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
Gender Degree Performance Age
Gender 1 Grade Rating Salary
Degree Service
b. For each variable that you did not call ratio, why did you make that decision?
In my opinion the variables are not what
you would describe as a ratio, My reasoning
is because ratio data should be a positive
number and a number that reaches 0 ceases
to prevail as a ratio.
<1 point> 2 The first step in analyzing data sets is to find some summary descriptive statistics for key va
For salary, coma, age, performance rating, and service; find the mean, standard deviation, a
You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =std
(the range must be found using the difference between the =max and =min functions with F
Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.
Salary Compa
Overall Mean 45.000 1.062
Standard Deviation 19.201 0.077
Range 55.000 0.340
Female Mean 38.000 1.069
Standard Deviation 18.294 0.070
Range 55.000 0.254
Male Mean 52.000 1.056
Standard Deviation 17.776 0.084
Range 53.000 0.305
<1 point> 3 What is the probability for a:
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 i
c. Why are the results different?
The results are different because overall a sample is taken from the entire group of 50 peopl
and Female the fields are narrowed to 25 per person in each ratio.
<1 point> 4 For each group (overall, females, and males) find:
a. The value that cuts off the top 1/3 salary in each group.
b. The z score for each value:
c. The normal curve probability of exceeding this score:
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
Someone could interpret the relationship between the data sets as female workers having a l
compilng the numbers I would agree that female workers are not being payed equally to wh
<2 points> 5. What conclusions can you make about the issue of male and female pay equality? Are all o
What is the difference between the sal and compa measures of pay?
Based on the data given I see that males are receiving more salary compared to female work
rates, in some cases it does appear that both male and female workers are getting paid a high
that being said both have compa ratio greater than one.
Conclusions from looking at salary results:
In my opinion based on the data presented I can conclude that the range of all three groups a
are no major discrepancies amongst any of the groups which indicates a normal falsehood.
Conclusions from looking at compa results:
After reviewing the compa results once more, I can say that the overall, female workers, and
indicates that the workers salaries are being paid above the normal salary within the industry
Do both salary measures show the same results?
<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
Gender Degree Performance Age
Gender 1 Grade Rating Salary
Degree Service
b. For each variable that you did not call ratio, why did you make that decision?
In my opinion the variables are not what
you would describe as a ratio, My reasoning
is because ratio data should be a positive
number and a number that reaches 0 ceases
to prevail as a ratio.
<1 point> 2 The first step in analyzing data sets is to find some summary descriptive statistics for key va
For salary, coma, age, performance rating, and service; find the mean, standard deviation, a
You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =std
(the range must be found using the difference between the =max and =min functions with F
Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.
Salary Compa
Overall Mean 45.000 1.062
Standard Deviation 19.201 0.077
Range 55.000 0.340
Female Mean 38.000 1.069
Standard Deviation 18.294 0.070
Range 55.000 0.254
Male Mean 52.000 1.056
Standard Deviation 17.776 0.084
Range 53.000 0.305
<1 point> 3 What is the probability for a:
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 i
c. Why are the results different?
The results are different because overall a sample is taken from the entire group of 50 peopl
and Female the fields are narrowed to 25 per person in each ratio.
<1 point> 4 For each group (overall, females, and males) find:
a. The value that cuts off the top 1/3 salary in each group.
b. The z score for each value:
c. The normal curve probability of exceeding this score:
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
Someone could interpret the relationship between the data sets as female workers having a l
compilng the numbers I would agree that female workers are not being payed equally to wh
<2 points> 5. What conclusions can you make about the issue of male and female pay equality? Are all o
What is the difference between the sal and compa measures of pay?
Based on the data given I see that males are receiving more salary compared to female work
rates, in some cases it does appear that both male and female workers are getting paid a high
that being said both have compa ratio greater than one.
Conclusions from looking at salary results:
In my opinion based on the data presented I can conclude that the range of all three groups a
are no major discrepancies amongst any of the groups which indicates a normal falsehood.
Conclusions from looking at compa results:
After reviewing the compa results once more, I can say that the overall, female workers, and
indicates that the workers salaries are being paid above the normal salary within the industry
Do both salary measures show the same results?
