Question

Suppose the following data were collected from a sample of 15 CEOs relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the following regression equation: SALARYi=b0+b1EXPERIENCEi+b2SERVICEi+b3INDUSTRIALi+ei .

Is there enough evidence to support the claim that on average, CEOs in the industrial sector have lower salaries than CEOs in the financial sector at the 0.05 level of significance?

If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence."

Suppose the following data were collected from a sample of 15 CEOS relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the following regression equation: SALARY bo biEXPERIENCEb2SERVICE b3INDUSTRIALe Is there enough evidence to support the claim that on average, CEOS in the industrial sector have lower salaries than CEOS in the financial sector at the 0.05 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence." Copy Data

Answer 1

Hi Coop in the industrial your Sector and financial . Sectory have equal Salariey. Hii CEO'y in the industrial secar have lower falemey than CFox in financial sectory. 2:0.05 (herel of significance) - Y= botb, x, + b2 Xz + b3 Xz te is the Regression e st in a salary, - X, - Experience X₂ = Selvice Xy: Industrial EXCEL SOFTWARE → Enter the data in Seperate columny with quitable - headings as given in the problem → Select Data Repression Data Analysix pake Regresion ~ Select the variable y me Salary → Select the remaing date under the Naniable y - select the labels in first row and confidence level 95% - click OK. The Fitted Regrexxion equation ini 1908.40 + 1279.01%, -25912.54 X₂ -24485-32 8₂ + e; e;= error R = 0.913 ~ 91.3% independent Variable, x, X₂ X are explained about the dependent variable Y (salary) Since F- calculated value > Fitable valle wes ad accent Hi

Industrial La nola 00 O OOO Salary Experience Service 136878 137700 130175 142800 198115 22 195412 26 152225 18 200357 173625 30 144075 177530 146825 138750 157300 132600 oo Hoo o o o do 110 1 14 ooh oooo .

Regression Input Input Y Range: HOK $C$286:$C$301 Cancel Input X Range: $0$286:$G$301 | 11 Help Labels Confidence Level: 1 Constant is Zero 195 Output options $1$288 O Output Range: New Worksheet Ply: New Workbook Residuals Residuals standardized Residuals Residual Plots Line Fit Plots Normal Probability Normal Probability Plots

SUMMARY OUTPUT Regression Statistics Multiple R 0.955740473 JR Square 0.913439851 Adjusted R Square 0.889832538 Standard Error 8254.26683 Observations 15 ANOVA df Significance F 38.69308803 3.88411E-06 Regression Residual Total S 7908819318 749462129.9 8658281448 MS 2636273106 68132920.9 t Intercept Experience Service Industrial Coefficients 149280.4031 1779.010819 -25912.54374 -24485.32426 Standard Error 5800.09335 245.8108057 5374.663244 5496.716567 Stat 25.73758629 7.237317393 -4.821240432 -4.454536443 P-value Lower 95% 3.51953E-11 136514.4837 1.67093E-05 1237.984884 0.000534816 -37742.0978 0.000971385 -36583.5158 is .