PLEASE HELP WITH HOW TO DO THIS WITH EXCEL!! THANK YOU! Repair Time in Hours Months Since Last Service Type of Repair Repairperson 2.9 2 Electrical Donna Newton 2.9 2 Electrical Donna Newton 4.4 4 Electrical Bob Jones 4.5 6 Electrical Donna Newton 4.8 8 Electrical Bob Jones 4.9 7 Electrical Bob Jones 1.8 3 Mechanical Donna Newton 3.0 6 Mechanical Donna Newton 4.2 9 Mechanical Bob Jones 4.8 8 Mechanical Bob Jones SUMMARY OUTPUT Regression Statistics Multiple R 0.730873795 R Square 0.534176504 Adjusted R Square 0.475948567 Standard Error 0.781022322 Observations 10 ANOVA df SS MS F Significance F Regression 1 5.596033058 5.596033058 9.17388683 0.016338159 Residual 8 4.879966942 0.609995868 Total 9 10.476 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2.147272727 0.604977289 3.549344356 0.007516627 0.752192596 3.542352858 0.752192596 3.542352858 X Variable 1 0.304132231 0.100412033 3.02884249 0.016338159 0.072581669 0.535682794 0.072581669 0.535682794 Using the simple linear regression model developed in part (a), calculate the predicted repair time and residual for each of the 10 repairs in the data. Sort the data in ascending order by value of the residual. Do you see any pattern in the residuals for the two types of repair? Do you see any pattern in the residuals for the two repairpersons? Do these results suggest any potential modifications to your simple linear regression model? Now create a scatter chart with months since last service on the x-axis and repair time in hours on the y-axis for which the points representing electrical and mechanical repairs are shown in different shapes and/or colors. Create a similar scatter chart of months since last service and repair time in hours for which the points representing repairs by Bob Jones and Donna Newton are shown in different shapes and/or colors. Do these charts and the results of your residual analysis suggest the same potential modifications to your simple linear regression model?

PLEASE HELP WITH HOW TO DO THIS WITH EXCEL!! THANK YOU! Repair Time in Hours Months Since Last Service Type of Repair Repairperson 2.9 2 Electrical Donna Newton 2.9 2 Electrical Donna Newton 4.4 4 Electrical Bob Jones 4.5 6 Electrical Donna Newton 4.8 8 Electrical Bob Jones 4.9 7 Electrical Bob Jones 1.8 3 Mechanical Donna Newton 3.0 6 Mechanical Donna Newton 4.2 9 Mechanical Bob Jones 4.8 8 Mechanical Bob Jones SUMMARY OUTPUT Regression Statistics Multiple R 0.730873795 R Square 0.534176504 Adjusted R Square 0.475948567 Standard Error 0.781022322 Observations 10 ANOVA df SS MS F Significance F Regression 1 5.596033058 5.596033058 9.17388683 0.016338159 Residual 8 4.879966942 0.609995868 Total 9 10.476 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2.147272727 0.604977289 3.549344356 0.007516627 0.752192596 3.542352858 0.752192596 3.542352858 X Variable 1 0.304132231 0.100412033 3.02884249 0.016338159 0.072581669 0.535682794 0.072581669 0.535682794 Using the simple linear regression model developed in part (a), calculate the predicted repair time and residual for each of the 10 repairs in the data. Sort the data in ascending order by value of the residual. Do you see any pattern in the residuals for the two types of repair? Do you see any pattern in the residuals for the two repairpersons? Do these results suggest any potential modifications to your simple linear regression model? Now create a scatter chart with months since last service on the x-axis and repair time in hours on the y-axis for which the points representing electrical and mechanical repairs are shown in different shapes and/or colors. Create a similar scatter chart of months since last service and repair time in hours for which the points representing repairs by Bob Jones and Donna Newton are shown in different shapes and/or colors. Do these charts and the results of your residual analysis suggest the same potential modifications to your simple linear regression model?

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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PLEASE HELP WITH HOW TO DO THIS WITH EXCEL!! THANK YOU!

Repair Time in Hours	Months Since Last Service	Type of Repair	Repairperson
2.9	2	Electrical	Donna Newton
2.9	2	Electrical	Donna Newton
4.4	4	Electrical	Bob Jones
4.5	6	Electrical	Donna Newton
4.8	8	Electrical	Bob Jones
4.9	7	Electrical	Bob Jones
1.8	3	Mechanical	Donna Newton
3.0	6	Mechanical	Donna Newton
4.2	9	Mechanical	Bob Jones
4.8	8	Mechanical	Bob Jones

SUMMARY OUTPUT

Regression Statistics
Multiple R	0.730873795
R Square	0.534176504
Adjusted R Square	0.475948567
Standard Error	0.781022322
Observations	10

ANOVA
	df	SS	MS	F	Significance F
Regression	1	5.596033058	5.596033058	9.17388683	0.016338159
Residual	8	4.879966942	0.609995868
Total	9	10.476

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	2.147272727	0.604977289	3.549344356	0.007516627	0.752192596	3.542352858	0.752192596	3.542352858
X Variable 1	0.304132231	0.100412033	3.02884249	0.016338159	0.072581669	0.535682794	0.072581669	0.535682794

Using the simple linear regression model developed in part (a), calculate the predicted repair time and residual for each of the 10 repairs in the data. Sort the data in ascending order by value of the residual. Do you see any pattern in the residuals for the two types of repair? Do you see any pattern in the residuals for the two repairpersons? Do these results suggest any potential modifications to your simple linear regression model? Now create a scatter chart with months since last service on the x-axis and repair time in hours on the y-axis for which the points representing electrical and mechanical repairs are shown in different shapes and/or colors. Create a similar scatter chart of months since last service and repair time in hours for which the points representing repairs by Bob Jones and Donna Newton are shown in different shapes and/or colors. Do these charts and the results of your residual analysis suggest the same potential modifications to your simple linear regression model?

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