Presentation on theme: "Regression Analysis Lecture 9. Regression analysis establishes relationship between a dependent variable and independent variables Relationship between."— Presentation transcript:
Regression Analysis Lecture 9
Regression analysis establishes relationship between a dependent variable and independent variables Relationship between Cause and Effecttt Relationship between variables
Usefulness of regression analysis Regression analysis is a vary widely used tool for research. It shows type and magnitude of relationship between two variables.
Example of Usefulness of Regression Analysis : 1.Shows for example whether there is any relationship between an increase in household income (Y) land an increase in consumption (C ). 2.Whether there is positive or negative relationship between Y and C. Whether if : Y C or reverse 1.How much of an increase in income (Y) is spent on consumption ( C ).
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.
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.
Other Logical Examples of Positive and Negative Dependency
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,…
Diagrammatic Representation of Regression Model Consumption Expenditure(,000Tk) Income of the Household (,000 Tk) 0 Each dot represent sample data for Income and Expenditure for each sample household
Consumption Expenditure ( C ) Income of the Household (Y) 0 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. Does dotted line minimize deviations?
Deviations between sample value and the mean value Mean value line
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.
Example Y1089 Av Y is 9 C867 Av C is 7 Dependent variable C - C Sum is zero (C – C)**211 0 Sum of square is + number
Formula for Regression coefficient b when sum of square is minimized, b = (Ci – C) (Yi –Y) (Yi – Y) 2 i = 1,2, ….n
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
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) Annual Expenditure (,000Tk) (C )
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 Annual Expenditure (C ) Av C = 100 Ci - C
Example (Ci – C) (Yi –Y) = = 5000 (Yi – Y) 2 = = Therefore b = (Ci – C) (Yi –Y) / (Yi – Y) 2 = 0.2
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
Calculated Regression Equation Example C = Y What kind of relationship between Y and C ? How much consumption increases for Tk 1000 increase in income ?
C = Y What is consumption, when income is zero? What is predicted consumption, when income is Tk 500,000?
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 ;
Formula of Correlation coefficient r is (Ci – C) (Yi –Y) (Yi – Y) 2 (Ci – C) 2
Formula of Correlation coefficient r in terms of regression coefficient r (Yi – Y)**2 (Ci – C )**2 r = b
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) Annual Expenditure (,000Tk) (C ) Class Assignment