Harvard Business School Floor Area vs Assessed Value Regression Worksheet

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?

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Use Excel’s Analysis ToolPak to conduct a regression analysis ofFloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue?

Construct a scatter plot in Excel with Ageas the independent variable and AssessmentValueas 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?
  • In addition, Constructa 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 ?=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?

    FloorArea (Sq.Ft.)
    4790
    4720
    5940
    5720
    3660
    5000
    2990
    2610
    5650
    3570
    2930
    1280
    4880
    1620
    1820
    4530
    2570
    4690
    1280
    4100
    3530
    3660
    1110
    2670
    1100
    5810
    2560
    2340
    3690
    3580
    3610
    3960
    Offices
    4
    3
    4
    4
    3
    4
    2
    2
    4
    2
    3
    2
    3
    1
    2
    2
    2
    2
    1
    3
    2
    2
    1
    2
    1
    4
    2
    3
    2
    3
    2
    3
    Entrances
    2
    2
    2
    2
    2
    2
    1
    1
    2
    1
    2
    1
    2
    2
    1
    2
    1
    2
    1
    1
    2
    2
    2
    2
    1
    3
    2
    1
    2
    2
    1
    2
    Age
    8
    12
    2
    34
    38
    31
    19
    48
    42
    4
    15
    31
    42
    35
    17
    5
    13
    45
    45
    27
    41
    33
    50
    39
    20
    17
    24
    5
    15
    27
    8
    17
    AssessedValue ($’000)
    1796
    1544
    2094
    1968
    1567
    1878
    949
    910
    1774
    1187
    1113
    671
    1678
    710
    678
    1585
    842
    1539
    433
    1268
    1251
    1094
    638
    999
    653
    1914
    772
    890
    1282
    1264
    1162
    1447

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