Score: Week 1. Measurement and Description - chapters 1 and 2 <1 point> 1 <1 point> 2 <1 point> 3 <1 point> <2 points> 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 Gender Degree Performance Rating Salary Gender1 Grade Compa ID Age midpoint Service raise b. For each variable that you did not call ratio, why did you make that decision? Only items that where an absolute zero doesn't exist are consider ratio variables. I am also unable to find a mean in the 5 items, which is another identifer. 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 45.0 1.0625 35.7 85.9 9.0 Standard Deviation 19.2014 0.0768 8.2513 11.4147 5.7177 Range 55 0.34 30 45 21 Female Mean 38.0 1.0687 32.5 84.2 7.9 Standard Deviation 18.2938969787 0.0703 6.881 13.592 4.907 Range 55 0.254 26 45 18 Male Mean 52.0 1.0562 38.9 87.6 10.0 Standard Deviation 17.776 0.084 8.386 8.675 6.357 Range 53 0.305 28 30 21 What is the probability for a: Probability a. Randomly selected person being a male in grade E? 0.2 b. Randomly selected male being in grade E? 0.4 Note part b is the same as given a male, what is probabilty of being in grade E? c. Why are the results different? The first question pertains to any person, regardless of gender. While the second questions applies to only the male data set. 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. 60 42 64 Hint: can u b. The z score for each value: 0.7811929 0.218652 0.67505274 Excel's sta c. The normal curve probability of exceeding this score: 0.2173445 0.413461 0.24982113 1-normsdis d. What is the empirical probability of being at or exceeding this salary value? 0.32 0.32 0.32 e. The value that cuts off the top 1/3 compa in each group. 1.119 1.119 1.122 f. The z score for each value: 0.7356973 0.714766 0.78482799 g. The normal curve probability of exceeding this score: 0.2309575 0.237377 0.21627721 h. What is the empirical probability of being at or exceeding this compa value? 0.32 0.32 0.32 i. How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question? Not only do we see a pay range descripency in this data between women and men, we also see that there is more variation in women's salary sa 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? Males appear to be receiving more salary as compared to females. However you can also note that both genders are receicing more salary as compared to the industry standard. You can see that each has a coma ratio greater than 1 Salary is an actual absolute data point, of payment received by employees. the coma measure is just comparing the industry standard to what the employees are recieving. Conclusions from looking at salary results: If we look back to the analysis of question 2 we see that the overall employee mean salary is 45, with a standard deviation of 19.2014. Breaking that down further we found that females have mean of 38, with a stanadard deviation of 18.29. This leaves men with the mean of 52, and lower standard deviation of 17.776. This to me says that men are recieving more salary, however the women's salary have more deviations amoung them. Conclusions from looking at compa results: For all groups we see that we have a salary over the industrial norm. Not only overall do we see this with the 1.06, we see it with women at 1.06 and men in 1.05. Which means that women are making slighty more then men when compared to the industrial average within this company, despite still making less then men. Do both salary measures show the same results? No, they sure don't. We can see that there are different results. Can we make any conclusions about equal pay for equal work yet? We can certainly see that men are making more pay then women in this company. nd Males. use these Fx functions andize function ist function alaries