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 .

1. Score:
Week 1.
Measurement and Description - chapters 1 and 2

5. <1 point>
1
Measurement issues.
Data, even numerically coded variables, can be one of 4 levels -

7. nominal, ordinal, interval, or ratio.
It is important to identify which level a variable is, as

8. this impact the kind of analysis we can do with the data.
For example, descriptive statistics

10. such as means can only be done on interval or ratio level data.

11. Please list under each label, the variables in our data set that
belong in each group.

13. Nominal
Ordinal
Interval
Ratio

24. b.
For each variable that you did not call ratio, why did you make
that decision?

33. <1 point>
2
The first step in analyzing data sets is to find some summary
descriptive statistics for key variables.

35. For salary, compa, age, performance rating, and service; find
the mean, standard deviation, and range for 3 groups: overall
sample, Females, and Males.

36. You can use either the Data Analysis Descriptive Statistics tool
or the Fx =average and =stdev functions.

38. (the range must be found using the difference between the =max
and =min functions with Fx) functions.

39. Note: Place data to the right, if you use Descriptive statistics,
place that to the right as well.

40. Salary
Compa
Age
Perf. Rat.
Service

42. Overall
Mean

44. Standard Deviation

45. Range

47. Female
Mean

48. Standard Deviation

50. Range

52. Male
Mean

53. Standard Deviation

55. Range

58. <1 point>
3
What is the probability for a:
Probability

60. a. Randomly selected person being a male in grade E?

61. b. Randomly selected male being in grade E?

63. Note part b is the same as given a male, what is probabilty of
being in grade E?

64. c.
Why are the results different?

67. <1 point>
4
For each group (overall, females, and males) find:
Overall
Female
Male

69. a.
The value that cuts off the top 1/3 salary in each group.
Hint: can use these Fx functions

70. b.
The z score for each value:
Excel's standize function

72. c.
The normal curve probability of exceeding this score:
1-normsdist function

73. d.
What is the empirical probability of being at or exceeding this
salary value?

74. e.
The value that cuts off the top 1/3 compa in each group.

76. f.
The z score for each value:

77. g.

78. The normal curve probability of exceeding this score:

79. h.
What is the empirical probability of being at or exceeding this
compa value?

80. i.
How do you interpret the relationship between the data sets?
What do they mean about our equal pay for equal work

81. question?

87. <2 points>
5.
What conclusions can you make about the issue of male and
female pay equality?
Are all of the results consistent?

88. What is the difference between the sal and compa measures of
pay?

93. Conclusions from looking at salary results:

98. Conclusions from looking at compa results:

102. Do both salary measures show the same results?

107. Can we make any conclusions about equal pay for equal work
yet?