A firm recorded its sales (Qt) in monthly observations from January 2013 to August 2014. The data collected is shown in the table below: 

Sales ($ Thousands)

Qt
Jan-13	46
Feb-13	56
Mar-13	72
May-13	67
Jun-13	77
Aug-13	66
Sep-13	69
Oct-13	79
Dec-13	88
Jan-14	91
Mar-14	94
Apr-14	104
Jun-14	100
Jul-14	113
Aug-14	120

a. Below is the output:

Regression Statistics
Multiple R	0.960795768
R Square	0.923128508
Adjusted R Square	0.917215316
Standard Error	6.064850994
Observations	15

ANOVA
	df	SS	MS	F	Significance F
Regression	1	5742.228571	5742.228571	156.1134082	1.28554E-08
Residual	13	478.1714286	36.78241758
Total	14	6220.4

Dependent var: Qt	Coefficients	Standard Error	t Stat	P-value
Intercept	46.57	3.30	14.13	2.87E-09
t	4.53	0.36	12.49	1.29E-08

b. The intercept (a) is 46.57. It tells us the value of Sales that is not dependent on time.

c. The slope term (b) is 4.53. It tells us the change in Sales when the time increases by 1 month.

d.

 

e. Forecast the level of sales in August 2015. Show your result in the chart.

f. Forecast the level of sales for August 2018. Don’t need to show it in the chart.

g. Which forecast is more reliable, 2015 or 2018? Why? Explain.

Transcribed Image Text:

140 y= 4.5286x + 46.571 120 R= 0.9231 100 80 60 40 20 3 4 6. 7 10 11 12 13 14 15 16 Months Sales 140 y= 4.5286x + 46.571 120 R= 0.9231 100 80 60 40 20 3 4 6. 7 10 11 12 13 14 15 16 Months Sales

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