Running head: PREDICTIVE SALES REPORT 1 PREDICTIVE SALES REPORT 5 Predictive Sales Report Name Course Number Tutor Date Predictive Sales Report The rate of unemployment in the economy is of great concern to retail stores. This is because an increase in the rate of unemployment translates to a decrease in consumer spending (Arnold, 2010). In contrast, a decrease in the rate of unemployment translates to an increase in consumer spending. In this respect, a decreasing rate of unemployment is welcome news for retail stores. As an economic consultant, I will analyze the rate of unemployment in the US in the last 55 years. Based on this analysis, I will make a prediction of the rate of unemployment in year 2016. Using this projected rate, I will postulate whether a retail store will gain or loss. In addition, the discussion will present other factors that are likely to influence sales in the retail industry. Part 1 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual 1958 5.8 6.4 6.7 7.4 7.4 7.3 7.5 7.4 7.1 6.7 6.2 6.2 6.8 1959 6.0 5.9 5.6 5.2 5.1 5.0 5.1 5.2 5.5 5.7 5.8 5.3 5.5 1960 5.2 4.8 5.4 5.2 5.1 5.4 5.5 5.6 5.5 6.1 6.1 6.6 5.5 1961 6.6 6.9 6.9 7.0 7.1 6.9 7.0 6.6 6.7 6.5 6.1 6.0 6.7 1962 5.8 5.5 5.6 5.6 5.5 5.5 5.4 5.7 5.6 5.4 5.7 5.5 5.6 1963 5.7 5.9 5.7 5.7 5.9 5.6 5.6 5.4 5.5 5.5 5.7 5.5 5.6 1964 5.6 5.4 5.4 5.3 5.1 5.2 4.9 5.0 5.1 5.1 4.8 5.0 5.2 1965 4.9 5.1 4.7 4.8 4.6 4.6 4.4 4.4 4.3 4.2 4.1 4.0 4.5 1966 4.0 3.8 3.8 3.8 3.9 3.8 3.8 3.8 3.7 3.7 3.6 3.8 3.8 1967 3.9 3.8 3.8 3.8 3.8 3.9 3.8 3.8 3.8 4.0 3.9 3.8 3.8 1968 3.7 3.8 3.7 3.5 3.5 3.7 3.7 3.5 3.4 3.4 3.4 3.4 3.6 1969 3.4 3.4 3.4 3.4 3.4 3.5 3.5 3.5 3.7 3.7 3.5 3.5 3.5 1970 3.9 4.2 4.4 4.6 4.8 4.9 5.0 5.1 5.4 5.5 5.9 6.1 5.0 1971 5.9 5.9 6.0 5.9 5.9 5.9 6.0 6.1 6.0 5.8 6.0 6.0 6.0 1972 5.8 5.7 5.8 5.7 5.7 5.7 5.6 5.6 5.5 5.6 5.3 5.2 5.6 1973 4.9 5.0 4.9 5.0 4.9 4.9 4.8 4.8 4.8 4.6 4.8 4.9 4.9 1974 5.1 5.2 5.1 5.1 5.1 5.4 5.5 5.5 5.9 6.0 6.6 7.2 5.6 1975 8.1 8.1 8.6 8.8 9.0 8.8 8.6 8.4 8.4 8.4 8.3 8.2 8.5 1976 7.9 7.7 7.6 7.7 7.4 7.6 7.8 7.8 7.6 7.7 7.8 7.8 7.7 1977 7.5 7.6 7.4 7.2 7.0 7.2 6.9 7.0 6.8 6.8 6.8 6.4 7.1 1978 6.4 6.3 6.3 6.1 6.0 5.9 6.2 5.9 6.0 5.8 5.9 6.0 6.1 1979 5.9 5.9 5.8 5.8 5.6 5.7 5.7 6.0 5.9 6.0 5.9 6.0 5.9 1980 6.3 6.3 6.3 6.9 7.5 7.6 7.8 7.7 7.5 7.5 7.5 7.2 7.2 1981 7.5 7.4 7.4 7.2 7.5 7.5 7.2 7.4 7.6 7.9 8.3 8.5 7.6 1982 8.6 8.9 9.0 9.3 9.4 9.6 9.8 9.8 10.1 10.4 10.8 10.8 9.7 1983 10.4 10.4 10.3 10.2 10.1 10.1 9.4 9.5 9.2 8.8 8.5 8.3 9.6 1984 8.0 7.8 7.8 7.7 7.4 7.2 7.5 7.5 7.3 7.4 7.2 7.3 7.5 1985 7.3 7.2 7.2 7.3 7.2 7.4 7.4 7.1 7.1 7.1 7.0 7.0 7.2 1986 6.7 7.2 7.2 7.1 7.2 7.2 7.0 6.9 7.0 7.0 6.9 6.6 7.0 1987 6.6 6.6 6.6 6.3 6.3 6.2 6.1 6.0 5.9 6.0 5.8 5.7 6.2 1988 5.7 5.7 5.7 5.4 5.6 5.4 5.4 5.6 5.4 5.4 5.3 5.3 5.5 1989 5.4 5.2 5.0 5.2 5.2 5.3 5.2 5.2 5.3 5.3 5.4 5.4 5.3.

1. Running head: PREDICTIVE SALES REPORT
1
PREDICTIVE SALES REPORT
5
Predictive Sales Report
Name
Course
Number
Tutor
Date
Predictive Sales Report
The rate of unemployment in the economy is of great concern to
retail stores. This is because an increase in the rate of
unemployment translates to a decrease in consumer spending
(Arnold, 2010). In contrast, a decrease in the rate of
unemployment translates to an increase in consumer spending.
In this respect, a decreasing rate of unemployment is welcome
news for retail stores. As an economic consultant, I will analyze
the rate of unemployment in the US in the last 55 years. Based
on this analysis, I will make a prediction of the rate of
unemployment in year 2016. Using this projected rate, I will
postulate whether a retail store will gain or loss. In addition, the
discussion will present other factors that are likely to influence
sales in the retail industry.
Part 1
Year
Jan

2. Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Annual
1958
5.8
6.4
6.7
7.4
7.4
7.3
7.5
7.4
7.1
6.7
6.2
6.2
6.8
1959
6.0
5.9
5.6
5.2
5.1
5.0
5.1
5.2
5.5

3. 5.7
5.8
5.3
5.5
1960
5.2
4.8
5.4
5.2
5.1
5.4
5.5
5.6
5.5
6.1
6.1
6.6
5.5
1961
6.6
6.9
6.9
7.0
7.1
6.9
7.0
6.6
6.7
6.5
6.1
6.0
6.7
1962
5.8
5.5
5.6

4. 5.6
5.5
5.5
5.4
5.7
5.6
5.4
5.7
5.5
5.6
1963
5.7
5.9
5.7
5.7
5.9
5.6
5.6
5.4
5.5
5.5
5.7
5.5
5.6
1964
5.6
5.4
5.4
5.3
5.1
5.2
4.9
5.0
5.1
5.1
4.8

5. 5.0
5.2
1965
4.9
5.1
4.7
4.8
4.6
4.6
4.4
4.4
4.3
4.2
4.1
4.0
4.5
1966
4.0
3.8
3.8
3.8
3.9
3.8
3.8
3.8
3.7
3.7
3.6
3.8
3.8
1967
3.9
3.8
3.8
3.8
3.8

6. 3.9
3.8
3.8
3.8
4.0
3.9
3.8
3.8
1968
3.7
3.8
3.7
3.5
3.5
3.7
3.7
3.5
3.4
3.4
3.4
3.4
3.6
1969
3.4
3.4
3.4
3.4
3.4
3.5
3.5
3.5
3.7
3.7
3.5
3.5
3.5

7. 1970
3.9
4.2
4.4
4.6
4.8
4.9
5.0
5.1
5.4
5.5
5.9
6.1
5.0
1971
5.9
5.9
6.0
5.9
5.9
5.9
6.0
6.1
6.0
5.8
6.0
6.0
6.0
1972
5.8
5.7
5.8
5.7
5.7
5.7
5.6

8. 5.6
5.5
5.6
5.3
5.2
5.6
1973
4.9
5.0
4.9
5.0
4.9
4.9
4.8
4.8
4.8
4.6
4.8
4.9
4.9
1974
5.1
5.2
5.1
5.1
5.1
5.4
5.5
5.5
5.9
6.0
6.6
7.2
5.6
1975
8.1

9. 8.1
8.6
8.8
9.0
8.8
8.6
8.4
8.4
8.4
8.3
8.2
8.5
1976
7.9
7.7
7.6
7.7
7.4
7.6
7.8
7.8
7.6
7.7
7.8
7.8
7.7
1977
7.5
7.6
7.4
7.2
7.0
7.2
6.9
7.0
6.8

10. 6.8
6.8
6.4
7.1
1978
6.4
6.3
6.3
6.1
6.0
5.9
6.2
5.9
6.0
5.8
5.9
6.0
6.1
1979
5.9
5.9
5.8
5.8
5.6
5.7
5.7
6.0
5.9
6.0
5.9
6.0
5.9
1980
6.3
6.3
6.3

11. 6.9
7.5
7.6
7.8
7.7
7.5
7.5
7.5
7.2
7.2
1981
7.5
7.4
7.4
7.2
7.5
7.5
7.2
7.4
7.6
7.9
8.3
8.5
7.6
1982
8.6
8.9
9.0
9.3
9.4
9.6
9.8
9.8
10.1
10.4
10.8

12. 10.8
9.7
1983
10.4
10.4
10.3
10.2
10.1
10.1
9.4
9.5
9.2
8.8
8.5
8.3
9.6
1984
8.0
7.8
7.8
7.7
7.4
7.2
7.5
7.5
7.3
7.4
7.2
7.3
7.5
1985
7.3
7.2
7.2
7.3
7.2

13. 7.4
7.4
7.1
7.1
7.1
7.0
7.0
7.2
1986
6.7
7.2
7.2
7.1
7.2
7.2
7.0
6.9
7.0
7.0
6.9
6.6
7.0
1987
6.6
6.6
6.6
6.3
6.3
6.2
6.1
6.0
5.9
6.0
5.8
5.7
6.2

14. 1988
5.7
5.7
5.7
5.4
5.6
5.4
5.4
5.6
5.4
5.4
5.3
5.3
5.5
1989
5.4
5.2
5.0
5.2
5.2
5.3
5.2
5.2
5.3
5.3
5.4
5.4
5.3
1990
5.4
5.3
5.2
5.4
5.4
5.2
5.5

15. 5.7
5.9
5.9
6.2
6.3
5.6
1991
6.4
6.6
6.8
6.7
6.9
6.9
6.8
6.9
6.9
7.0
7.0
7.3
6.9
1992
7.3
7.4
7.4
7.4
7.6
7.8
7.7
7.6
7.6
7.3
7.4
7.4
7.5
1993
7.3

16. 7.1
7.0
7.1
7.1
7.0
6.9
6.8
6.7
6.8
6.6
6.5
6.9
1994
6.6
6.6
6.5
6.4
6.1
6.1
6.1
6.0
5.9
5.8
5.6
5.5
6.1
1995
5.6
5.4
5.4
5.8
5.6
5.6
5.7
5.7
5.6

17. 5.5
5.6
5.6
5.6
1996
5.6
5.5
5.5
5.6
5.6
5.3
5.5
5.1
5.2
5.2
5.4
5.4
5.4
1997
5.3
5.2
5.2
5.1
4.9
5.0
4.9
4.8
4.9
4.7
4.6
4.7
4.9
1998
4.6
4.6
4.7

18. 4.3
4.4
4.5
4.5
4.5
4.6
4.5
4.4
4.4
4.5
1999
4.3
4.4
4.2
4.3
4.2
4.3
4.3
4.2
4.2
4.1
4.1
4.0
4.2
2000
4.0
4.1
4.0
3.8
4.0
4.0
4.0
4.1
3.9
3.9
3.9

19. 3.9
4.0
2001
4.2
4.2
4.3
4.4
4.3
4.5
4.6
4.9
5.0
5.3
5.5
5.7
4.7
2002
5.7
5.7
5.7
5.9
5.8
5.8
5.8
5.7
5.7
5.7
5.9
6.0
5.8
2003
5.8
5.9
5.9
6.0
6.1

20. 6.3
6.2
6.1
6.1
6.0
5.8
5.7
6.0
2004
5.7
5.6
5.8
5.6
5.6
5.6
5.5
5.4
5.4
5.5
5.4
5.4
5.5
2005
5.3
5.4
5.2
5.2
5.1
5.0
5.0
4.9
5.0
5.0
5.0
4.9
5.1

21. 2006
4.7
4.8
4.7
4.7
4.6
4.6
4.7
4.7
4.5
4.4
4.5
4.4
4.6
2007
4.6
4.5
4.4
4.5
4.4
4.6
4.7
4.6
4.7
4.7
4.7
5.0
4.6
2008
5.0
4.9
5.1
5.0
5.4
5.6
5.8

22. 6.1
6.1
6.5
6.8
7.3
5.8
2009
7.8
8.3
8.7
9.0
9.4
9.5
9.5
9.6
9.8
10.0
9.9
9.9
9.3
2010
9.8
9.8
9.9
9.9
9.6
9.4
9.5
9.5
9.5
9.5
9.8
9.3
9.6
2011
9.1

23. 9.0
8.9
9.0
9.0
9.1
9.0
9.0
9.0
8.9
8.6
8.5
8.9
2012
8.3
8.3
8.2
8.1
8.2
8.2
8.2
8.1
7.8
7.9
7.8
7.8
8.1
2013
7.9
7.7
7.6
7.5
7.6

24. Scatter Plot with linear regression line
Summary Output
Regression Statistics
Multiple R
0.202606
R Square
0.041049
Adjusted R Square
0.022956
Standard Error
1.555886
Observations
55
From the regression statistics above, the R-Square value is
0.041049, meaning that only 4.1 percent of our dependent
variable is accounted for by the predictor variable. This,
therefore, means that the dependent variable (Unemployment
rate) can be explained by many other factors that are not
captured in this model. Some of these factors include GDP
growth, foreign exchange rate and interest rates.
ANOVA
Source
df
SS
MS
F

25. Regression
1
5.4921219
5.4921219
2.26874
Residual
53
128.3013326
2.4207799
Total
54
133.7934545
The table above is the summary statistics for Analysis of
Variance (ANOVA).
DF stands for degrees of freedom
SS stands for Sum of Squares.
MS stands for Mean Sum of Squares
F stands for the F ratio
At 5 percent significance level, the F critical value is 4.0012.
We compare this value with the F ratio and observe that the F
critical value is greater than the F calculated. We, therefore,
conclude that the regression is not significant.
Coefficients
Standard Error
t Stat
P-value
Intercept
-33.424726
26.234357

26. -1.274082
0.208193
Year
0.019906
0.013216
1.506234
0.137944
Our linear regression line should be of the form;
Where; Y is the dependent or response variable
is the constant term or intercept
is the coefficient of regression
is the independent or predictor variable
In our case, the unemployment rate is the dependent variable,
while the year is the independent variable. We obtain a linear
regression line of the form;
The positive coefficient of regression means that there is a
positive correlation between year and unemployment rate. For
every additional unit increase in year, the rate of unemployment
increases by 0.0199 units or 1.99 percent.
Since the P values for the intercept and regression coefficient
are greater than 0.05, we conclude that the coefficients of
regression are not different from 0.
Projection of the rate of unemployment for year 2016
This, therefore, means the rate of unemployment is likely to
decrease in the coming years. This decrease suggests a positive
outlook of the economy. It is likely that more jobs will be

27. created in the coming years, meaning that people will take
advantage of these newly created positions. In the retail front, a
decrease in unemployment means that consumer spending is
likely to increase.
The rate of unemployment includes the percentage of people in
the labor force who are not employed at a given point in time
(Abel & Bernanke, 2006). This people are very much willing to
work, but find it difficult to secure jobs. There is a positive
correlation between the rate of unemployment and consumer
spending. An increase in unemployment means that fewer
people earn income, implying that consumer spending is low. In
contrast, a decrease in unemployment means that more people
earn income and wages. As a result, consumer spending tends to
increase. This is particularly the case in retail stores. If the rate
of unemployment is high, retail stores will experience a
shortage of consumer spending (Krafft & Mantrala, 2010). In
contrast, a falling unemployment rate is welcome news for retail
stores, who are likely to experience an increase in consumer
spending.
The rate of unemployment aside, surveys have indicated that a
number of factors will influence the retail industry in the
coming years. For instance, surveys have shown that consumers
will likely be focused on settling old debts and raising their
savings. Other surveys have also indicated that price-conscious
consumers will hold to their conservativeness. Currently, there
is stiff competition in the retail industry, and it can be expected
that this competition will get tougher in the coming years. This,
therefore, means that retailers who will survive are the ones
with a considerable competitive advantage. It has also been
observed that online purchases have increased in the recent
years (Gupta, Jaroliya, & Prestige Institute of Management and
Research, 2008). This is attributable to the increase in access of
internet as well as the rise of online marketing giants such as
Amazaon.com. In the coming years, a similar trend is likely
going to be observed. Retailers can benefit from this by
affording consumers the option to purchase products online.

28. Another important point to note is that the rate of
unemployment has been a bit consistent, which gives retailers
more optimism on the business outlook in the coming years.
When the rate of unemployment hovered around 8 percent in
2011, developers were pessimistic about creating novel centers.
This pessimism by retailers can potentially hinder the growth of
novel retailers as well as the introduction of novel vendors. For
existing retailers, this is a plus because they do not need to be
troubled by competitors.
Conclusion
This discussion has analyzed unemployment data in the last 55
years. In particular, a linear regression analysis was carried out.
It was observed that there is a positive correlation between
unemployment rate (dependent variable) and the year
(independent variable). The resulting regression was noted to be
significant. Using the regression model, it was projected that
the rate of unemployment in 2016 will be 6.7, meaning that
unemployment is likely to decrease in the coming years. This is
welcome news for the retail store, because consumer spending is
likely to increase. That notwithstanding, the retail store should
also be cognizant of other factors that may influence its sales in
the coming years. One factor that was noted is competition. It
has been noted that competition in the retail industry has
increased in the recent years and will likely increase in the
coming years. In this respect, the retail store should aim to have
a competitive advantage over its rivals. It was also noted that
with the increase in the use of technology, consumers have
preferred online shopping. To this end, the retail store should
afford consumers the option of purchasing products online.
Overall, there is a positive economic outlook and the retail store
should seize the opportunity to benefit from the projected
decrease in the rate of unemployment in 2016.
References
Abel, A. B., & Bernanke, B. (2006). Macroeconomics. Boston,

29. Mass: Pearson.
Arnold, R. A. (2010). Macroeconomics. Mason, OH: Cengage
Learning, South Western
Gupta, I. C., Jaroliya, D., & Prestige Institute of Management
and Research. (2008). IT enabled practices and emerging
management paradigms. Indore: Prestige Institute of
Management and Research.
Krafft, M., & Mantrala, M. K. (2010). Retailing in the 21st
century: Current and future trends. Heidelberg: Springer.
University of Phoenix
Colleges of Arts and Sciences
BSHS/373 – Financial Management in Human Services
Week Four
CareSafe Foster Systems Budget GRADING RUBRIC
(LEARNING TEAM)
Content
Points Available
9
Points Earned
X/9
Additional Comments:
All key questions are answered in a substantive way and are
supported by specific examples, details, or analysis from the
CareSafe Foster Systems activity.
· The importance of a workable budget
· Discussed at least four methods to increase the size of the

30. CareSafe’s surplus
· Rationale for most feasible method
· Foreseen problems in CareSafe’s financial future and what
may be done to prevent those problems
Organization / Development
Points Available
3
Points Earned
X/3
Additional Comments:
· The paper is 1,050- to 1,200 words in length.
· The introduction provides sufficient background on the topic
and previews major points.
· The conclusion is logical, flows, and reviews the major points.
Mechanics
Points Available
3
Points Earned
X/3
Additional Comments:
· The paper, including the title page, reference page, tables, or
appendices, follows APA standards for format as directed by the
instructor.
· The paper is laid out with effective use of headings, font
styles, and white space.

31. · Rules of grammar, usage, and punctuation are followed, and
spelling is correct
Total Available
Total Earned
15