Selling Price        Living Area (Sq Feet)       No. Bathrooms No Bedrooms    Age (Years) $240,000              2,022     2.5          3              20 $235,000              1,578     2              3              20 $500,075              3,400     3              3              20 $240,000              1,744     2.5          3              20 $270,000              2,560     2.5          3              20 $225,000              1,398     2.5          3              20 $280,000              2,494     2.5          3              20 $225,000              2,208     2.5          4              20 $248,220              2,550     2.5          3              20 $275,000              1,812     2.5          2              20 $137,000              1,290     1              2              20 $150,000              1,172     2              2              20 $649,000              4,128     3.5          3              20 $195,000              1,816     2.5          3              97 $373,200              2,628     2.5          4              20 $169,450              1,254     2.5          3              20 $144,200              1,660     1.5          4              20 $189,900              1,850     1.5          3              20 $166,000              1,258     2              3              20 $160,000              1,219     2              3              20 $327,355              1,850     2.5          3              20 $247,000              2,103     2.5          3              20 $318,000              1,806     2.5          3              20 $341,000              1,674     1.5          2              17 $288,650              2,242     2.5          3              20 $157,000              1,408     1.5          3              20 $449,000              3,457     2.5          3              21 $142,000              1,728     1.5          3              21 $389,000              2,354     2.5          3              21 $476,000              2,246     2.5          3              21 $249,230              1,902     2.5          2              21 $139,900              1,178     1              3              21 $301,900              2,896     3.5          4              21 $425,000              2,457     3              3              41 $121,000              936         1              3              50 $150,000              934         1              2              21 $138,000              1,279     1              3              21 $199,900              1,888     2              3              26 $145,000              1,686     1.5          4              21 $465,000              2,310     3              2              21 $158,000              1,200     1.5          3              21             Develop a multiple linear regression model...

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Selling Price        LivingArea (Sq Feet)       No. Bathrooms NoBedrooms    Age (Years)

$240,000             2,022    2.5         3             20

$235,000             1,578    2             3             20

$500,075             3,400    3             3             20

$240,000             1,744    2.5         3             20

$270,000             2,560    2.5         3             20

$225,000             1,398    2.5         3             20

$280,000             2,494    2.5         3             20

$225,000             2,208    2.5         4             20

$248,220             2,550    2.5         3             20

$275,000             1,812    2.5         2             20

$137,000             1,290    1             2             20

$150,000             1,172    2             2             20

$649,000             4,128    3.5         3             20

$195,000             1,816    2.5         3             97

$373,200             2,628    2.5         4             20

$169,450             1,254    2.5         3             20

$144,200             1,660    1.5         4             20

$189,900             1,850    1.5         3             20

$166,000             1,258    2             3             20

$160,000             1,219    2             3             20

$327,355             1,850    2.5         3             20

$247,000             2,103    2.5         3             20

$318,000             1,806    2.5         3             20

$341,000             1,674    1.5         2             17

$288,650             2,242    2.5         3             20

$157,000             1,408    1.5         3             20

$449,000             3,457    2.5         3             21

$142,000             1,728    1.5         3             21

$389,000             2,354    2.5         3             21

$476,000             2,246    2.5         3             21

$249,230             1,902    2.5         2             21

$139,900             1,178    1             3             21

$301,900             2,896    3.5         4             21

$425,000             2,457    3             3             41

$121,000             936        1             3             50

$150,000             934        1             2             21

$138,000             1,279    1             3             21

$199,900             1,888    2             3             26

$145,000             1,686    1.5         4             21

$465,000             2,310    3             2             21

$158,000             1,200    1.5         3             21

  1.            Develop a multiple linear regression model to predict the price ofa house using the square feet of living area, number of bedrooms,number of bathrooms, and age as the predictor variables.
  1.            Write the reqression equation.
  2.            Discuss the statistical significance of the model as a whole usingthe appropriate regression statistic at a 95% level ofconfidence.
  3.             Discuss the statistical significance of the coefficient for eachindependent variable using the appropriate regression statistics ata 95% level of confidence.
  4.            Discuss the statistical significance of the coefficient for eachindependent variable using the appropriate regression statistics ata 90% level of confidence.
  1.            Develop a multiple linear regression model to predict the price ofa house using the square feet of living area, number of bedrooms,and number of bathrooms as the predictor variables.
  1.            Write the reqression equation.
  2.            Discuss the statistical significance of the model as a whole usingthe appropriate regression statistic at a 95% level ofconfidence.
  3.             Discuss the statistical significance of the coefficient for eachindependent variable using the appropriate regression statistics ata 95% level of confidence.
  4.            Interpret the coefficient for each independent variable.
  5.            What percentage of the observed variation in housing prices isexplained by the model?
  6.             Determine the prediction interval of a house with 3,000 square feetof living area, 3 bedrooms, and 2.5 bathrooms, and comment on theprediction interval.
  1.            In Case Study No.1 you ran a simple linear regression model forpredicting the price of a house based upon its living area insquare feet using a 95% level of confidence. Rerun that simplelinear regression model for predicting the price of a house basedupon its living area in square feet using the Case study 2 datausing a 95% level of confidence. Compare the regression statisticsfor this simple linear regression model with the statistics for thepreceding multiple linear regression model using square feet ofliving area, number of bedrooms, and number of bathrooms as thepredictor variables. Which model is the preferred model at a 95%level of confidence? Use the Significance F values, p-values forthe independent variables, R-squared or Adjusted R-squared values(as appropriate), and standard errors to explain yourselection.

Prepare a single Microsoft Excel file to document yourregression analyses. Prepare a single Microsoft Word document thatoutlines your responses for each portion of the case study.

Answer & Explanation Solved by verified expert
3.9 Ratings (632 Votes)

data

selling price sq-feet no. of bathroom no. of bedroom age
240000 2022 2.5 3 20
235000 1578 2 3 20
500075 3400 3 3 20
240000 1744 2.5 3 20
270000 2560 2.5 3 20
225000 1398 2.5 3 20
280000 2494 2.5 3 20
225000 2208 2.5 4 20
248220 2550 2.5 3 20
275000 1812 2.5 2 20
137000 1290 1 2 20
150000 1172 2 2 20
649000 4128 3.5 3 20
195000 1816 2.5 3 97
373200 2628 2.5 4 20
169450 1254 2.5 3 20
144200 1660 1.5 4 20
189900 1850 1.5 3 20
166000 1258 2 3 20
160000 1219 2 3 20
327355 1850 2.5 3 20
247000 2103 2.5 3 20
318000 1806 2.5 3 20
341000 1674 1.5 2 17
288650 2242 2.5 3 20
157000 1408 1.5 3 20
449000 3457 2.5 3 21
142000 1728 1.5 3 21
389000 2354 2.5 3 21
476000 2246 2.5 3 21
249230 1902 2.5 2 21
139900 1178 1 3 21
301900 2896 3.5 4 21
425000 2457 3 3 41
121000 936 1 3 50
150000 934 1 2 21
138000 1279 1 3 21
199900 1888 2 3 26
145000 1686 1.5 4 21
465000 2310 3 2 21
158000 1200 1.5 3 21

a)

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.890990637
R Square 0.793864316
Adjusted R Square 0.770960351
Standard Error 58681.22233
Observations 41
ANOVA
df SS MS F Significance F
Regression 4 4.77413E+11 1.19353E+11 34.66056288 6.90482E-12
Residual 36 1.23965E+11 3443485854
Total 40 6.01378E+11
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 91934.57151 57728.62547 1.592530062 0.120009481 -25144.50748 209013.6505
sq-feet 129.2855885 20.82894503 6.207015682 3.67893E-07 87.04252999 171.5286469
no. of bathroom 39969.87177 21376.02784 1.869845608 0.069655054 -3382.722058 83322.4656
no. of bedroom -54284.86462 17629.8245 -3.079149461 0.003959575 -90039.80593 -18529.92331
age -359.3853607 718.6420902 -0.500089496 0.620055382 -1816.859073 1098.088351

y^ = 91934.5715 + 129.2856 * sq-feet + 39969.8718 * no.of bathroom -54284.8646*no, of bedroom -359.3854*age

b)

p-value = 6.90482E-12 < 0.05

hence the model is significant

c)

if p-value < alpha

the variable is significant

here sq-feet and no. of bedroom have p-value < 0.05

hence they are significant at 95 % level

d)

apart from sq-feet and no. of bedroom ,no. of bathroom have p-value < 0.10

hence these three are significant at 90% confidence level

e) R^2 = 0.793864316

hence 79.39%


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