Panama Canal Expansion Project | Report

Running head: Business analysis project 1
Panama Canal Expansion Project
Student name:
Course code:
Tutor:
1

Business analysis project 2
Table of Contents
1.0 Introduction................................................................................................................................4
2.0 Data analysis and results............................................................................................................4
2.1 Population regression model..................................................................................................4
2.2 Sample regression model.......................................................................................................5
2.3 Sample regression equation...................................................................................................5
2.4 Statistical significance of the regression model.....................................................................6
2.5 Interpretation of the coefficients of X1 to X5.........................................................................6
2.5.1 Population regression model...........................................................................................6
2.5.2 Sample regression model.................................................................................................6
2.6 Plots to check existence of outliers........................................................................................7
2.6.1 Lot size residual plot.......................................................................................................7
2.6.2 Bedroom residual plot.....................................................................................................7
2.6.3 Bathroom residual plot....................................................................................................8
2.6.4 Basement residual plot....................................................................................................8
2.6.5 Air condition residual plot...............................................................................................8
2.7 Heteroscedasticity test............................................................................................................9
2.7.1 Relationship between residual and lot size.........................................................................9
2.7.2 Relationship between residual and number of bedrooms...................................................9
2.7.3 Relationship between residual and number of bathrooms.............................................10
2.7.4 Relationship between residual and number of basement..............................................10
2.7.5 Relationship between residual and number of air condition.........................................11
2.8 Test for multicollinearity.....................................................................................................11
3.0 Conclusion...............................................................................................................................12
References.....................................................................................................................................13
Appendices....................................................................................................................................14
Executive summary

Business analysis project 3
The objective of this research Panama Canal expansion project report was to build a
model that would be used to predict the house prices using lot size, number of bedrooms, and
number of bathrooms, basement and presence of air conditioners. Multiple linear regression was
employed to come up with the model. The research study found and made several conclusions.
The models generated were very significant and could be used to predict the house prices. The
study also found out that there was a case of multicollinearity between number of bathrooms and
number of bedrooms. This could compromise the robustness of the model and therefore
recommended the removal of one of them when generating the model. It was also concluded that
all the independent variables were significant predictors of house prices except the basement.
1.0 Introduction
In determining house prices, several variables come into play. Some of the variables
include lot size, number of bedrooms, and number of bathrooms, basement and presence of air

Business analysis project 4
conditioners in the house (Abelson and Chung, 2015, pp. 265-280) and (Dongsheng and Zhong,
2010, pp. 3-7) The objective of this research report was to build a model that would be used to
predict the house prices using the mentioned variables. The results of the analysis are shown in
the next sections.
2.0 Data analysis and results
2.1 Population regression model
Result
SUMMARY
OUTPUT
Regression Statistics
Multiple R
0.75627
4
R Square 0.57195
Adjusted R
Square
0.56798
7
Standard
Error
17551.0
5
Observations 546
ANOVA
df
SS
MS
F
Significan
ce F
Regression 5
2.22262E
+11
44452310
359
144.30
73
4.65196E-
97
Residual 540
1.66341E
+11
30803932
2.3
Total 545
3.88603E
+11
Coefficie
nts Std error t Stat
P-
value
Lower
95%
Upper
95%
Intercept
50.2677
8
3475.938
24
0.014461
644
0.9884
6
-
6777.749
81
6878.
28
lot size
4.73635
4
0.361155
17
13.11445
795
2.63E-
34
4.026913
148
5.445
79
#bedroom
4660.11
7
1109.449
99
4.200385
079
3.12E-
05
2480.750
493
6839.
48
#bath
17594.4
9
1645.957
45
10.68951
621
2.53E-
24
14361.22
473
20827
.7
basement
6081.04
7
1587.200
27
3.831303
988
0.0001
4
2963.203
252
9198.
89
air condition 16130.4 1680.554 9.598277 3.01E- 12829.19 19431