Question

Question 2 Suppose a researcher gathered survey data from 19 employees and asked the employees to rate their job satisfaction on a scale from 0 to 100 (with 100 being perfectly satisfied). Suppose the following data represent the results of this survey. Assume that relationship with their supervisor is rated on a scale from 0 to 50 (0 represents a poor relationship and 50 represents an excellent relationship); overall quality of the work environment is rated on a scale from 0 to 100 (0 represents poor work environment and 100 represents an excellent work environment); and opportunities for GB513: Business Analytics 2 of 4 advancement is rated on a scale from 0 to 100 (0 represents no opportunities and 100 represents excellent opportunities). Answer the following questions: a) What is the regression formula based on the results from your regression? b) How reliable do you think the estimates will be based on this formula? Explain your answer by citing the relevant metrics. c) Are there any variables that do not appear to be good predictors of job satisfaction? How can you tell? d) If a new employee reports that her relationship with her supervisor is 40, rates her opportunities for advancement to be at 30, finds the quality of the work environment to be at 75, and works 60 hours per week, what would you expect her job satisfaction score to be? Job satisfaction Relationship with supervisor Opportunities for advancement Overall quality of work environment Total hours worked per week 55 27 42 50 52 20 35 28 60 60 85 40 7 45 42 65 35 48 65 53 45 29 32 40 58 70 42 41 50 48 35 22 18 75 55 60 34 32 40 50 95 40 48 45 40 65 33 11 60 38 85 38 33 55 47 10 5 21 50 62 75 37 42 45 43 80 37 46 40 42 50 31 48 60 46 90 42 30 55 38 75 36 39 70 43 45 20 22 40 42 65 32 12 55 53

can the answer be typed out please with excel?

Answer 1

Bitmap Bitmap Bitmap SUMMARY OUTPUT
Regression Statistics
Multiple R	0.906974193
R Square	0.822602186
Adjusted R Square	0.771917097
Standard Error	11.05943598
Observations	19
ANOVA
	df	SS	MS	F	Significance F
Regression	4	7940.275841	1985.06896	16.22966819	3.73654E-05
Residual	14	1712.355738	122.3111241
Total	18	9652.631579
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	98.32908724	29.9636493	3.281612539	0.005458373	34.0634513	162.5947232	34.0634513	162.5947232
Relationship with supervisor(X1)	1.323243881	0.368701564	3.58892939	0.002962862	0.532457676	2.114030086	0.532457676	2.114030086
Opportunities for advancement(X2)	0.077349313	0.207254871	0.373208663	0.714583336	-0.367168174	0.5218668	-0.367168174	0.5218668
Overall quality of work environment(X3)	-0.170042037	0.25373087	-0.670166926	0.513654675	-0.714240629	0.374156554	-0.714240629	0.374156554
Total hours worked per week(X4)	-1.522385082	0.433602894	-3.511012271	0.003458686	-2.452370794	-0.59239937	-2.452370794	-0.59239937

a) the regression formula based on the results from your regression

Y = 98.329 + 1.323 X1 + 0.077 X2 - 0.17 X3 - 1.522 X4

b)

R = 0.90697 = 90.7%

Which means There is statistical strong relation between dependent and independent variables

Another way

H0 : There is no statistically significant relation between dependent and independent variables

Ha : There is statistically significant relation between dependent and independent variables

P value = 0 , reject H0

There is statistically significant relation between dependent and independent variables

c)

Coefficient Table Iteration 1 (adjusted R-squared = 0.772)

	Coeff	SE	t-stat	lower t0.025(14)	upper t0.975(14)	Stand Coeff	p-value	VIF
b	98.3291	29.9636	3.2816	34.0635	162.5947	0.000	0.005458
X1	1.3232	0.3687	3.5889	0.5325	2.1140	0.5143	0.002963	1.6209
X2	0.07735	0.2073	0.3732	-0.3672	0.5219	0.04450	0.7146	1.1219
X3	-0.1700	0.2537	-0.6702	-0.7142	0.3742	-0.07679	0.5137	1.0361
X4	-1.5224	0.4336	-3.5110	-2.4524	-0.5924	-0.4866	0.003459	1.5156

Coefficient Table Iteration 2 (adjusted R-squared = 0.785)

	Coeff	SE	t-stat	lower t0.025(15)	upper t0.975(15)	Stand Coeff	p-value	VIF
b	98.9633	29.0445	3.4073	37.0565	160.8701	0.000	0.003899
X1	1.3661	0.3402	4.0160	0.6411	2.0911	0.5310	0.001122	1.4637
X3	-0.1781	0.2454	-0.7257	-0.7013	0.3450	-0.08044	0.4792	1.0286
X4	-1.5048	0.4185	-3.5958	-2.3967	-0.6128	-0.4809	0.002648	1.4976

Coefficient Table Iteration 3 (adjusted R-squared = 0.791)

	Coeff	SE	t-stat	lower t0.025(16)	upper t0.975(16)	Stand Coeff	p-value	VIF
b	92.4738	27.2217	3.3971	34.7663	150.1814	0.000	0.003683
X1	1.3504	0.3344	4.0381	0.6415	2.0593	0.5249	0.0009526	1.4578
X4	-1.5543	0.4067	-3.8215	-2.4165	-0.6921	-0.4968	0.001503	1.4578

X2,X3 are not good predictors of job satisfaction

d) If a new employee reports that her relationship with her supervisor is 40, rates her opportunities for advancement to be at 30, finds the quality of the work environment to be at 75, and works 60 hours per week

Y = 98.329 + 1.323 X1 + 0.077 X2 - 0.17 X3 - 1.522 X4

Y = 98.329 + 1.323 *40 + 0.077 *30 - 0.17 *75 - 1.522 * 60

Y = 98.329 + 52.92 + 2.31 - 12.75 - 91.32

= 153.559 - 104.07

Y = 49.489

Job satisfaction = 49.49