Question

Clicca My WUIN The following information on maintenance and repair costs and revenues for the last two years is available from the accounting records at Arnie's Arcade & Video Palace. Arnie has asked you to help him understand the relation between business volume and maintenance and repair cost. Month July August September October November December January February March April May June July August September October November December January February March April 1.59 Maintenance and Repair Cost $000) Revenues ($000) $1.86 $50.00 2.83 44.00 2.35 40.00 1.76 56.00 1.85 68.00 0.69 96.00 2.67 36.00 2.71 42.00 2.57 52.00 2.53 54.00 58.00 1.33 70.00 2.15 64.00 1.57 58.00 2.12 66.00 1.93 68.00 0.98 78.00 0.31 108.00 2.13 52.00 1.83 54.00 1.24 74.00 1.50 78.00 1.50 64.00 1.60 60.00 May June Required: Using Excel, estimate a linear regression with maintenance and repair cost as the dependent variable and revenue as the independent variable. (Negative amounts should be indicated by a minus sign. Round "Multiple R, R Square and Standard Error" to 7 decimal places, "Intercept and Revenues" to 4 decimal places.)

Answer 1

Regression statistics
Multiple R	0.8852219
R square	0.7836178
Standard error	0.3041103
Observations	24
Coefficients
Intercept	3.9044
Revenues	-0.0336

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The general form of linear regression equation is defined as

Y = b₀ + b₁ X

where Y = dependent variable = Maintenance and repair cost

X = independent variable = Revenues

b₀ = intercept

b₁ = slope

To obtain the values of intercept and slope, we have to perform regression analysis in excel.

In excel, go to data tab and choose data analysis and select regression.

File Connections 2 A2 Home Insert Page Layout Formulas A Show Queries From Table Properties New Query Le Recent Sources All - Edit Links Get & Transform Connections Data Review View Power Pivot Tell me what you want to do... 1x Clear B Flash Fill -- Consolidate - Remove Duplicates Relationships al Sort Filter Advanced Columns Bo Data Validation Manage Data Model Sort & Filter Data Tools Reapply Data Analysis ? Solver Refresh Text to Get External Data el Group - Ungroup - 5 Subtotal Outline What-If Forecast Analysis Sheet Forecast ihtotal Analyze B25 - fa 1.6 D E F G H I J K L M N O P Q R S T U V W Data Analysis ок — Cancel Analysis Tools Exponential Smoothing F-Test Two-Sample for Variances Fourier Analysis Histogram Moving Average Random Number Generation Rank and Percentile Regression Sampling t-Test: Paired Two Sample for Means Help AA в C Maintenance and Repair 1 Month cost ($000) Revenues($000) 2 July $1.86 $50.00 3 August 2.83 44.00 4 September 2.35 40.00 5 October 1.76 56.00 6 November 1.85 68.00 7 December 0.69 96.00 8 January 2.67 36.00 9 February 2.71 42.00 10 March 2.57 52.00 11 April 2.53 54.00 12 May 1.59 58.00 13 June 1.33 70.00 14 July 2.15 64.00 15 August 1.57 58.00 16 September 2.12 66.00 17 October 1.93 68.00 18 November 0.98 78.00 19 December 0.31 108.00 20 January 2.13 52.00 21 February 1.83 54.00 22 March 1.24 74.00 23 April 78.00 24 May 64.00 25 June 60.00 150

View P ower Pivot BE Home Insert Page Layout Formulas Data Review a Show Queries Connections AL ZA iet External From Table E Properties New Refresh AL Sort Data Q uery-Lo Recent Sources All Edit Links Get & Transform Regression Clear Reapply y Advanced Tell me what you want to do... E Flash Fill - Consolidate - Remove Duplicates Relationships What If Forecast Data Validation Manage Data Model Analysis Sheet Data Tools Forecast Data Analysis ? Solver Filter Text to lumns Group Ungroup - Subtotal Outline C ELL Analyze C? Input Input Y Range: | ок Cancel M N O P Q R $B$2:$B$256 SCS2:SC525 S T U V W Input X Range: E Help Labels Confidence Level: Constant is Zero 95 % 41 ABC Maintenance and Repair 1 Month cost (5000) Revenues(5000) 2 July $1.86 550.00 3 August 2.83 44.00 4 September 2.35 40.00 5 October 1.76 56.00 5 November 1.85 68.00 7 December 0.69 96.00 8 January 2.671 36.00 9 February 2.71 42.00 -0 March 2.57 -1 April 2.53 -2 May 1.591 -3 June 1.33 -4 July 2.15 5 August 1.57! -6 September 2.12 -7 October 1.93 8 November 0.98 78.00 9 December 0.31! 108.00 0 January 2.13 52.00! 1 February 1.83 2 March 1.24 74.00 3 April 1.50H 78.00 4 May 64.00 5 June 1.60 Output options Qutput Range: New Worksheet Ply: New Workbook Residuals Residuals Standardized Residuals Residual Plots Line Fit Plots Normal Probability Normal Probability Plots 54.00 1.50 60.00

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.885221888
R Square	0.783617791
Adjusted R Square	0.773782236
Standard Error	0.304110289
Observations	24
ANOVA
	df	SS	MS	F	Significance F
Regression	1	7.368305846	7.368306	79.67194	9.148E-09
Residual	22	2.034627487	0.092483
Total	23	9.402933333
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	3.904394424	0.241992535	16.13436	1.12E-13	3.4025326	4.40625623	3.402532622	4.406256226
X Variable 1	-0.03362783	0.003767438	-8.92591	9.15E-09	-0.041441	-0.0258146	-0.041441018	-0.025814641