REPORT by Assignment 1 Asssignment 1 Submission dat e : 30- Jan- 2018 06:37 AM (UT C- 0800) Submission ID: 9087 314 35 File name : Linear_Regressio n_SLP_2_by_T eresa_Co wad.do cx (50.98K) Word count : 1296 Charact e r count : 7 133 48% SIMILARIT Y INDEX 27% INT ERNET SOURCES 14% PUBLICAT IONS 42% ST UDENT PAPERS 1 14% 2 12% 3 4% 4 3% 5 3% 6 3% 7 2% REPORT ORIGINALITY REPORT PRIMARY SOURCES brainmass.com Int ernet Source Submitted to Trident University International St udent Paper www.statisticshowto.com Int ernet Source www.internationalresearchjournalof f inanceandeconomics.com Int ernet Source Submitted to American Public University System St udent Paper www.ref erence.com Int ernet Source Luca Gugliermetti, Gianf ranco Caruso, Luca Saraceno. "Prediction of subcooled f low boiling pressure drops in small circular tubes", International Journal of Heat and Mass Transf er, 2017 Publicat ion 8 2% 9 1% 10 1% 11 1% 12 1% 13 1% Exclude quo tes Of f Exclude biblio graphy Of f Exclude matches Of f Submitted to American Intercontinental University Online St udent Paper Submitted to University of Witwatersrand St udent Paper Li, Xuejiao, and Takashi Hibiki. "Frictional pressure drop correlation f or two-phase f lows in mini and micro single-channels", International Journal of Multiphase Flow, 2017. Publicat ion hec.gov.pk Int ernet Source www.jolst.net Int ernet Source Hamed Fazlollahtabar, Mohammad Ali Ehsani. "Integration between Regression Model and Fuzzy Logic Approach f or Analyzing Various Electronic Commerce Ef f ects on Economic Growth in Organizations", Journal of Electronic Commerce in Organizations, 2010 Publicat ion FINAL GRADE /0 REPORT GRADEMARK REPORT GENERAL COMMENTS Instructor PAGE 1 PAGE 2 PAGE 3 PAGE 4 PAGE 5 PAGE 6 PAGE 7 REPORTby Assignment 1 Asssignment 1REPORTORIGINALITY REPORTPRIMARY SOURCESREPORTGRADEMARK REPORTFINAL GRADEGENERAL COMMENTSInstructor REPORT 2 by Ass 2 Ass 2 Submission date: 30-Jan-2018 06:38AM (UTC-0800) Submission ID: 908731785 File name: Linear_Ref ression_Case_2_by_Teresa_Coward.docx (52.24K) Word count: 1360 Character count: 7692 56% SIMILARITY INDEX 32% INTERNET SOURCES 21% PUBLICATIONS 53% STUDENT PAPERS 1 11% 2 10% 3 6% 4 4% 5 3% 6 2% 7 2% 8 REPORT 2 ORIGINALITY REPORT PRIMARY SOURCES Submitted to Trident University International Student Paper Submitted to Colorado Technical University Online Student Paper Submitted to American Public University System Student Paper www.statisticshowto.com Internet Source Submitted to Southern New Hampshire University - Continuing Education Student Paper swdllc.paresspacewarpresearch.org Internet Source Submitted to American Intercontinental University Online Student Paper Submitted to Laureate Higher Education Group 2% 9 2% 10 2% 11 2% 12 1% 13 1% 14 1% 15 1% 16 1% 17 1% 18 1% Student Paper brainmass.com Internet Source cc.msnscache.com ...

1. REPORT
by Assignment 1 Asssignment 1
Submission dat e : 30- Jan- 2018 06:37 AM (UT C- 0800)
Submission ID: 9087 314 35
File name : Linear_Regressio n_SLP_2_by_T eresa_Co wad.do
cx (50.98K)
Word count : 1296
Charact e r count : 7 133
48%
SIMILARIT Y INDEX
27%
INT ERNET SOURCES

2. 14%
PUBLICAT IONS
42%
ST UDENT PAPERS
1 14%
2 12%
3 4%
4 3%
5 3%
6 3%
7 2%
REPORT
ORIGINALITY REPORT
PRIMARY SOURCES
brainmass.com
Int ernet Source
Submitted to Trident University International
St udent Paper
www.statisticshowto.com
Int ernet Source
www.internationalresearchjournalof f inanceandeconomics.com
Int ernet Source
Submitted to American Public University
System
St udent Paper

3. www.ref erence.com
Int ernet Source
Luca Gugliermetti, Gianf ranco Caruso, Luca
Saraceno. "Prediction of subcooled f low boiling
pressure drops in small circular tubes",
International Journal of Heat and Mass
Transf er, 2017
Publicat ion
8 2%
9 1%
10 1%
11 1%
12 1%
13 1%
Exclude quo tes Of f
Exclude biblio graphy Of f
Exclude matches Of f
Submitted to American Intercontinental
University Online
St udent Paper
Submitted to University of Witwatersrand
St udent Paper
Li, Xuejiao, and Takashi Hibiki. "Frictional
pressure drop correlation f or two-phase f lows

4. in mini and micro single-channels",
International Journal of Multiphase Flow, 2017.
Publicat ion
hec.gov.pk
Int ernet Source
www.jolst.net
Int ernet Source
Hamed Fazlollahtabar, Mohammad Ali Ehsani.
"Integration between Regression Model and
Fuzzy Logic Approach f or Analyzing Various
Electronic Commerce Ef f ects on Economic
Growth in Organizations", Journal of Electronic
Commerce in Organizations, 2010
Publicat ion
FINAL GRADE
/0
REPORT
GRADEMARK REPORT
GENERAL COMMENTS
Instructor
PAGE 1
PAGE 2
PAGE 3

5. PAGE 4
PAGE 5
PAGE 6
PAGE 7
REPORTby Assignment 1 Asssignment
1REPORTORIGINALITY REPORTPRIMARY
SOURCESREPORTGRADEMARK REPORTFINAL
GRADEGENERAL COMMENTSInstructor
REPORT 2
by Ass 2 Ass 2
Submission date: 30-Jan-2018 06:38AM (UTC-0800)
Submission ID: 908731785
File name: Linear_Ref ression_Case_2_by_Teresa_Coward.docx
(52.24K)
Word count: 1360
Character count: 7692

6. 56%
SIMILARITY INDEX
32%
INTERNET SOURCES
21%
PUBLICATIONS
53%
STUDENT PAPERS
1 11%
2 10%
3 6%
4 4%
5 3%
6 2%
7 2%
8
REPORT 2
ORIGINALITY REPORT
PRIMARY SOURCES

7. Submitted to Trident University International
Student Paper
Submitted to Colorado Technical University
Online
Student Paper
Submitted to American Public University
System
Student Paper
www.statisticshowto.com
Internet Source
Submitted to Southern New Hampshire
University - Continuing Education
Student Paper
swdllc.paresspacewarpresearch.org
Internet Source
Submitted to American Intercontinental
University Online
Student Paper
Submitted to Laureate Higher Education Group
2%
9 2%
10 2%
11 2%
12 1%
13 1%

8. 14 1%
15 1%
16 1%
17 1%
18 1%
Student Paper
brainmass.com
Internet Source
cc.msnscache.com
Internet Source
Submitted to South Iredell High School
Student Paper
Submitted to Belhaven University
Student Paper
Submitted to Oklahoma State University
Student Paper
Submitted to Saint Joseph's Institution,
Singapore
Student Paper
Submitted to University of Witwatersrand
Student Paper
Submitted to Manchester Metropolitan
University
Student Paper

9. en-mat.com
Internet Source
blog.minitab.com
Internet Source
19 1%
Exclude quotes Of f
Exclude bibliography Of f
Exclude matches Of f
Hamed Fazlollahtabar, Mohammad Ali Ehsani.
"Integration between Regression Model and
Fuzzy Logic Approach for Analyzing Various
Electronic Commerce Effects on Economic
Growth in Organizations", Journal of Electronic
Commerce in Organizations, 2010
Publicat ion
REPORT 2by Ass 2 Ass 2REPORT 2ORIGINALITY
REPORTPRIMARY SOURCES
Case 2: 0/50
SLP 2: 0/30
Individual Feedback:
Teresa, I am not able to award a passing grade due to a high
percentage of text from external/previously submitted sources.
Please resubmit with corrections.

10. BUS520 Business Analytics and Decision Making
(2018JAN02FT-2) - Updated Grade: Your grade for "SLP 2" has
been updated
Sent Monday, January 29, 2018 9:57 PM PST
Your grade for "Case 2" has been updated.
Your grade is: F
BUS520 Business Analytics and Decision Making
(2018JAN02FT-2) - Updated Grade: Your grade for "SLP 2" has
been updated
Sent Monday, January 29, 2018 9:54 PM PST
Your grade for "SLP 2" has been updated.
Your grade is: F
Best Regards,
Dr. Fogarty
[Type text][Type text][Type text]
7
Regression Analysis Reporting II
Trident University
Teresa A. Coward/ ID M0000318024
Module 2 SLP 2
BUS520: Business Analytics and Decision Making

11. Professor Dr. David Fogarty
January 29th, 2018
Overview
I’m a consultant for the Diligent Consulting Group, previously
completed the initial project for our client, which was
comprised of developing and testing a forecasting method that
used linear regression techniques. This method used monthly
year one sales over a twelve-month period to forecast year two
sales. The ABC Furniture Company believed that the number of
patrons who visit their store during any particular month was in
relation to the total number of sales for that given month in
question. More specifically, the client believed that there was a
positive relationship between higher customer traffic in the
store and higher total sales associated with consumer commerce,
i.e. the client believed that the higher the number of customers
who visited the store, the higher the total sales would be.
The client had provided me with the number of customers who
visited the store over the most recent twelve-month period from
January to December, with the sales corresponding to each of
those months. A linear regression equation was obtained using
this client's collected information. The linear regression
equation was then used to forecast the sales for year two. The
forecast sales were later compared with the actual year two
sales. In this case the comparison was meant to obtain the trend
with which the performance in this docket was moving. This is
an analytical move that is used in obtaining for example
variances for analysis purposes and ultimately making a
decision.

12. Statistical Evaluation
When factual information is used to scientifically examine
closely data by utilizing linear, logarithmic or exponential
models for representations and make for certifiable
investigations. The information gathered acts as a motivator
behind basic leadership decisions. In this manner, for our
situation we will utilize the factoring principle where the data is
concerned and negate through the research, taking a gander at
all the different issues that needs to be address that are concerns
of management, from those suggestions steer to a
comprehension of these different factors connecting together for
a solution. One of the most usual applications of statistics is
describing a set of data using estimation. By anlizing thus
throughly examining the raw data, we can make and draw a
logical conclusion or even compare, contrast or rank of the data
on the specified attribute. This helps us to make a clear analysis
of the data at hand and therefore come up with clear
understanding of this correlation between the two, therefore
coming to a sound decision in the end accordingly. Evaluating
the status of your business by considering its attributes that
affect customers is a very important aspect for growth and
development, of any business establishments (Walpole, 1982).
As a manager or any other executive for consideration with the
mandate of managing the existence and operations of the
business, the understanding of the foresaid variables
relationship is a crucial thing that needs not be ignored. My
research will show this, as far as wanting the corporation to go
far as far as performance and economic visibility are concerned.
According to Statistics How To.com; “the mean error is an
informal term that usually refers to the average of all the errors
in a set. In dissecting this case study, we are creating the
linear equation and regression model that will give us a clear
relationship between our independent and dependent variable.
First, we’ll calculate in excel the mean error and then we’ll
streamline to viable conclusion, as quoted from Statistics How
To.com; an “error” in this context is an uncertainty in a

13. measurement, or the difference between the measured value and
true/correct value. The more formal term for error is
measurement error, also called observational error. How the
data relate in regard to the correlation that the two variables
have, the value expected from the same correlation and the
behavior of the regression line. The linear regression makes an
effort to model the affiliation between supported variable and
objective variable by fitting a linear equation to observed this
figures. In our case the dependent variable is sale and
independent variable is the consumer.
The mean percentage error (MPE) is the computed average of
percentage errors by which forecasts of a model differ from
actual values of the quantity being forecast.
The mean absolute percentage error (MAPE), also known
as mean absolute percentage deviation (MAPD), is a measure of
prediction accuracy of a forecasting method in statistics, for
example in trend estimation, as it usually expresses accuracy as
a percentage.
Value Calculation Forecast
Endeavoring to fit all raw data for value review, applicable
information in this technique once the determination of the
association between not standing more on the opposition that
one variable causes the other. A linear regression line has an
equation of the form, where X is the explanatory variable and Y
is the dependent variable. The slope of the line is, and is the
intercept (the value of y when x = 0).
The provided in the excel sheet we can see that there are two
column one is sales and other one is customer. This portion of
the research we’ll assume and conclude that
Dependent variable (Y) = sales, the Independent variable (X) =
customers hence we have to fit regression and find scatter plot
and analyze as well as interpret the data. From the regression
and scatter plot the linear equation of the model is. (Excel sheet
is attached)

14. In the equation the slope is 0.648 and the y intercept is 111.65.
The interpretation of slope is for one unit change in customers
will be 0.648 unit increase in sales.
Mean absolute percentage error calculation.
And for SES – MAPE for alpha = 0.15
And for SES – MAPE for alpha = 0.9
Now for overall significance test statistic follows F-distribution
and for individual significance test statistic follows t-
distribution.
Here P-value < alpha, Reject H0 at 0.05 level of significance.
Deduction, the population slope for customers is different than
0. Or consumers are significant variable.
Concluded Recommendation
After diligent research and as your consultant for the Diligent
Consulting Group, I’ve completed the analysis as well as
finalized the forecasting by the two methods; fist Linear
Regression (LR) and Single Exponential Smoothing (SES) to
forecast sales. Therefore, I have been able to categorize the
relationship between our two main identified variables in this
case; consequently, my proposal is as follows:
My recommendations as I’ve come to understand through my
research, is that the mean absolute percentage error is 6.620 for

15. Single Exponential Smoothing method and the mean absolute
percentage error is 17.736 for forecast method. Simply, I’ve
concluded that the lowest mean absolute percentage error is
better to use and suggested which Single Exponential
Smoothing method.
References
CONTENT TEAM, A. (2016, July 14). Going Deeper into
Regression Analysis with
Assumptions, Plots &
Downie, N. M. & Heath, R. W. (1965). Basic Statistical
Methods (2nd ed.). Harper &
Row Publishers
Solution
s, S. (n.d.). Assumptions of Linear Regression. Retrieved
January 23, 2018, from
http://www.statisticssolutions.com/assumptions-of-linear-
regression/
Statistics How To.com. (n.d.). Regression Equation: What it is
and How to use it.

16. Retrieved January 22, 2018, from
http://www.statisticshowto.com/what-is-a-regression-equation/
Walpole, R. (1982). Introduction to Statistics. (3rd ed.).
Prentice Hall Publication.
(2016, January 22). Retrieved January 23, 2018, from
https://www.youtube.com/watch?v=n8J5TbbFSN4
[Type text][Type text][Type text]
7
Regression Analysis Report
Trident University
Teresa A. Coward/ ID M0000318024
Module 2 Case 2
BUS520: Business Analytics and Decision Making
Professor Dr. David Fogarty
January 29th, 2018

17. What To Know
As one of the consultants for the Diligent Consulting Group, I
had previously completed the initial project for our client, the
ABC Furniture Company, which was comprised of developing
and testing a forecasting method which uses linear regression as
a technique to simplify and give direction on how we go about
moving forward in understanding the relationship between the
consumers who visits the stores and the related sales associated
with this collected customer traffic data. In this report, we’re
going to analyze a case study, in which my role as lead
consultant of D.C.G; other clients like the New Star Grocery
Company, who also trusts that there might be a connection
between the quantity of clients and the aggregate deals for
consumer volume for the given time frame in the same month
has financial similarity. To test this examination, the customer
information in the course of current numerical vales in the
recent months and on a month to month basis for the duration of

18. the same year
Statistical Analysis
Statistics is the field of scientific examination and investigation
thats utilized for making sense of the models, for example,
linear models, exponential models, logarithmic models and
more others, in representing and or making summations about
information or real world real-time investigations. One of the
most usual applications of Statistics is describing a set of data
using estimation. By analysing and examining the raw data, we
can make and draw logical conclusions or even compare,
contrast or rank of the data on the specified attribute.
Evaluating the status of your business by considering its
attributes that affect customers is a very important aspect for
the growth and development of any business establishments.
(Walpole, 1982)
The mean error is an informal term that usually refers to the
average of all the errors in a set. An “error” in this context is
an uncertainty in a measurement, or the difference between the
measured value and true or correct value. The more formal term
for error is measurement error, also called observational error.
To analyze this case study, we are creating the linear equation
and regression model that will give a clear guideline on the
relationship between the various variables that are to be
considered for the analysis. And then we come to conclude that
how the data relate to one another.

19. The linear regression makes and attempts to model the
relationship between dependent variable and independent
variable by fitting a linear equation to observed information. In
our case the dependent variable is sales and independent
variable is the consumer. These two variables are our main
concern all through this analysis report so a clearer and concise
picture can be drawn. For example, in my research on this
study, we want to relate that the customer and sales using linear
regression model will give us a clear flow of this relationship
that co-exist between the two mathematically. We will be able
to interpret what is really the relationship between the two and
therefore from the research standpoint, we can get to a point for
a decision to be made for this case truly evaluating the
information on just these two variables as a clear outline as the
conclusionary route to take for that matter.
Before attempting to fit a linear model to the observed data, a
modeler should first determine whether or not there is a
relationship between the variables of interest. This is to make
sure that the resultant values will give a credible data that can
be analyzed and therefore referenced when making any decision
that is in connection to the matter at hand. This does not
necessarily imply that one variable causes the other. But there is
some significant association amongst the two variables. A
scatterplot can be a helpful tool in determining the strength of
the relationship between two variables. If there appears to be no

20. association between the proposed explanatory and dependent
variables (i.e., the scatterplot does not indicate any increasing
or decreasing trends), then fitting a linear regression model to
the data probably will not provide a useful model. A valuable
numerical measure of association between two variables is the
correlation coefficient, which is a value between -1 and 1
indicating the strength of the association of the observed data
for the two variables.
A linear regression line has an equation of the form, where X is
the explanatory variable and Y is the dependent variable. The
slope of the line is, and is the intercept (the value of y when x
= 0).
The provided in the excel sheet we can see that there are two
column one is sales and other one is customer. Here we assume
and conclude that:
Dependent variable (Y) = sales
Independent variable (X) = customers
Now we have to fit regression and find scatter plot and analyze
and interpret the data.
From the regression and scatter plot the linear equation of the
model is. (Excel sheet is attached)
In the equation the slope is 0.648 and the y intercept is 111.65.
The interpretation of slope is for one unit change in customers
will be 0.648 unit increase in sales. We draw the sector

21. diagram. From that we can conclude that there is positives
linear relationship exist bet R-squared is a statistical measure of
how close the data are to the fitted regression line. It is also
known as the coefficient of determination, or the coefficient of
multiple determinations for multiple regressions. From the
scatter diagram we also see R-squared values is 0.718. R-
squared values indicate that the model explains 71.8 % the
variability of the response data around its mean. In general, the
higher the R-squared, the better the model fits your data.
Predicting future sales, the equation of the Predicting future
sales is the same with linear regression equation.
But from the scatter diagram we also see R-squared values is 1.
It is indicates that the model explains all the variability of the
response data around its mean. We can test the same hypothesis
using overall significance and individual significance. Let
suppose Here we want to test the hypothesis that.
Where B is population slope for customers. Assume alpha =
level of significance = 0.05
Here for overall significance test statistic follows F-distribution
and for individual significance test statistic follows t-
distribution.

22. Here P-value < alpha Reject H0 at 0.05 level of significance.
Conclusion of this is the population slope for customers is
differing than 0. OR customers are an significant variable.
Conclusion And Recommendation
From all of the above analysis, graphs, regression model,
Predicting future sales and R-squared value, we conclude that is
significant and positive linear relation exist between customer
and sales. We also seen that the linear model explains 71.8% the
variability of the response data around its mean and the
prediction future model explains 100% the variability of the
response data around its mean. We also know that the higher the
R-squared, the better the model fits your data. So, I would like
to recommend and suggest predicting future sales should be use
because of high fitness of the model.

23. References
Casella, G. and Berger, R. L. (2002). Statistical Inference.
Duxbury Press.
Cox, D. R. and Hinkley, D. V. (2000). Theoretical Statistics.
Chapman and Hall Ltd
Frost, J. (1970, May 30). Regression Analysis: How Do I
Interpret R-squared and Assess
the Goodness-of-Fit? Retrieved January 23, 2018, from
http://blog.minitab.com/blog/adventures-in-statistics-
2/regression-analysis-how-do-i-interpret-r-squared-and-assess-
the-goodness-of-fit
Khan, S. (n.d.). Second regression example. Retrieved January
22, 2018, from
https://www.khanacademy.org/math/statistics-
probability/describing-relationships-quantitative-data/more-on-
regression/v/second-regression-example?topic=statistics
Khan, S. (n.d.). Regression line example. Retrieved January 22,

24. 2018, from
https://www.khanacademy.org/math/statistics-
probability/describing-relationships-quantitative-data/more-on-
regression/v/regression-line-example?topic=statistics
Linear Regression. (n.d.). Retrieved January 23, 2018, from
http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm