Question

Civilian Employment Level	All Employees: Total Nonfarm Payrolls	Total Public Construction Spending	Total Construction Spending Annual Rate
1000s of Persons, Seasonally Adjusted	1000s of Persons, Seasonally Adjusted	$Millions, Not Seasonally Adjusted	$Millions, Seasonally Adjusted
CE16OV	PAYEMS	TLPBLCON	TTLCONS
136559	131007	11709	784940
136598	131138	11404	793737
136701	131605	12977	809459
137270	131898	14052	804766
136630	132117	15106	805005
136940	132081	16255	795411
136531	132254	16201	783795
136662	132239	18787	805341
136893	132383	18836	814330
137088	132368	17270	816100
137322	132590	15398	820054
137614	132727	13331	811516
137778	132702	12411	814479
137612	132776	12168	813647
137783	132748	13638	828057
137299	132469	15518	842392
137092	132428	17557	848146
136873	132307	19012	855730
137071	132199	19470	850471
136241	132053	20706	847465
136846	131803	19836	837412

1. From the data set, run a regression using the Civilian Employment Level (CE16OV) as the dependent variable and PAYEMS, TLPBLCON, TTLCONS as the independent variables.

1. What is the sample regression equation relating CE16OV to the three independent variables?
2. What percentage of the variation of CE16OV has been explained by the regression?
3. Test the overall fit of the regression. Use a 5% level of significance. State the hypotheses to be tested, the decision rule, the test statistic, and your decision.

2. From the data set, determine the best regression model to explain the Civilian Employment Level. Write a concise report to show, explain, and justify why you chose that model. In the technical section of your report you will want to discuss aspects such as your regression equation, the choice of variables, the strength of the relationship, and the practical usefulness of the results. What do your results tell you about employment levels?

*Document how you got your answers and be sure to label (identify) your steps. That is, be concise but do not oversimplify your answers. Give sufficient detail to show how you reach your conclusion.

Answer 1

excel data analysis tool for regression output summary is

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.8900
R Square	0.7921
Adjusted R Square	0.7555
Standard Error	215.4669
Observations	21
ANOVA
	df	SS	MS	F	Significance F
Regression	3	3007737.58	1002579.19	21.60	0.00
Residual	17	789241.66	46425.98
Total	20	3796979.24
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	52010.7009	13547.6475	3.8391	0.0013	23427.6631	80593.7386
PAYEMS	0.6159	0.1075	5.7290	0.0000	0.3891	0.8427
TLPBLCON	-0.1020	0.0206	-4.9619	0.0001	-0.1454	-0.0586
TTLCONS	0.0064	0.0030	2.1513	0.0461	0.0001	0.0126

a)

regression eqn is

predicted Civilian Employment Level (CE16OV) = 52010.7009 + 0.6159*payems - 0.1020*TTPBLCON +0.0064*TTLCONS

b)since R^2=0.7921,so

79.21 percentage of the variation of CE16OV has been explained by the regression

c)

hypothesis:

Ho:overall model is significnat

h1: overall model is not significant

F-stat=21.60

F-critical = 3.197

alpha=0.05

if F-stat > f-critical then reject Ho

decision : reject Ho

conclusion : overall fir of regression is significant