | | | The column labels in the table mean: |
| | | nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, as | | | | | | | | | | | ID – Employee sample number | | | Salary – Salary in thousands |
| | | this impact the kind of analysis we can do with the data. For example, descriptive statistics | | | | | | | | | | | Age – Age in years | | | Performance Rating - Appraisal rating (employee evaluation score) |
| | | such as means can only be done on interval or ratio level data. | | | | | | | | | | | Service – Years of service (rounded) | | | Gender – 0 = male, 1 = female |
| | | Please list under each label, the variables in our data set that belong in each group. | | | | | | | | | | | Midpoint – salary grade midpoint | | | Raise – percent of last raise |
| | | Nominal | Ordinal | Interval | Ratio | | | | | | | | Grade – job/pay grade | | | Degree (0= BS\BA 1 = MS) |
| | | Gender1 | ID | Raise | Performance rating-Appraisal rating (employee evaluation score) | | | | | | | | Gender1 (Male or Female) | | | Compa - salary divided by midpoint |
| | | | Degree | Midpoint |
| | | | Salary |
| | Norminal are qualitative and are categorized using names, labels or qualities. Ordinal can be qualitative or quantitative and the data are arranged in order or ranked. |
| | Interval is ordered and meaningful differences between data entries can be calculated. Ratio of two data values can be formed so that one data value can be meaningfully expressed as a multiple of another. |
| | 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 | 44.7 | 1.0553 | 35.7 | 85.9 | 9.0 |
| | | | Standard Deviation | 19.4507 | 0.0856 | 8.2513 | 11.4147 | 5.7117 |
| | | | Range | 59.2 | 0.396 | 30 | 45 | 21 |
| | | Female | Mean | 37.2 | 1.0532 | 32.5 | 84.2 | 7.4 |
| | | | Standard Deviation | 17.544 | 0.0809 | 6.881 | 13.592 | 4.321 |
| | | | Range | 53.7 | 0.31899 | 26 | 45 | 17 |
| | | Male | Mean | 52.3 | 1.0574 | 38.9 | 87.6 | 10.0 |
| | | | Standard Deviation | 18.615 | 0.092 | 8.386 | 8.675 | 6.357 |
| | | | Range | 57.9 | 0.374 | 28 | 30 | 21 |
| <1 point> | 3 | What is the probability for a: | | | | | | | Probability |
| | | a. Randomly selected person being a male in grade E? | | | | | | | 10 out of 50 |
| | | b. Randomly selected male being in grade E? | | | | | | | 10 out of 25 |
| | | | Note part b is the same as given a male, what is probabilty of being in grade E? |
| | | c. Why are the results different? |
| | | In part a there are only 10 changes to select a person with a male in grade E out of the entire |
| | | population of 50, however in part a out of the entire samples of 25 males there are only 10 chances. | | | | | | | | | | with E |
| <1 point> | 4 | For each group (overall, females, and males) find: | | | | | | | | Overall | Female | Male | | | | I can do all things through Christ that strengthen me. |
| For the overall Salary Column: | | | | Variance(population st. dev) 0.00718 | | | | | | 1.05528 | 1.04668 | 1.0574 |
| | Mean is 1.05528 | | | Standard deviation: 0.08559 | | | | | | | For the females I added up all the females in the Salary colum then divide it by the number of females. |
| Population standard deviation: 0.08473 | | | | | variance(St.dev)0.00733 | | | | | | | For the males I added all the number of males in the Salary colum then divided the the total number of males. |
| | 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? |
Alana22
alana22week_1_data_questions.xlsx
