1. Lecture 9
Regression Analysis

2. Relationship between “Cause” and “Effect”t
Regression analysis establishes relationship between a dependent variable and independent variables
Relationship between “Cause” and “Effect”t
Relationship between variables

3. Usefulness of regression analysis
Regression analysis is a vary widely used tool for research.
It shows type and magnitude of relationship between two variables.

4. Example of Usefulness of Regression Analysis:
Shows for example whether there is any relationship between an increase in household income (Y) land an increase in consumption (C ).
Whether there is positive or negative relationship between Y and C. Whether if : Y
C or reverse
How much of an increase in income (Y) is spent on consumption ( C ).

5. Example of Usefulness of Regression Analysis
Regression is also used for prediction and forecasting,
Regression analysis allows to measure confidence or significance level of the findings.

6. Example of Usefulness of Regression Analysis
Increase in traffic jam (hours of non-movement) depends on Increase in number of cars in Dhaka City. (+ dependency)
A decrease in number of School drop-out depends on an increase I income of parents.(-ve dependency)
An increase in household income leads to an increase in household consumption.

7. Other Logical Examples of Positive and Negative Dependency

8. Forms of regression models
A regression model relates dependent variable Y to be a function/relation of independent variable X.
Symbolically, Y = f (Xi)
Where i = 1,2,3,4,…

9. Diagrammatic Representation of Regression Model
Consumption Expenditure(,000Tk)
Each dot represent sample data for Income and Expenditure for each sample household
100 90
130
Income of the Household (,000 Tk)
120

10. Consumption Expenditure ( C )
C = a + by
Regression analysis draw a mean /average line with equation C = a + b Y so that difference between sample data and estimated data is minimized.
Income of the Household (Y)
Does dotted line minimize deviations?

11. Deviations between sample value and the mean value
Mean value line

12. Diagrammatic Representation of Regression Equation
In mean or average line, square of the deviation ( C i) for each of the
sample from mean ( C )is minimized.
Why ?
Because simple sum of difference from mean is always zero.

13. Example C - C Sum is zero 1 -1 0 (C – C)**2 1 0
(C – C)**2
Sum of square is + number Y 10 8 9
Av Y is 9 C 6 7
Av C is 7
Dependent variable

14. Formula for Regression coefficient b when sum of square is minimized , b = (Ci – C) (Yi –Y) (Yi – Y) 2 i = 1,2, ….n

15. General Formula
If Y is dependent variable and X is independent variable e.g. Y = f (x) then
Regression coefficient =
Sum of (Xi –X) (Yi – Y)
Sum of (Xi –X)**2

16. Example : Given the following data C = f (Y), predict Consumption level for a household with annual income of 500 thousand Taka
Annual Income (Y)
(,000Tk)
100
150
200
250
300
Annual Expenditure
(C ) 80 90
110
120

17. Example : Given the following data, predict Consumption level for a household with annual income of 500 thousand Taka. (Fig in,000Tk)
Annual Income (Y)
Av Y = 200
Yi - Y
100
-100
150
-50
200
250 50
300
Annual Expenditure
(C )
Av C = 100
Ci - C 80
-20 90
-10
110 10
120 20

18. Example (Ci – C) (Yi –Y) = 2000 +500 + 0 + 500 + 2000 = 5000
= = 5000
(Yi – Y) 2 = = 25000
Therefore b = (Ci – C) (Yi –Y) / (Yi – Y) 2 = 0.2

19. Calculated Regression Equation Example
C = a + b Y
Or C = a Y or C = a Y
Or a = C -0.2 Y
Or a = x 200 = 100 – 40 = 60
Therefore C = Y

20. Calculated Regression Equation Example
C = Y
What kind of relationship between Y and C ?
How much consumption increases for Tk 1000 increase in income ?

21. C = 60 +0.2 Y What is consumption, when income is zero?
What is predicted consumption, when income is Tk 500,000?

22. Correlation : A measure of simple relationship
Correlation shows only associanship or relationship between two variables.
Whereas Regression analysis shows dependency relationship
Correlation between two variables ( for example Income and Expenditure) is measured by a formula shown as ;

23. Formula of Correlation coefficient r is (Ci – C) (Yi –Y) (Yi – Y) 2 (Ci – C)2

24. Formula of Correlation coefficient r in terms of regression coefficient r
(Yi – Y)**2
r = b
(Ci – C )**2

25. The End

26. Class Assignment
Given the following data, calculate correlation coefficient between Income and Expenditure. Also predict how much Consumption will increase for a 1000 Tk increase in household income?
Annual Income (Y)
(,000Tk)
110
160
210
260
310
Annual Expenditure
(C ) 75 85 95
105
115

27. The End