PurposeThis assignment provides an opportunity to develop, evaluate, and apply bivariate and...

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PurposeThis assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.	Resources:Microsoft Excel®, DAT565_v3_Wk5_Data_File	Instructions:The Excel file for this assignment contains a database with information about the tax assessment value assigned to medical office buildings in a city. The following is a list of the variables in the database:			FloorArea: square feet of floor space			Offices: number of offices in the building			Entrances: number of customer entrances			Age: age of the building (years)			AssessedValue: tax assessment value (thousands of dollars)	Use the data to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics.		Construct a scatter plot in Excel with		FloorArea		as the independent variable and		AssessmentValue		as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?	Use Excel’s Analysis ToolPak to conduct a regression analysis of		FloorArea		and		AssessmentValue. Is			FloorArea		a significant predictor of		AssessmentValue?	Construct a scatter plot in Excel with		Age		as the independent variable and		AssessmentValue		as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?	Use Excel’s Analysis ToolPak to conduct a regression analysis of Age and Assessment Value. Is		Age		a significant predictor of		AssessmentValue?	Construct a multiple regression model.	Use Excel’s Analysis ToolPak to conduct a regression analysis with		AssessmentValue		as the dependent variable and		FloorArea,		Offices,		Entrances, and		Age		as independent variables. What is the overall fit r^2? What is the adjusted r^2?	Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated?	What is the final model if we only use		FloorArea		and Offices as predictors?	Suppose our final model is:			AssessedValue		= 115.9 + 0.26 x		FloorArea		+ 78.34 x		Offices		What wouldbe the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?

Shakeel · Accepted Answer

Regression Modeling Data
	FloorArea (Sq.Ft.)	Offices	Entrances	Age	AssessedValue ($'000)
	4790	4	2	8	1796
	4720	3	2	12	1544
	5940	4	2	2	2094
	5720	4	2	34	1968
	3660	3	2	38	1567
	5000	4	2	31	1878
	2990	2	1	19	949
	2610	2	1	48	910
	5650	4	2	42	1774
	3570	2	1	4	1187
	2930	3	2	15	1113
	1280	2	1	31	671
	4880	3	2	42	1678
	1620	1	2	35	710
	1820	2	1	17	678
	4530	2	2	5	1585
	2570	2	1	13	842
	4690	2	2	45	1539
	1280	1	1	45	433
	4100	3	1	27	1268
	3530	2	2	41	1251
	3660	2	2	33	1094
	1110	1	2	50	638
	2670	2	2	39	999
	1100	1	1	20	653
	5810	4	3	17	1914
	2560	2	2	24	772
	2340	3	1	5	890
	3690	2	2	15	1282
	3580	3	2	27	1264
	3610	2	1	8	1162
	3960	3	2	17	1447
Simple Regression
											SUMMARY OUTPUT
											Regression Statistics
											Multiple R	0.9683582089
											R Square	0.9377176208
											Adjusted R Square	0.9356415415
											Standard Error	115.5993039377
											Observations	32
											ANOVA
												df	SS	MS	F	Significance F
											Regression	1	6035851.90287359	6035851.90287359	451.6771673353	1.22547865240093E-19
											Residual	30	400895.972126411	13363.1990708804
											Total	31	6436747.875
		Yes, there is a linear relationship betwene both variables.										Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
											Intercept	162.6627672772	54.4785653123	2.9858122428	0.0055856115	51.4026942588	273.9228402955	51.4026942588	273.9228402955
											FloorArea (Sq.Ft.)	0.306732084	0.0144326187	21.2526978837	1.22547865240093E-19	0.2772567446	0.3362074235	0.2772567446	0.3362074235
											Since, the p-value of Floor area is less than 0.05, it is concluded that Floor area is significantly related to the Assessed value.

Purpose This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models. Resources:Microsoft Excel®, DAT565_v3_Wk5_Data_File Instructions:...

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