A short project work

1. Statistical Analysis of Rent Paid by U.S.
Households
Group Mentor :
Prof. Manish Thakkar
Group Members:
Riddhima Kartik (20151037)
Rishabh Surana (20151038)

2. Objective :
To find the extent of relation/dependency of
variables(dependent and independent) by using
various statistical tools like Correlation, Regression
and Independence test using SPSS.

3. Variables Involved
Dependent Variables :
 Rent
Independent Variables :
 Household Income
 Electricity Cost
 Gas Cost
 Rooms Per House
 Vehicle Per Household

4. Software Used :
SPSS (Statistical Package for the Social Sciences)
Statistical Methods Used :
 Normality Test
 Correlation with Scatter plot.
 Multiple Regression Analysis

5. Condition to Apply Parametric Statistical
Methods-
 Parametric statistical methods(ANOVA and Linear Regression etc.) requires that
dependent must be normally distributed.
 To check Normality by SPSS:
Steps Involved : Analysis->Descriptive->Explore->Plot->Normality plot-> ok

6. Operations Involved
Test of Normality
Considering:
1. No. of Person
2. No. of Vehicles
3. No. of Rooms

7. Normality Test w.r.t. No. of Persons

8. Normality Test w.r.t. No. of Rooms

9. Normality Test w.r.t. No. of Vehicles

10. Correlation Test :
Hypothesis considered:
 Hypothesis 1
 H0 : There is no significant correlation between Rent and Household Income.
 Ha : There is significant correlation between Rent and Household Income.
 Hypothesis 2
 H0 : There is no significant correlation between Rent and Electricity Cost.
 Ha : There is significant correlation between Rent and Electricity Cost.
 Hypothesis 3
 H0 : There is no significant correlation between Rent and Gas Cost.
 Ha : There is significant correlation between Rent and Gas Cost.

11. Correlation between:
 Rent and Household Income = .000
Rent and Electricity Cost = .009
 Rent and Gas Cost = .013
Therefore, we reject all three null
Hypothesis and can say that these
factors have significant impact
over rent
Confidence Level taken is 95%
To check Strength of Correlation we can see Scatter plot ->

12. Graphical analysis : Scatter Plot (Household Income)

13. Graphical analysis : Scatter Plot (Electricity Cost)

14. Graphical analysis : Scatter Plot (Gas Cost)

15. Strength with Household Income: Moderate, not very strong.
Strength with Electricity Cost: Weak
Strength with Gas Cost: Weak
Steps Involved :-(Graph->chart-builder->drag the simple scatter graph):- If all
the scatter points are in same straight line then we have strong relationship
between them.

16. Multiple Regression :
Ho : Variation in Y(Dependent variable(Rent)) is unrelated to variation in
X(Independent variable(Household Income, Electricity Cost, Gas Cost)).
Or
Ho: Correlation between X(Household Income, Electricity Cost, Gas Cost) and
Y(Rent) is 0.
Ha: Correlation between X(Household Income, Electricity Cost, Gas Cost) and
Y(Rent) is not 0.

17. Regression Summary

18. Summary Results

19. Result
R^2 value is 28.6, which means that 28.6% variation in Rent is due to
Household Income, Electricity Cost, Gas Cost and rest because of other
factors.
Constant = 1168.89 -> This much will be the basic Rent that households have
to pay irrespective of other factors.
p-value for all the variables are quite lower than our significance level that is
0.05. So, Null hypothesis is rejected and we can conclude that :
Conclusion : Correlation between X(Household Income, Electricity Cost, Gas
Cost) and Y(Rent) is not 0.
Y = 1168.89 + .003A + 2.415B + 4.481C

20. THANK YOU