Score: <1 point> Week 1. Measurement and Description - chapters 1 and 2 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, an 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 <2 points> 5. 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 f b. The z score for each value: Excel's standize functio 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? 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? s, and Males. Fx functions ction n