Report

For Ahaana Sales report

Running head: PREDICTIVE SALES REPORT

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

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

7.4

6.7

6.2

5.1

5.2

5.8

5.5

5.2

5.1

5.4

5.5

5.6

5.5

6.1

5.5

6.6

6.9

7.1

6.9

7.0

6.6

6.7

6.1

6.0

6.7

5.8

5.5

5.6

5.5

5.4

5.7

5.6

5.4

5.7

5.5

5.6

5.7

5.9

5.7

5.9

5.6

5.4

5.5

5.7

5.5

5.6

5.4

5.3

5.1

5.2

5.0

5.1

4.8

5.0

5.2

4.9

5.1

4.8

4.6

4.4

4.0

3.8

3.7

3.8

3.9

3.8

3.9

3.8

4.0

3.9

3.8

3.7

3.8

3.7

3.5

3.7

3.5

3.4

3.6

3.4

3.5

3.7

3.5

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

5.9

6.0

5.9

6.0

6.1

6.0

5.8

6.0

5.8

5.7

5.8

5.7

5.6

5.5

5.6

5.3

5.2

5.6

4.9

5.0

4.9

5.0

4.9

4.8

4.6

4.8

4.9

5.1

5.2

5.1

5.4

5.5

5.9

6.0

6.6

5.6

8.1

8.8

8.6

8.4

7.7

7.4

7.6

7.8

7.6

7.7

7.8

7.7

7.5

7.6

7.4

7.2

7.0

7.2

6.9

7.0

6.8

6.4

7.1

6.4

6.3

6.1

6.0

5.9

6.2

5.9

6.0

5.8

5.9

6.0

6.1

5.9

5.8

5.6

5.7

6.0

5.9

6.0

5.9

6.0

5.9

6.3

6.9

7.5

7.6

7.8

7.7

7.5

7.2

7.5

7.4

7.2

7.5

7.2

7.4

7.6

7.9

8.3

8.5

7.6

8.6

9.0

9.8

10.8

10.4

10.1

9.4

8.8

8.5

8.3

9.6

7.8

7.7

7.4

7.2

7.5

7.3

7.4

7.2

7.3

7.5

7.3

7.2

7.3

7.2

7.4

7.1

7.0

7.2

6.7

7.2

7.1

7.2

7.0

6.9

7.0

6.9

6.6

7.0

6.6

6.3

6.2

6.1

6.0

5.9

6.0

5.8

5.7

6.2

5.7

5.4

5.6

5.4

5.6

5.4

5.3

5.5

5.4

5.2

5.0

5.2

5.3

5.2

5.3

5.4

5.3

5.4

5.3

5.2

5.4

5.2

5.5

5.7

5.9

6.2

6.3

5.6

6.4

6.6

6.8

6.7

6.9

6.8

6.9

7.0

7.3

6.9

7.3

7.4

7.6

7.8

7.7

7.6

7.3

7.4

7.5

7.3

7.1

7.0

7.1

7.0

6.9

6.8

6.7

6.8

6.6

6.5

6.9

6.6

6.5

6.4

6.1

6.0

5.9

5.8

5.6

5.5

6.1

5.6

5.4

5.8

5.6

5.7

5.6

5.5

5.6

5.5

5.6

5.3

5.5

5.1

5.2

5.4

5.3

5.2

5.1

4.9

5.0

4.9

4.8

4.9

4.7

4.6

4.7

4.9

4.6

4.7

4.3

4.4

4.5

4.6

4.5

4.4

4.5

4.3

4.4

4.2

4.3

4.2

4.3

4.2

4.1

4.0

4.2

4.0

4.1

4.0

3.8

4.0

4.1

3.9

4.0

4.2

4.3

4.4

4.3

4.5

4.6

4.9

5.0

5.3

5.5

5.7

4.7

5.7

5.9

5.8

5.7

5.9

6.0

5.8

5.9

6.0

6.1

6.3

6.2

6.1

6.0

5.8

5.7

6.0

5.7

5.6

5.8

5.6

5.5

5.4

5.5

5.4

5.5

5.3

5.4

5.2

5.1

5.0

4.9

5.0

4.9

5.1

4.7

4.8

4.7

4.6

4.7

4.5

4.4

4.5

4.4

4.6

4.5

4.4

4.5

4.4

4.6

4.7

4.6

4.7

5.0

4.6

5.0

4.9

5.1

5.0

5.4

5.6

5.8

6.1

6.5

6.8

7.3

5.8

7.8

8.3

9.0

9.4

9.5

9.6

9.8

9.9

9.3

9.8

9.9

9.6

9.4

9.5

9.8

9.3

9.6

9.0

8.9

9.0

9.1

9.0

8.9

8.6

8.5

8.9

8.3

8.2

8.1

8.2

8.1

7.8

7.9

7.8

8.1

7.9

7.7

7.6

7.5

7.6

	Year	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Annual
1958																			5.8				6.4					6.7								7.4			7.3			7.5		7.1		6.2	6.8
1959																				6.0															5.9								5.6					5.2	5.1	5.0	5.5	5.7	5.3
1960										4.8																							5.4				6.1				6.6
1961														6.9										7.0			6.5
1962
1963
1964																4.9
1965														4.7											4.6									4.4		4.3		4.2		4.1		4.0			4.5
1966																		3.8						3.9						3.7	3.6
1967
1968									3.5								3.4
1969
1970
1971
1972
1973
1974																7.2
1975					8.1				8.6			8.8							9.0		8.4				8.3				8.2			8.5
1976				7.9								7.7									7.6									7.8
1977
1978									6.3
1979
1980
1981
1982				8.9			9.3				9.4				9.6					9.8		10.1		10.4	10.8	9.7
1983	10.3	10.2							9.5	9.2
1984	8.0
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009	8.7	10.0				9.9
2010
2011		9.1
2012
2013

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.

5.4921219

ANOVA
Source	df	SS	MS	F
Regression	1		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.

Year

Coefficients	Standard Error	t Stat	P-value
Intercept	-33.424726	26.234357	-1.274082	0.208193
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 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.

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, 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.

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