Score: Week 5 Correlation and RegressionCorrelation and RegressionCorrelation and Regression <1 point> 1. Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.) a. Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)? b. Place table here (C8):b. Place table here (C8):b. Place table here (C8): c. Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables areUsing r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables areUsing r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables areUsing r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables areUsing r = approxi.

1. Score: Week 5 Correlation and RegressionCorrelation and
RegressionCorrelation and Regression
<1 point> 1. Create a correlation table for the variables in our
data set. (Use analysis ToolPak or StatPlus:mac LE function
Correlation.)Create a correlation table for the variables in our
data set. (Use analysis ToolPak or StatPlus:mac LE function
Correlation.)Create a correlation table for the variables in our
data set. (Use analysis ToolPak or StatPlus:mac LE function
Correlation.)Create a correlation table for the variables in our
data set. (Use analysis ToolPak or StatPlus:mac LE function
Correlation.)Create a correlation table for the variables in our
data set. (Use analysis ToolPak or StatPlus:mac LE function
Correlation.)Create a correlation table for the variables in our
data set. (Use analysis ToolPak or StatPlus:mac LE function
Correlation.)Create a correlation table for the variables in our
data set. (Use analysis ToolPak or StatPlus:mac LE function
Correlation.)Create a correlation table for the variables in our
data set. (Use analysis ToolPak or StatPlus:mac LE function
Correlation.)Create a correlation table for the variables in our
data set. (Use analysis ToolPak or StatPlus:mac LE function
Correlation.)
a. Reviewing the data levels from week 1, what variables can be
used in a Pearson's Correlation table (which is what Excel
produces)?Reviewing the data levels from week 1, what
variables can be used in a Pearson's Correlation table (which is
what Excel produces)?Reviewing the data levels from week 1,
what variables can be used in a Pearson's Correlation table
(which is what Excel produces)?Reviewing the data levels from
week 1, what variables can be used in a Pearson's Correlation
table (which is what Excel produces)?Reviewing the data levels
from week 1, what variables can be used in a Pearson's

2. Correlation table (which is what Excel produces)?Reviewing the
data levels from week 1, what variables can be used in a
Pearson's Correlation table (which is what Excel
produces)?Reviewing the data levels from week 1, what
variables can be used in a Pearson's Correlation table (which is
what Excel produces)?Reviewing the data levels from week 1,
what variables can be used in a Pearson's Correlation table
(which is what Excel produces)?Reviewing the data levels from
week 1, what variables can be used in a Pearson's Correlation
table (which is what Excel produces)?
b. Place table here (C8):b. Place table here (C8):b. Place table
here (C8):
c. Using r = approximately .28 as the signicant r value (at p =
0.05) for a correlation between 50 values, what variables
areUsing r = approximately .28 as the signicant r value (at p =
0.05) for a correlation between 50 values, what variables
areUsing r = approximately .28 as the signicant r value (at p =
0.05) for a correlation between 50 values, what variables
areUsing r = approximately .28 as the signicant r value (at p =
0.05) for a correlation between 50 values, what variables
areUsing r = approximately .28 as the signicant r value (at p =
0.05) for a correlation between 50 values, what variables
areUsing r = approximately .28 as the signicant r value (at p =
0.05) for a correlation between 50 values, what variables
areUsing r = approximately .28 as the signicant r value (at p =
0.05) for a correlation between 50 values, what variables
areUsing r = approximately .28 as the signicant r value (at p =
0.05) for a correlation between 50 values, what variables are
significantly related to Salary?significantly related to
Salary?significantly related to Salary?
To compa?
d. Looking at the above correlations - both significant or not -
are there any surprises -by that I Looking at the above

3. correlations - both significant or not - are there any surprises -
by that I Looking at the above correlations - both significant or
not - are there any surprises -by that I Looking at the above
correlations - both significant or not - are there any surprises -
by that I Looking at the above correlations - both significant or
not - are there any surprises -by that I Looking at the above
correlations - both significant or not - are there any surprises -
by that I Looking at the above correlations - both significant or
not - are there any surprises -by that I
mean any relationships you expected to be meaningful and are
not and vice-versa?mean any relationships you expected to be
meaningful and are not and vice-versa?mean any relationships
you expected to be meaningful and are not and vice-versa?mean
any relationships you expected to be meaningful and are not and
vice-versa?mean any relationships you expected to be
meaningful and are not and vice-versa?mean any relationships
you expected to be meaningful and are not and vice-versa?
e. Does this help us answer our equal pay for equal work
question?Does this help us answer our equal pay for equal work
question?Does this help us answer our equal pay for equal work
question?Does this help us answer our equal pay for equal work
question?Does this help us answer our equal pay for equal work
question?
<1 point> 2 Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint,Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint,Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint,Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint,Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint,Below is a regression analysis for salary being

4. predicted/explained by the other variables in our sample
(Midpoint,Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint,Below is a regression analysis for salary being
predicted/explained by the other variables in our sample
(Midpoint,
age, performance rating, service, gender, and degree variables.
(Note: since salary and compa are different ways of age,
performance rating, service, gender, and degree variables.
(Note: since salary and compa are different ways of age,
performance rating, service, gender, and degree variables.
(Note: since salary and compa are different ways of age,
performance rating, service, gender, and degree variables.
(Note: since salary and compa are different ways of age,
performance rating, service, gender, and degree variables.
(Note: since salary and compa are different ways of age,
performance rating, service, gender, and degree variables.
(Note: since salary and compa are different ways of age,
performance rating, service, gender, and degree variables.
(Note: since salary and compa are different ways of age,
performance rating, service, gender, and degree variables.
(Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both
used in the same regression.) expressing an employee’s salary,
we do not want to have both used in the same regression.)
expressing an employee’s salary, we do not want to have both
used in the same regression.) expressing an employee’s salary,
we do not want to have both used in the same regression.)
expressing an employee’s salary, we do not want to have both
used in the same regression.) expressing an employee’s salary,
we do not want to have both used in the same regression.)
expressing an employee’s salary, we do not want to have both
used in the same regression.)
Plase interpret the findings.Plase interpret the findings.
Ho: The regression equation is not significant.Ho: The

5. regression equation is not significant.Ho: The regression
equation is not significant.Ho: The regression equation is not
significant.
Ha: The regression equation is significant.Ha: The regression
equation is significant.Ha: The regression equation is
significant.
Ho: The regression coefficient for each variable is not
significantHo: The regression coefficient for each variable is
not significantHo: The regression coefficient for each variable
is not significantHo: The regression coefficient for each
variable is not significantHo: The regression coefficient for
each variable is not significant Note: technically we have one
for each input variable. Note: technically we have one for each
input variable. Note: technically we have one for each input
variable. Note: technically we have one for each input variable.
Ha: The regression coefficient for each variable is
significantHa: The regression coefficient for each variable is
significantHa: The regression coefficient for each variable is
significantHa: The regression coefficient for each variable is
significantHa: The regression coefficient for each variable is
significant Listing it this way to save space. Listing it this
way to save space.
Sal
SUMMARY OUTPUTSUMMARY OUTPUT
Regression StatisticsRegression Statistics
Multiple R 0.99155907466
R Square 0.98318939853
Adjusted R SquareAdjusted R SquareAdjusted R Square
0.98084373321
Standard ErrorStandard Error 2.65759257261
ObservationsObservations 50

6. ANOVA
df SS MS F Significance F
Regression 6 17762.29967 2960.38328 419.1516111 1.81215E-
36
Residual 43 303.7003261 7.06279828
Total 49 18066
Coefficients
Standard
Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper
95.0%
Intercept -1.7496212123 3.618367658 -0.4835388 0.63116649 -
9.046755043 5.547512618 -9.04675504272 5.5475126180402
Midpoint 1.21670105053 0.031902351 38.1382881 8.66416E-35
1.1523638283 1.2810382727 1.1523638283163
1.2810382727441
Age -0.0046280102 0.065197212 -0.0709848 0.943738987 -
0.1361107191 0.1268546987 -0.1361107191429
0.1268546986526
Performace RatingPerformace RatingPerformace Rating -
0.0565964405 0.034495068 -1.6407111 0.108153182 -
0.126162375 0.0129694936 -0.1261623747113
0.0129694936118
Service -0.0425003573 0.084336982 -0.503935 0.616879352 -
0.212582091 0.1275813765 -0.212582091218 0.1275813765336
Gender 2.42033721201 0.860844318 2.81158528 0.007396619
0.684279192 4.156395232 0.6842791920166 4.156395232009
Degree 0.27553341432 0.799802305 0.3445019 0.732148119 -
1.337421655 1.8884884833 -1.337421654707 1.8884884833423
Note: since Gender and Degree are expressed as 0 and 1, they
are considered dummy variables and can be used in a multiple

7. regression equation.Note: since Gender and Degree are
expressed as 0 and 1, they are considered dummy variables and
can be used in a multiple regression equation.Note: since
Gender and Degree are expressed as 0 and 1, they are
considered dummy variables and can be used in a multiple
regression equation.Note: since Gender and Degree are
expressed as 0 and 1, they are considered dummy variables and
can be used in a multiple regression equation.Note: since
Gender and Degree are expressed as 0 and 1, they are
considered dummy variables and can be used in a multiple
regression equation.Note: since Gender and Degree are
expressed as 0 and 1, they are considered dummy variables and
can be used in a multiple regression equation.Note: since
Gender and Degree are expressed as 0 and 1, they are
considered dummy variables and can be used in a multiple
regression equation.Note: since Gender and Degree are
expressed as 0 and 1, they are considered dummy variables and
can be used in a multiple regression equation.Note: since
Gender and Degree are expressed as 0 and 1, they are
considered dummy variables and can be used in a multiple
regression equation.Note: since Gender and Degree are
expressed as 0 and 1, they are considered dummy variables and
can be used in a multiple regression equation.
Interpretation:Interpretation:
For the Regression as a whole:For the Regression as a
whole:For the Regression as a whole:
What is the value of the F statistic: What is the value of the F
statistic: What is the value of the F statistic:
What is the p-value associated with this value: What is the p-
value associated with this value: What is the p-value associated
with this value: What is the p-value associated with this value:
Is the p-value <0.05?Is the p-value <0.05?
Do you reject or not reject the null hypothesis: Do you reject or

8. not reject the null hypothesis: Do you reject or not reject the
null hypothesis: Do you reject or not reject the null hypothesis:
What does this decision mean for our equal pay question: What
does this decision mean for our equal pay question: What does
this decision mean for our equal pay question: What does this
decision mean for our equal pay question: What does this
decision mean for our equal pay question:
For each of the coefficients:For each of the coefficients:
Intercept Midpoint Age Perf. Rat. Service Gender Degree
What is the coefficient's p-value for each of the variables: What
is the coefficient's p-value for each of the variables: What is the
coefficient's p-value for each of the variables: What is the
coefficient's p-value for each of the variables: What is the
coefficient's p-value for each of the variables:
Is the p-value < 0.05?Is the p-value < 0.05?
Do you reject or not reject each null hypothesis: Do you reject
or not reject each null hypothesis: Do you reject or not reject
each null hypothesis: Do you reject or not reject each null
hypothesis:
What are the coefficients for the significant variables?What are
the coefficients for the significant variables?What are the
coefficients for the significant variables?What are the
coefficients for the significant variables?
Using only the significant variables, what is the equation?Using
only the significant variables, what is the equation?Using only
the significant variables, what is the equation?Using only the
significant variables, what is the equation?Using only the
significant variables, what is the equation? Salary =
Is gender a significant factor in salary:Is gender a significant
factor in salary:Is gender a significant factor in salary:
If so, who gets paid more with all other things being equal?If

9. so, who gets paid more with all other things being equal?If so,
who gets paid more with all other things being equal?If so, who
gets paid more with all other things being equal?If so, who gets
paid more with all other things being equal?
How do we know? How do we know?
<1 point> 3 Perform a regression analysis using compa as the
dependent variable and the same independentPerform a
regression analysis using compa as the dependent variable and
the same independentPerform a regression analysis using compa
as the dependent variable and the same independentPerform a
regression analysis using compa as the dependent variable and
the same independentPerform a regression analysis using compa
as the dependent variable and the same independentPerform a
regression analysis using compa as the dependent variable and
the same independentPerform a regression analysis using compa
as the dependent variable and the same independent
variables as used in question 2. Show the result, and interpret
your findings by answering the same questions.variables as used
in question 2. Show the result, and interpret your findings by
answering the same questions.variables as used in question 2.
Show the result, and interpret your findings by answering the
same questions.variables as used in question 2. Show the result,
and interpret your findings by answering the same
questions.variables as used in question 2. Show the result, and
interpret your findings by answering the same
questions.variables as used in question 2. Show the result, and
interpret your findings by answering the same
questions.variables as used in question 2. Show the result, and
interpret your findings by answering the same
questions.variables as used in question 2. Show the result, and
interpret your findings by answering the same questions.
Note: be sure to include the appropriate hypothesis
statements.Note: be sure to include the appropriate hypothesis
statements.Note: be sure to include the appropriate hypothesis

10. statements.Note: be sure to include the appropriate hypothesis
statements.Note: be sure to include the appropriate hypothesis
statements.
Regression hypothesesRegression hypotheses
Ho:
Ha:
Coefficient hyhpotheses (one to stand for all the separate
variables)Coefficient hyhpotheses (one to stand for all the
separate variables)Coefficient hyhpotheses (one to stand for all
the separate variables)Coefficient hyhpotheses (one to stand for
all the separate variables)Coefficient hyhpotheses (one to stand
for all the separate variables)
Ho:
Ha:
Place D94 in output box.Place D94 in output box.
Interpretation:Interpretation:
For the Regression as a whole:For the Regression as a
whole:For the Regression as a whole:
What is the value of the F statistic: What is the value of the F
statistic: What is the value of the F statistic:
What is the p-value associated with this value: What is the p-
value associated with this value: What is the p-value associated
with this value: What is the p-value associated with this value:
Is the p-value < 0.05?Is the p-value < 0.05?
Do you reject or not reject the null hypothesis: Do you reject or
not reject the null hypothesis: Do you reject or not reject the
null hypothesis: Do you reject or not reject the null hypothesis:
What does this decision mean for our equal pay question: What
does this decision mean for our equal pay question: What does
this decision mean for our equal pay question: What does this
decision mean for our equal pay question: What does this

11. decision mean for our equal pay question:
For each of the coefficients: For each of the coefficients:
Intercept Midpoint Age Perf. Rat. Service Gender Degree
What is the coefficient's p-value for each of the variables: What
is the coefficient's p-value for each of the variables: What is the
coefficient's p-value for each of the variables: What is the
coefficient's p-value for each of the variables: What is the
coefficient's p-value for each of the variables:
Is the p-value < 0.05?Is the p-value < 0.05?
Do you reject or not reject each null hypothesis: Do you reject
or not reject each null hypothesis: Do you reject or not reject
each null hypothesis: Do you reject or not reject each null
hypothesis:
What are the coefficients for the significant variables?What are
the coefficients for the significant variables?What are the
coefficients for the significant variables?What are the
coefficients for the significant variables?
Using only the significant variables, what is the equation?Using
only the significant variables, what is the equation?Using only
the significant variables, what is the equation?Using only the
significant variables, what is the equation?Using only the
significant variables, what is the equation? Compa =
Is gender a significant factor in compa:Is gender a significant
factor in compa:Is gender a significant factor in compa:
If so, who gets paid more with all other things being equal?If
so, who gets paid more with all other things being equal?If so,
who gets paid more with all other things being equal?If so, who
gets paid more with all other things being equal?If so, who gets
paid more with all other things being equal?
How do we know? How do we know?

12. <1 point> 4 Based on all of your results to date, Based on all of
your results to date, Based on all of your results to date,
Do we have an answer to the question of are males and females
paid equally for equal work?Do we have an answer to the
question of are males and females paid equally for equal
work?Do we have an answer to the question of are males and
females paid equally for equal work?Do we have an answer to
the question of are males and females paid equally for equal
work?Do we have an answer to the question of are males and
females paid equally for equal work?Do we have an answer to
the question of are males and females paid equally for equal
work?Do we have an answer to the question of are males and
females paid equally for equal work?
If so, which gender gets paid more? If so, which gender gets
paid more? If so, which gender gets paid more?
How do we know? How do we know?
Which is the best variable to use in analyzing pay practices -
salary or compa? Why?Which is the best variable to use in
analyzing pay practices - salary or compa? Why?Which is the
best variable to use in analyzing pay practices - salary or
compa? Why?Which is the best variable to use in analyzing pay
practices - salary or compa? Why?Which is the best variable to
use in analyzing pay practices - salary or compa? Why?Which
is the best variable to use in analyzing pay practices - salary or
compa? Why?
What is most interesting or surprising about the results we got
doing the analysis during the last 5 weeks?What is most
interesting or surprising about the results we got doing the
analysis during the last 5 weeks?What is most interesting or
surprising about the results we got doing the analysis during the
last 5 weeks?What is most interesting or surprising about the
results we got doing the analysis during the last 5 weeks?What
is most interesting or surprising about the results we got doing
the analysis during the last 5 weeks?What is most interesting or

13. surprising about the results we got doing the analysis during the
last 5 weeks?What is most interesting or surprising about the
results we got doing the analysis during the last 5 weeks?What
is most interesting or surprising about the results we got doing
the analysis during the last 5 weeks?
<2 points> 5 Why did the single factor tests and analysis (such
as t and single factor ANOVA tests on salary equality) not
provide a complete answer to our salary equality question?Why
did the single factor tests and analysis (such as t and single
factor ANOVA tests on salary equality) not provide a complete
answer to our salary equality question?Why did the single factor
tests and analysis (such as t and single factor ANOVA tests on
salary equality) not provide a complete answer to our salary
equality question?Why did the single factor tests and analysis
(such as t and single factor ANOVA tests on salary equality) not
provide a complete answer to our salary equality question?Why
did the single factor tests and analysis (such as t and single
factor ANOVA tests on salary equality) not provide a complete
answer to our salary equality question?Why did the single factor
tests and analysis (such as t and single factor ANOVA tests on
salary equality) not provide a complete answer to our salary
equality question?Why did the single factor tests and analysis
(such as t and single factor ANOVA tests on salary equality) not
provide a complete answer to our salary equality question?Why
did the single factor tests and analysis (such as t and single
factor ANOVA tests on salary equality) not provide a complete
answer to our salary equality question?Why did the single factor
tests and analysis (such as t and single factor ANOVA tests on
salary equality) not provide a complete answer to our salary
equality question?Why did the single factor tests and analysis
(such as t and single factor ANOVA tests on salary equality) not
provide a complete answer to our salary equality question?Why
did the single factor tests and analysis (such as t and single
factor ANOVA tests on salary equality) not provide a complete
answer to our salary equality question?

14. What outcomes in your life or work might benefit from a
multiple regression examination rather than a simpler one
variable test?What outcomes in your life or work might benefit
from a multiple regression examination rather than a simpler
one variable test?What outcomes in your life or work might
benefit from a multiple regression examination rather than a
simpler one variable test?What outcomes in your life or work
might benefit from a multiple regression examination rather
than a simpler one variable test?What outcomes in your life or
work might benefit from a multiple regression examination
rather than a simpler one variable test?What outcomes in your
life or work might benefit from a multiple regression
examination rather than a simpler one variable test?What
outcomes in your life or work might benefit from a multiple
regression examination rather than a simpler one variable
test?What outcomes in your life or work might benefit from a
multiple regression examination rather than a simpler one
variable test?What outcomes in your life or work might benefit
from a multiple regression examination rather than a simpler
one variable test?
ID Salary Compa Midpoint Age Performance
Rating
Service Gender Raise Degree Gender1 Gr
1 65.5 1.149 57 34 85 8 0 5.7 0 M E The ongoing question that
the weekly assignments will focus on is: Are males and females
paid the same for equal work (under the Equal Pay Act)?

15. 2 27.8 0.897 31 52 80 7 0 3.9 0 M B Note: to simplfy the
analysis, we will assume that jobs within each grade comprise
equal work.
3 36.4 1.173 31 30 75 5 1 3.6 1 F B
4 56.3 0.988 57 42 100 16 0 5.5 1 M E The column labels in
the table mean:
5 47.9 0.998 48 36 90 16 0 5.7 1 M D ID – Employee sample
number Salary – Salary in thousands
6 74.8 1.117 67 36 70 12 0 4.5 1 M F Age – Age in years
Performance Rating - Appraisal rating (employee evaluation
score)
7 41.5 1.037 40 32 100 8 1 5.7 1 F C Service – Years of
service (rounded) Gender – 0 = male, 1 = female
8 24 1.044 23 32 90 9 1 5.8 1 F A Midpoint – salary grade
midpoint Raise – percent of last raise
9 77.1 1.151 67 49 100 10 0 4 1 M F Grade – job/pay grade
Degree (0= BSBA 1 = MS)
10 23.4 1.016 23 30 80 7 1 4.7 1 F A Gender1 (Male or
Female) Compa - salary divided by midpoint
11 23 1.000 23 41 100 19 1 4.8 1 F A
12 62.2 1.091 57 52 95 22 0 4.5 0 M E
13 41.4 1.034 40 30 100 2 1 4.7 0 F C
14 23.7 1.029 23 32 90 12 1 6 1 F A
15 22.7 0.985 23 32 80 8 1 4.9 1 F A
16 37 0.926 40 44 90 4 0 5.7 0 M C
17 69.3 1.216 57 27 55 3 1 3 1 F E
18 34.8 1.122 31 31 80 11 1 5.6 0 F B
19 24 1.045 23 32 85 1 0 4.6 1 M A
20 33.3 1.075 31 44 70 16 1 4.8 0 F B
21 75.3 1.124 67 43 95 13 0 6.3 1 M F
22 43.7 0.910 48 48 65 6 1 3.8 1 F D
23 22.5 0.977 23 36 65 6 1 3.3 0 F A
24 52.8 1.100 48 30 75 9 1 3.8 0 F D
25 23.7 1.032 23 41 70 4 0 4 0 M A
26 22.9 0.994 23 22 95 2 1 6.2 0 F A

16. 27 40.5 1.012 40 35 80 7 0 3.9 1 M C
28 76.4 1.141 67 44 95 9 1 4.4 0 F F
29 78.6 1.173 67 52 95 5 0 5.4 0 M F
30 47.6 0.992 48 45 90 18 0 4.3 0 M D
31 21.8 0.947 23 29 60 4 1 3.9 1 F A
32 28 0.904 31 25 95 4 0 5.6 0 M B
33 63.8 1.120 57 35 90 9 0 5.5 1 M E
34 28 0.903 31 26 80 2 0 4.9 1 M B
35 23.9 1.039 23 23 90 4 1 5.3 0 F A
36 22.2 0.966 23 27 75 3 1 4.3 0 F A
Sheet1Data