Journal Article Sales and Dealership Size as a Predictor of a Store’s Profit Abstract This study aims to know if a dealership’s size and sales could affect the owner’s profit. The statistical analysis that was used is multiple linear regression analysis. The results showed that a dealership’s size can explain 94.46% of the owner’s profit. On the other hand, the sales in both Sedans and SUV’s can explain 79.26% of the owner’s profit. Other than that, the analysis also showed that the increase in dealership size by a thousand sq. ft can also increase the profit by 11 940. For the sales, an increase in Sedan sales by one could increase the profit by 2 320 and an increase in SUV sales by one could increase the profit by 4 790. All of the coefficients and the regression models are proven significant and reliable by using multiple hypothesis testing. By using these results, a person aspiring to be a retailer owner would know what to increase so that his/her profits would increase too. SALES AND DEALERSHIP SIZE AS A PREDICTOR OF A STORE’S PROFIT Establishing a store is easy because all that is needed is an initial investment and good management skills. The challenging task to do is making that store successful. There are many factors that could affect a store’s monthly profit. The mere design of a retailer, including color and interior design, can increase the owner’s profit. One measurable factor that could affect revenue is the owner’s initial investment. If the owner is willing to risk a lot, then the possible income would be more than that. In the end, knowing how much one factor can affect a store’s profit is a desirable trait. It can be achieved easily by using regression analysis in Microsoft Excel or SPSS. Getting the data is easy but interpreting the data can be difficult. METHODOLOGY Linear regression and multiple linear regression analysis are both thorough methods of determining correlation and determination. This is the statistical analysis used. By using Microsoft Excel’s Analyst Tool Pack, summary outputs of regression statistics and ANOVA was able to be gathered. The summary outputs are attached in the appendices. From those analyses, the equations for the predicted value of profit based on the independent variables were created. Other than the equations, their characteristics are also present, such as the standard error, t-stat, p-value, and F value. Standard error of a statistic is the standard deviation of the data, which uses sampling distribution (Everett). In regression, it is the standard error of the regression coefficient. P-value is the probability value for a given statistical data is the same or greater than the number of the observed (Wasserstein and Lazar). F value is used to compare the data that has been fitted to another data set to check if the sample can represent the population (Lomax). Lastly, the t-statistic is the proportion of how far the value of a restriction is from a computed value to its stan ...

1. Journal Article
Sales and Dealership Size as a Predictor of a Store’s Profit
Abstract
This study aims to know if a dealership’s size and sales
could affect the owner’s profit. The statistical analysis that was
used is multiple linear regression analysis. The results showed
that a dealership’s size can explain 94.46% of the owner’s
profit. On the other hand, the sales in both Sedans and SUV’s
can explain 79.26% of the owner’s profit. Other than that, the
analysis also showed that the increase in dealership size by a
thousand sq. ft can also increase the profit by 11 940. For the
sales, an increase in Sedan sales by one could increase the
profit by 2 320 and an increase in SUV sales by one could
increase the profit by 4 790. All of the coefficients and the
regression models are proven significant and reliable by using
multiple hypothesis testing. By using these results, a person
aspiring to be a retailer owner would know what to increase so
that his/her profits would increase too.
SALES AND DEALERSHIP SIZE AS A PREDICTOR OF A
STORE’S PROFIT
Establishing a store is easy because all that is needed is an

2. initial investment and good management skills. The challenging
task to do is making that store successful. There are many
factors that could affect a store’s monthly profit. The mere
design of a retailer, including color and interior design, can
increase the owner’s profit. One measurable factor that could
affect revenue is the owner’s initial investment. If the owner is
willing to risk a lot, then the possible income would be more
than that. In the end, knowing how much one factor can affect a
store’s profit is a desirable trait. It can be achieved easily by
using regression analysis in Microsoft Excel or SPSS. Getting
the data is easy but interpreting the data can be difficult.
METHODOLOGY
Linear regression and multiple linear regression analysis are
both thorough methods of determining correlation and
determination. This is the statistical analysis used. By using
Microsoft Excel’s Analyst Tool Pack, summary outputs of
regression statistics and ANOVA was able to be gathered. The
summary outputs are attached in the appendices. From those
analyses, the equations for the predicted value of profit based
on the independent variables were created. Other than the
equations, their characteristics are also present, such as the
standard error, t-stat, p-value, and F value. Standard error of a
statistic is the standard deviation of the data, which uses
sampling distribution (Everett). In regression, it is the standard
error of the regression coefficient. P-value is the probability
value for a given statistical data is the same or greater than the
number of the observed (Wasserstein and Lazar). F value is
used to compare the data that has been fitted to another data set
to check if the sample can represent the population (Lomax).
Lastly, the t-statistic is the proportion of how far the value of a
restriction is from a computed value to its standard. It is a lot
like the z score but it’s used when the sample size is less than
30 ("T Statistic: Definition, Types and Comparison to Z
Score"). Before showing the results of the analysis, the initial
data should be presented first. The units are in thousands square
feet and in thousands.

3. Dealer Number
Sedan Sales
SUV Sales
Dealership Size
(In thousands sq. ft)
Profit
(In thousands)
1
366
42
42
480
2
156
118
26
360
3
227
100
32
400
4
210
91
19
220
5
121
112
34
380
6
323
92
55

4. 640
7
505
53
67
840
8
234
68
14
160
9
338
37
48
520
10
244
110
38
560
Table 1: Data of a store showing their dealership size, profit,
and sales which is categorized by type of car,
This is the data that was analyzed. The next two tables are
the results.
Regression Equation:
Ŷ = 8.18 + 11.94X
R Square
0.95
F
140.28
Significance-F:
≈ 0.00
Property
Intercept
Dealership Size (X)

5. Sb1:
40.88
1.01
T-stat:
0.20
11.84
P-value:
≈ 0.00
0.85
This shows the result for the regression analysis when profit is
the dependent variable and dealership size is the independent
variable.
Regression Equation:
Ŷ = -569.64 + 2.32X1 + 4.79 X2
R Square:
0.79
F:
13
Significance-F:
≈ 0.00
Property
Intercept
Sedan Sales (X1)
SUV Sales (X2)
Sb1:
259.37
0.47
1.77
T-stat:
-2.20
4.91
2.71
P-value:

6. ≈ 0.06
0.03
0.03
This shows the result for the regression analysis when profit is
the dependent variable and Sedan sales and SUV sales are the
independent variables.
EMPERICAL-RESULTS
Both of the situations show promising results. The very
first data property needed is the significance-F. It shows how
reliable the results are. To know how important the result is,
with the use of significance-F, subtract is from one (1) and then
multiply it to 100 and add a percentage symbol (%). The
resulting number is the confidence level of how reliable the
results are. Both of the equations show 99.99% confidence
level.
The next data results are for the use of hypothesis testing.
To test if the slopes of the equations are significant, the slope
should not be equal to 0. Therefore, the null hypothesis is that
the coefficient is equal to 0. For the coefficient to be considered
significant, the null hypothesis should be rejected. For that to
happen, the t-statistic from the results should be greater than
the critical values from the t-table.
For the first equation, wherein the dealership size is the
independent variable, this is the result.
Hypotheses:
Ho: β = 0
Ha: β ≠ 0
Decision: From the t distribution, df = 7 and α = 0.05, critical
value is 2.365
11.84 > 2.365 Therefore, the Ho is rejected.
For the second equation, wherein the Sedan and SUV sales are
the independent variables, this is the result.

7. Hypotheses:
Ho: β1 = B2 = 0
Ha: β1 ≠ β2 ≠ 0, or all three are not equal to 0.
Decision: From the t distribution, df = 7 and α = 0.05, critical
value is 2.365
2.71 > 2.365 and 4.91 > 2.365 Therefore, the Ho
is rejected.
To test if the regression models are significant, calculated
F value should be greater than F critical, or the F value from a
standard F-ratio table.
For the first equation, wherein the dealership size is the
independent variable, this is the result.
Hypotheses:
Ho: the regression model does not explain any of the total
variation in the dependent variable
Ha: the regression model does explain a proportion of the
total variation in the dependent variable that is greater than 0
Decision: From the F-ratio table, regression df = 1 residual df =
8 and α = 0.05, F critical is 5.32
140.28 > 5.32 Therefore, the Ho is rejected.
For the second equation, wherein the Sedan and SUV sales are
the independent variables, this is the result.
Hypotheses:
Ho: the regression model does not explain any of the total
variation in the dependent variable
Ha: the regression model does explain a proportion of the
total variation in the dependent variable that is greater than 0
Decision: From the F-ratio table, regression df = 2 residual df =
7 and α = 0.05, F critical is 4.74
13.36 > 4.74 Therefore, the Ho is rejected.
To test if the coefficients are significant, the P-value of an

8. independent value should be less than the confidence level (α).
For the first equation, wherein the dealership size is the
independent variable, this is the result.
Hypotheses:
Ho: β = 0
Ha: β ≠ 0
Decision: the confidence level α is 95% or 0.95.
2.36 × (10-6) < 0.05 Therefore, the Ho is rejected.
For the second equation, wherein the Sedan and SUV sales are
the independent variables, this is the result.
Hypotheses:
Ho: β1 =2 = 0
Ha: β1 ≠ β2 ≠ 0, or all three are not equal to 0.
Decision: the confidence level is 95%, therefore α is 0.05
0.03 < 0.05
4.08 × (10-3) < 0.0.5 Therefore, the Ho is rejected.
Now that it has been proven that the regression models and
their coefficients are significant, the thing to explain is how
much do the regression models explain the data. The coefficient
of determination, or R square, measures how much can the
independent variable explain the value of the dependent
variable. For the first equation, the R square is 0.9460. This
means, that 94.60% of the difference in Dealership Size (X) can
explain the Profit (Y) of a store. For the first equation, the R
square is 0.7924. This means, that 79.24% of the difference in
Sedan sales (X1) and SUV sales (X2) can explain the Profit (Y)
of a store. They are both positive, this means that greater the
dealership size, Sedan sales, and SUV sales are, the higher the
profits would be.
DISCUSSION
According to the results, all of the determinants, given
dealership size, Sedan sales, and SUV sales, can and will
increase the profits of a store. But in this situation, the
dealership size of a retailer is the most influential factor in the

9. increase or decrease of their profit. The regression model
wherein the dealership size is the independent variable is better
than the regression model wherein the Sedan sales and the SUV
sales are the independent variables. This is because the value of
R square in the first situation is significantly greater than the
latter. This means that the increase in dealership size can
greatly affect the retailer’s profit. According to the equation,
each 1000 sq. ft increase in the size of their dealership would
also mean an increase in their profit by 11 940.
CONCLUSION
Using regression models in sales is very effective and
important to boost in sales. In these situations, it has been
evident that a dealership’s size and sales will greatly affect a
dealer’s profit.

10. Appendices (Excel Workbook Copied)
SUMMARY OUTPUT for Dealership as Independent Variable
Regression Statistics
Multiple R
0.97
R Square
0.95
Adjusted R Square
0.94

11. Standard Error
49.15
Observations
10.00
ANOVA
df
SS
MS
F
Significance F
Regression
1.00
338912.01
338912.01
140.28

12. 0.00
Residual
8.00
19327.99
2416.00
Total
9.00
358240.00
Coefficients
Standard Error
t Stat
P-value
Intercept
8.18
40.88
0.20
0.85
Dealership Size (In thousands sq ft)
11.94
1.01
11.84
0.00

13. SUMMARY OUTPUT for Sedan Sales and SUV sales as
Independent Variable
Regression Statistics
Multiple R
0.89
R Square
0.79
Adjusted R Square
0.73
Standard Error
103.08

14. Observations
10.00
ANOVA
df
SS
MS
F
Significance F
Regression
2.00
283855.99
141928.00
13.36
0.00
Residual
7.00
74384.01

15. 10626.29
Total
9.00
358240.00
Coefficients
Standard Error
t Stat
P-value
Intercept
-569.64
259.37
-2.20
0.06
Sedan Sales
2.32
0.47
4.91
0.00
Suv Sales
4.79
1.77

16. 2.71
0.03
SOUTHWEST AIRLINES
1. Calculate the investor's required rate‑of‑return on the
common stock & find the investor's required rate‑of‑return on
one of the company's bond issues.
2. Provide a conclusion as to whether the company currently is
overvalued, undervalued, or fairly valued.
3. Make a commendation as to buy, hold, or sell the stock.
Provide the rationale for your recommendation.
Journal Article
ECO578_Fall_2019"
You can submit your work either as a PDF or WORD File
(Make sure Only ONE file is
submitted)

17. Excel file is allowed) *Only one Word or PDF file and one
Excel file should be submitted in the drop box
Profits
Use the given Sample data for profits made by Car Dealers and
follow the instructions below:
1. Run a regression using the given data. (With Profit as the
dependent variable and Dealership
Store size as the independent variable)
2. Compare the results of question (1) with another regression
equation obtained by regressing the
Profit on the variables : Sedan Sales and Suv sales.
3. Which of the models do you prefer? Why?
4. Interpret your results for (1) and (2).
5. In writing your paper, you should start by indicating the
objectives of the study and value
addition thereof from the business context. Also, discuss the
methodology and conclusions.
**Range: 5 pages + Excel printout results**
**You can use EXCEL or any other software**
** You need to include your printout in your submissions**
The layout and format of the paper should include the following
sections:

18. Title page, Abstract, Introduction, Methodology, Empirical-
Results, and Discussion, Conclusions.
a) Abstract: is a very concise summary of the paper.
b) Introduction: Tells the reader about the topic. Specifically, it
should start with 'the purpose of this paper is to examine.' 'What
the issue is', 'what is known about it', and the specific focus?
Put a business context to it - write the value added by your work
or what businesses can gain from the knowledge of the
determinants of incomes.
1
c) Methodology: You should be able to explain what method
you are using for your work. For
example, you can start with telling the reader that the 'ordinary
least squares method (OLS) was used to obtain estimated
coefficients...' (Then write more). You should be able to write
the
equation from the Excel result. We expect to see you
1. Using the excel result to generate the equation.
2. Find the standard error of each variable. 3. Find the t-stat of
each variable.
4. Find the p-value of each variable. 5. Find the F-value of the
equations
EXAMPLE:
Ŷ = 1.24 + 1.71X1 - 0.83X2 - 2.12X3
������ (6.79) (1.43) (0.22) (0.85)
T-stat [0.18] [1.20] [3.78] [2.48]
P-value 0.857 0.247 0.002 0.025
d) Empirical-Results: Should tell what was found from the
computed data. Using Chapter 10 and 11 for
helping you interpret your result. For instance, you should
interpret the Coefficient of Determination (R2),

19. F-test, t-test, p-value and etc. In addition, your result should use
the hypothesis equation.
e) Discussion: Describes what your findings mean in the light of
the information presented in the introduction. It is the
interpretive segment of the paper and loops back to answer the
issues raised in the introduction. Remember to discuss questions
2 and 3.
f) Conclusion: What conclusion would you draw based on your
study.
**Remember that the results section should include hypothesis
testing for a t-test about each slope coefficient and an F-test for
the overall regression model.
EXAMPLE:
For the t-test, it is written like this:
H0: 1 = 0
Ha: 1 0
Note:
ide whether to use one-tail or two-tail test
alternative hypothesis in each case
very well
F-test:
H0: 1 = 2 = 3 = 0
Ha: 1 2 3 0, or all three are 0
Note: mention what is used in your hypothesis testing.

20. 2
Dealership Size (In
Dealer Number Sedan Sales Suv Sales Profit (In
thousands)
thousands sq ft)
1 366 42 42 480 2 156 118 26 360 3 227
100 32 400 4 210 91 19 220 5 121 112 34
380 6 323 92 55 640 7 505 53 67 840 8
234 68 14 160 9 338 37 48 520
10 244 110 38 560
How to Submit Your Journal
You should save your file as "Lastname_Firstname_
ECO578_Fall_2019"
You can submit your work either PDF or WORD file
(Make sure Only ONE file is submitted)
You can attach your Excel file as your appendix (only
ONE Excel file is allowed)
*Only one Word or PDF file and one Excel file should be
submitted in the drop box
er "Journal Article" in Dropbox

21. 3