Question

QUESTION 8

1. Use the Manufacturing database from “Excel Databases.xls” on Blackboard. Use Excel to develop a multiple regression model to predict Cost of Materials by Number of Employees, Number of Production Workers, Value Added by Manufacture, New Capital Expenditures, and End-of-Year Inventories. Use Excel to perform a backward elimination regression analysis at a 5% level of significance. What is the test statistic of the independent variable that is dropped from the linear model in the first step. Write your answer as a number and round to 2 decimal places.

https://drive.google.com/file/d/19TI3HId0greXS0nkmDuoITv1IMPF_TUK/view?usp=sharing

Please answer tonight, I need help with this and my last questions got closed.

Answer 1

A multiple regression model to predict Cost of Materials by Number of Employees, Number of Production Workers, Value Added by Manufacture, New Capital Expenditures, and End-of-Year Inventories.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.839739844
R Square	0.705163006
Adjusted R Square	0.694161626
Standard Error	13587.81459
Observations	140
ANOVA
	df	SS	MS	F	Significance F
Regression	5	59171362320	1.18E+10	64.0976843	7.34406E-34
Residual	134	24740246509	1.85E+08
Total	139	83911608829
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-921.8128594	1576.212143	-0.58483	0.55964687	-4039.285837	2195.660118	-4039.285837	2195.660118
No. Emp.	-279.160853	45.42582026	-6.14542	8.53111E-09	-369.0052104	-189.3164956	-369.0052104	-189.3164956
No. Prod. Wkrs.	336.1246881	50.9337021	6.599259	8.80871E-10	235.3867001	436.8626761	235.3867001	436.8626761
Value Added by Mfg.	1.338788815	0.26095153	5.130412	9.92166E-07	0.822672156	1.854905473	0.822672156	1.854905473
New Cap. Exp.	1.264888119	1.42112829	0.890059	0.375029357	-1.54585602	4.075632257	-1.54585602	4.075632257
End Yr. Inven.	0.777053472	0.433971543	1.790563	0.075621193	-0.081266625	1.635373569	-0.081266625	1.635373569

A backward elimination regression analysis at a 5% level of significance.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.786739964
R Square	0.61895977
Adjusted R Square	0.610554471
Standard Error	15332.99468
Observations	140
ANOVA
	df	SS	MS	F	Significance F
Regression	3	51937910124	17312636708	73.63923123	2.37064E-28
Residual	136	31973698705	235100725.8
Total	139	83911608829
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-1296.437754	1766.919907	-0.733727515	0.464377817	-4790.629258	2197.75375	-4790.629258	2197.75375
No. Prod. Wkrs.	40.5598283	19.9748762	2.030542162	0.044250749	1.058296608	80.06136	1.058296608	80.06136
Value Added by Mfg.	0.654798086	0.207463658	3.156206217	0.001968557	0.244526094	1.065070078	0.244526094	1.065070078
New Cap. Exp.	3.71342516	1.497100481	2.48041144	0.014343744	0.75281797	6.674032351	0.75281797	6.674032351

The test statistic of the independent variable that is dropped from the linear model in the first step.

The test statistic t has the same sign as the correlation coefficient r is used to drop independent variable(No. Emp.) from the linear model in the first step with the significant F value(1.2849E-27).