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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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:


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?

dat565v3wk5datafile-wk5-ishfgyio.xlsx
Answered Same DayMay 08, 2021

Answer To: Purpose This assignment provides an opportunity to develop, evaluate, and apply bivariate and...

Shakeel answered on May 09 2021
146 Votes
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.
                                            SUMMARY...
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