Question

video Consider the following time series. Quarter Year 2Year 3 Year 1 71 49 58 75 68 41 60 81 62 51 53 75 a. Choose a time series plot. TimeSeries Value 60 40 20 TimePeriod(t) TimeSeries Value O Type here to search otps/hg cengage.com/static/nb/uifevo/index.html?deploymentld-57360022185226012174 N-97813371153778id-429851674&snapsho NGAGE MINDTAP er 17 Assignment a. Choose a time series plot. TameSeries Value 60 40 20 24TimePeriod it) TimeSeries Value 60 40 20 TimeSeries Value 60 40 20 TimePeriodit) Time Period it 2 TimeSeries Value 40 TimePeriod (t) - TimeSeries Value 2 4 TimePeriod it) 4 TimeSeries Value 2 4TimePeriodt Tvpe here to search GAGE MINDTAP Q Search this course r 17 Assignment 40 20 TimePeriodit TimeSeries Value 60 40 20 Select your What type of pattern exists in the data? b. Use the following duminy variables to develop an estimate regression equator ta acont, Qtr3 1 if Quarter 3, 0 otherwise. Enter negative values as negative numbers. Value - c. Compute the quarterly forecasts for next year Quarter 1 forecast Quarter 2 forecast Quarter 3 forecast Quarter 4 forecast or 95ggonal efects n heda s . r srt-1 n il f psrer· ther ser the nisei Qtr1+ Qtr3 Check My Work (2 remaining mdeploymentld 573600221852260121746122951 8elSBN-9781337 115378id 4298516748snapshotld -10627788 SAGE MINDTAP Q Search this course 17 Assignment 40 20 TimePeriod t) TmeSeries Value 60 40 20 24Time Period it) Select your answer What type of pattern exists in the data? Select your a Select your answer Horizontal regression equation to account for seasonal effects in the dota: Qurl-1 f Quarter 1, 0 ctherwise; Qtr2-1Quarter 2, 0 othe otherwise. Enter negative values as negative numbers Qtri t Qtr2 + Qtr3 forecasts for next year Quarter 1 forecast Quarter 2 forecast Quarter 3 forecast Quarter 4 forecast Check My Work (remain

Answer 1

A)

90 80 70 60 50 40 30 20 10 0 10 12 14

correct graph is 1 )

there is seasonality

B)

Y	t	Q1	Q2	Q3
71	1	1	0	0
49	2	0	1	0
58	3	0	0	1
75	4	0	0	0
68	5	1	0	0
41	6	0	1	0
60	7	0	0	1
81	8	0	0	0
62	9	1	0	0
51	10	0	1	0
53	11	0	0	1
75	12	0	0	0

Using Excel

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.954040971
R Square	0.910194175
Adjusted R Square	0.87651699
Standard Error	4.301162634
Observations	12
ANOVA
	df	SS	MS	F	Significance F
Regression	3	1500	500	27.02702703	0.000154211
Residual	8	148	18.5
Total	11	1648
	Coefficients	Standard Error	t Stat	P-value	Lower 95%
Intercept	77	2.483277404	31.00740975	1.27215E-09	71.27355204
Q1	-10	3.511884584	-2.847473987	0.021562653	-18.09842037
Q2	-30	3.511884584	-8.542421962	2.7151E-05	-38.09842037
Q3	-20	3.511884584	-5.694947975	0.000457035	-28.09842037

y^ = 77 - 10Qtr1 -30 Qtr2 - 20 Qtr3

Q1	67
Q2	47
Q3	57
Q4	77