Excel Practice 2 Alexa Mancillas ECON 261-1001 Professor Assané Spring 2013 Recently, we students have been urged to obtain college degrees and gain experience in our fields of study to ensure that we get well-paying jobs. But how exactly do those details affect the wages we are paid? Do other variables have an effect on our wages? With the data provided, we will analyze the descriptive statistics of each variable—Hourly Wage (H. wage), Education, Experience, Female, and Union. We will calculate the mean, standard deviation, minimum, and maximum values of each variable; calculate the correlation coefficient between H. wage, Education, and Experience; and graph the relationships between H. wage and Education, and between H. wage and Experience. Next, for male and female workers, we will calculate the mean and standard deviation for H. wage, Education, Experience, and Union. We will analyze the average difference for each variable. Finally, for union and non-union workers, we will calculate mean and standard deviation for H. wage, Education, Experience, and Female. We will then analyze the average differences for each variable. Table 1: Summary Statistics for All Variables According to Table 1, the average hourly wage received by workers is $9.01 with a standard deviation of $4.90. The minimum and maximum hourly wages received by workers are $2.01 and $26.29 respectively. The average years of education a worker has received is 13.09 years with a standard deviation of 2.50 years. The minimum and maximum years of education received by workers are 6.00 and 18.00 years respectively. The average experience of workers is 17.75 years with a standard deviation of 12.14 years. The minimum and maximum years of experience workers have are 0.00 years and 49.00 years respectively. Approximately 46%, or 243, of these workers are female, and approximately 18%, or 97, are union members. Table 2: Correlation Coefficients Table 2 shows us that the correlation coefficient between H. wage and Education is 0.41, indicating a positive but weak relationship between the two variables. The correlation coefficient of 0.11 indicates a positive but weak relationship between H. wage and Experience as well. The correlation coefficient between Education and Experience is -0.32. This indicates a weak, negative relationship between the two variables. Figure 1: Scatter plot illustrating relationship between H. wage and Education Variables Mean Standard Deviation Minimum Maximum H. wage 9.01 4.90 2.01 26.29 Education 13.09 2.50 6.00 18.00 Experience 17.75 12.14 0.00 49.00 Female 0.46 0.50 0.00 1.00 Union 0.18 0.39 0.00 1.00 Variables H. wage Education Experience H. wage 1 Education 0.41 1 Experience 0.11 -0.32 1 Although the scatter plot in Figure 1 illustrates a weak relationship between the two variables, the trendline i.

1. Excel Practice 2
Alexa Mancillas
ECON 261-1001
Professor Assané
Spring 2013
Recently, we students have been urged to obtain college
degrees and gain
experience in our fields of study to ensure that we get well-
paying jobs. But how
exactly do those details affect the wages we are paid? Do other
variables have
an effect on our wages? With the data provided, we will analyze

2. the descriptive
statistics of each variable—Hourly Wage (H. wage), Education,
Experience,
Female, and Union. We will calculate the mean, standard
deviation, minimum,
and maximum values of each variable; calculate the correlation
coefficient
between H. wage, Education, and Experience; and graph the
relationships
between H. wage and Education, and between H. wage and
Experience. Next,
for male and female workers, we will calculate the mean and
standard
deviation for H. wage, Education, Experience, and Union. We
will analyze the
average difference for each variable. Finally, for union and non-
union workers,
we will calculate mean and standard deviation for H. wage,
Education,
Experience, and Female. We will then analyze the average
differences for each
variable.
Table 1: Summary Statistics for All Variables

3. According to
Table 1, the
average hourly
wage received
by workers is
$9.01 with a
standard
deviation of
$4.90. The minimum and maximum hourly wages received by
workers are $2.01
and $26.29 respectively. The average years of education a
worker has received
is 13.09 years with a standard deviation of 2.50 years. The
minimum and
maximum years of education received by workers are 6.00 and
18.00 years
respectively. The average experience of workers is 17.75 years
with a standard
deviation of 12.14 years. The minimum and maximum years of
experience
workers have are 0.00 years and 49.00 years respectively.

4. Approximately 46%, or
243, of these workers are female, and approximately 18%, or
97, are union
members.
Table 2: Correlation Coefficients
Table 2 shows us that the
correlation coefficient
between H. wage and
Education is 0.41, indicating a
positive but weak relationship
between the two variables. The correlation coefficient of 0.11
indicates a
positive but weak relationship between H. wage and Experience
as well. The
correlation coefficient between Education and Experience is -
0.32. This indicates
a weak, negative relationship between the two variables.
Figure 1: Scatter plot illustrating relationship between H. wage
and Education
Variables Mean

5. Standard
Deviation
Minimum Maximum
H. wage 9.01 4.90 2.01 26.29
Education 13.09 2.50 6.00 18.00
Experience 17.75 12.14 0.00 49.00
Female 0.46 0.50 0.00 1.00
Union 0.18 0.39 0.00 1.00
Variables H. wage Education Experience
H. wage 1
Education 0.41 1
Experience 0.11 -0.32 1
Although the scatter
plot in Figure 1 illustrates
a weak relationship
between the two
variables, the trendline

6. is consistent with the
intuition that people
with more years of
education tend to be
paid higher hourly
wages.
Figure 2: Scatter plot illustrating relationship between H. wage
and Experience
The scatter plot in
Figure 2 is consistent
with the intuition that
people with more
experience in their field
of work tend to receive
higher hourly wages;
although, this

7. relationship is weak.
Table 3: Summary Statistics for Male and Female Workers
Variables
Male Workers Female Workers
Average
Difference Mean
Standard
Deviation
Mean
Standard
Deviation
H. wage 10.08 5.27 7.74 4.10 2.34
Education 13.16 2.55 13.01 2.44 0.14
In Table 3, it is

8. shown that the
average hourly
wage of male workers is $10.08 with a standard deviation of
$5.27. Female
workers are shown to have an average hourly wage of $7.74
with a standard
deviation of $4.10. The average years of education male
workers have received
is 13.16 years with a standard deviation of 2.55 years. Female
workers have
received an average of 13.01 years of education with a standard
deviation of
2.44 years. Male workers have an average of 16.64 years of
experience with a
standard deviation of 11.69 years, while female workers have an
average of
19.04 years of experience with a standard deviation of 12.55
years. 24% of male
workers and 12% of female workers are shown to be union
members.
From Table 3, we can conclude that male workers receive
approximately $2.34
more in hourly wages than female workers make. This could be

9. because of
gender discrimination in the workplace. It could also be
because, physically,
men tend to be stronger than women; therefore, they are able to
do more
arduous tasks.
Table 4: Summary Statistics for Union and Non-Union Workers
According
to Table 4,
the average
hourly wage
received by
union
workers is
$10.80 with
a standard deviation of $4.56. Non-union workers receive an
average of $8.60
per hour with a standard deviation of $4.89. Union workers have
an average of
12.89 years of education with a standard deviation of 2.64

10. years. Non-union
workers are shown to have an average of 13.14 years of
education with a
standard deviation of 2.46 years. In years of experience, union
workers have an
average of 20.94 years with a standard deviation of 12.59 years,
whereas non-
union workers have an average of 17.03 years with a standard
deviation of 11.93
years. Table 4 also shows that 29% of union workers and 50%
of non-union
workers are female.
From Table 4, we can conclude that, on average, union workers
make $2.19
more in hourly wages than non-union workers make. This is
mainly due to the
fact that union workers are able to negotiate their wages as part
of their
contract.
Experience 16.64 11.69 19.04 12.55 -2.40
Union 0.24 0.43 0.12 0.32 0.12

11. Variables
Union Workers Non-Union Workers
Average
Difference Mean
Standard
Deviation
Mean
Standard
Deviation
H. wage 10.80 4.56 8.60 4.89 2.19
Education 12.89 2.64 13.14 2.46 -.025
Experience 20.94 12.59 17.03 11.93 3.90
Female 0.29 0.46 0.50 0.50 -.021
Overall, we can conclude that factors such as education,
experience, gender,
and union membership have an influence on the hourly wages a
worker is paid;
although, it is shown to be a weak influence.
Excel Practice I

12. Cindy Smith
ECON 261-1001
Professor Assané
Fall 2018
Acknowledgements:
To my family…
For tolerating all the time I spend studying while finishing my
degree;
And for supporting the time and effort that goes into higher
education while encouraging me to do my best.
Distance of Fire Stations and Fire Damage
Introduction
Residential fires can cause varying amounts of damage,
resulting in devastating loss in some cases. Of the many
variables to consider when determining the ability to extinguish
the fire, one factor is the distance of the fire station in relation
to the location of the burning residence. A fire insurance
company collected data to show the relationship between fire
station locations and the amount of damage caused by a fire. A
hypothesis was drawn to answer the question of how relevant
the distance between a fire station and a fire are in correlation
to reduce the amount of damages sustained. Data from recent
fires were used to create the following tables depicting the
mean, standard deviation, and the minimum and maximum
variables used to test and validate the hypothesis.

13. Descriptive Statistics
Table 1: Descriptive Statistics for Fire Damages
Variable
Mean
Standard Deviation
Minimum
Maximum
Miles
3.27
1.57
0.7
6.1
Damage (Thousands in U.S. Currency)
26.41
8.07
14.1
43.2
The table shows a sample data set from fifteen fires, with the
mean (average) distance of a fire station from a fire being 3.27
miles, with a standard deviation of 1.57 miles. The amount of
damages in dollars (thousands) in relation to the mean was
$26.41, and $8.07 in relation to the standard deviation. At a
minimum distance of 0.7 miles, the damages totaled $14.1
thousand dollars. The maximum distance was 6.1 miles, with the
damages totaling $43.2 thousand dollars. The statistics show a
relationship between the distance of fire stations and the amount
of damages incurred.
Correlation Coefficient
Table 2: Data Correlation Coefficient
Miles

14. Damage
Miles
1
Damage
0.95944171
1
The data from the table shows the correlation between the
distance of the fire stations and the fires. The correlation
coefficient confirms that there is a direct relationship between
the distance of fire stations and damages sustained. The further
a fire station is from a fire, the greater the damages are as
illustrated by the strong relationship of miles at 1, and damages
at 0.96. The same is said for the opposite, being that the closer
the fire station is to the fire, the less the damages are.
Scatterplot Data
Figure 1: Correlation Coefficient Scatterplot
Figure 1 illustrates the high correlation coefficient between the
distance of a fire station from a fire. As you can see from the
points on the scatterplot, there is a positively strong
relationship between the two variables.
Conclusion
In conclusion, the statistical data confirms the hypothesis that
there is indeed a strong relationship for the distance between a
fire station and the amount of damages sustained due to fire.
The further away a fire station location is from the fire, the
greater the damages are. To reduce fire damage in residential
areas where fire stations are located more than a mile away
from populated residential areas, it would be beneficial to build
additional fire stations.

15. Grade Point Averages and Law School Admissions Tests
Introduction
It is well known that to get into college, one must have a grade
point average score (GPA) over a certain number, with the most
common average being 2.5. Upon completing an undergraduate
degree, many students choose to continue their education in a
graduate program such as Law School. A hypothesis was formed
to answer the question of whether or not student’s GPA scores
would be a good indicator of how well they would perform on
the Law School Admissions Test (LSAT). To answer this
question a study of 82 undergraduate students entering into the
class of 2000 was conducted to see if there is a correlation
between student’s GPA Scores and LSAT Scores. Data from the
study was used to create the following tables depicting the
mean, standard deviation, and the minimum and maximum
variables used to test and validate the hypothesis.
Descriptive Statistics
Table 3: Descriptive Statistics for LSAT and GPA Scores
Variable
Mean
Standard Deviation
Minimum
Maximum
GPA Score
3.13
0.19
2.57
3.5
LSAT Score
597.59
38.49
477
704

16. The data for students entering Law School in Table 3 shows the
mean for GPA scores to be 3.13, while the mean for LSAT
scores is 597.59. The method for calculating GPA scores is not
the same as it is for LSAT scores, but the perspective of either
is the same, being the higher the score the better the students’
performance. The standard deviation for GPA is 0.19, and the
for the LSAT it is 38.49. The minimum score for GPA is 2.57,
with a maximum of 3.5. The minimum Score for the LSAT is
477, with a maximum of 704. To illustrate the similarity in the
two scores we can use the percentage difference in the scores
ranges. The percentage difference between the minimum and
maximum GPA score is 73%, and for the difference in the LSAT
scores it is 68%.
Correlation Coefficient
Table 4: Data Correlation Coefficient
GPA
LSAT
GPA
1
LSAT
0.759892
1
The data in Table 4 illustrates the relationship between GPA
and LSAT scores. The correlation coefficient between GPA and
LSAT scores comes in at 0.76, which shows a moderately high
level of correlation between the scores. The correlation shows
that students with higher GPA Scores also score higher on the
LSAT, indicating the hypothesis is correct.

17. Scatterplot Data
Figure 2: Correlation Coefficient Scatterplot
The data shown in Figure 2 shows the relationship between GPA
and LSAT Scores. The scatterplot shows the strong correlation
of students who have high GPA scores and their ability to do
well on the LSAT.
Conclusion
In conclusion, the results of the statistical data show a strong
relationship in a student with a high GPA score and their ability
to score high on the LSAT. A high GPA score is a strong
indicator of a student’s ability to do well when furthering their
education by taking the LSAT to enter into Law School.
Distance and Damages
Damage
3.4 1.8 4.5999999999999996 2.2999999999999998
3.1 5.5 0.7 3 2.6 4.3 2.1 1.1000000000000001
6.1 4.7 3.8 26.2 17.8 31.3 23.1 27.5 36 14.1 22.3
19.600000000000001 31.3 24 17.3 43.2 36.4 26.1
Distance from Fire Station
(Miles)
Damages
(Thousands of Dollard)
Law School Admissions

18. LSAT 3.23 2.83 3.24 3.12 3.09 3.39 3.1 3.4 2.97 2.91
3.11 3.24 3.3 3.22 3.43 2.91 3.23 3.47 3.15 3.33 2.99
3.19 3.03 3.01 3.39 3.41 3.04 3.29 3.16 3.17 3.13 3.36
2.57 3.02 3.03 2.88 3.37 3.2 3.23 2.98 3.11 2.92 2.85
3.38 2.76 3.27 3.36 3.19 3.17 3 3.11 3.07 2.96 3.05
2.93 3.28 3.01 3.21 3.32 3.24 3.03 3.33 3.08 3.13 3.01
3.3 3.15 2.82 3.2 3.44 3.01 2.92 3.45 3.15 3.5 3.16
3.19 3.15 2.81 3.16 3.02 2.74 622 542 579 653 606
576 620 615 553 607 558 596 635 581 661 547
599 646 622 611 546 614 628 575 662 627 608
632 587 581 605 704 477 591 578 572 615 606
603 535 598 575 573 644 545 645 651 562 609
555 586 580 594 594 560 641 512 631 597 621
617 637 572 610 562 635 614 546 598 666 570
570 605 565 686 608 595 590 558 611 564 575
GPA
LSAT Scores
Sheet1GPALSAT3.236222.835423.245793.126533.096063.3957
63.16203.46152.975532.916073.115583.245963.36353.225813.4
36612.915473.235993.476463.156223.336112.995463.196143.0
36283.015753.396623.416273.046083.296323.165873.175813.1
36053.367042.574773.025913.035782.885723.376153.26063.23
6032.985353.115982.925752.855733.386442.765453.276453.36
6513.195623.1760935553.115863.075802.965943.055942.93560
3.286413.015123.216313.325973.246213.036173.336373.08572
3.136103.015623.36353.156142.825463.25983.446663.015702.9
25703.456053.155653.56863.166083.195953.155902.815583.16
6113.025642.74575
Sheet1MilesDamage3.426.21.817.84.631.32.323.13.127.55.5360

19. .714.1322.32.619.64.331.32.1241.117.36.143.24.736.43.826.1
Sheet2
Sheet3
Econ 261-1
Djeto Assané
Guide to Excel Practice
Here is your guide to Excel Practice I. I will take the time to go
over the guide in class.
1. The front page of the project should contain
1. The title of the study
1. Your name(s)
1. Econ 261-1
1. Fall 2017
1. Acknowledgment: Thank those who help you
Section 1: Introduction
The introductory section emphasizes the purpose of the study,
motivation in selecting the topic and conclusion that announces
the organization of the rest of the paper.
Section 2: Descriptive statistics
Section 2 analyzes descriptive statistics. Construct a table that
contains the mean, standard deviation, minimum, and maximum
of the data used. Interpret your finding.
Section 3: Correlation matrix
Section 3 analyzes the relationship between the two variables
used. Construct a matrix of correlation coefficient and interpret
your finding.
Section 4: Scatter plots
Section 4 pictures the relationship between the two variables.
Construct a scatter plot of the selected variables and interpret
your findings.
Section 5: Conclusion
The conclusion emphasizes the purpose of the study, reports key
findings, and provides a brief statement on policy
recommendation and possible extension for future studies.