Compile logistic1 Idrees waris IUGC

LOGISTIC REGRESSION IDREES WARIS 3095

LOGISTIC REGRESSION ,[object Object]

WHY WE USE LOGISTIC ? ,[object Object],[object Object],[object Object],[object Object]

TYPES OF LOGISTIC REGRESSION ,[object Object],[object Object],[object Object],[object Object]

BINARY LOGISTIC REGRESSION EXPRESSION Y = Dependent Variables ß ˚ = Constant ß 1 = Coefficient of variable X 1 X 1 = Independent Variables E = Error Term BINARY

STAGE 1: OBJECTIVES OF LOGISTIC REGRESSION ,[object Object],[object Object],DECISION PROCESS

STAGE 2: RESEARCH DESIGN FOR LOGISTIC REGRESSION

[object Object],[object Object],[object Object],[object Object]

4. SAMPLE SIZE ,[object Object],[object Object],[object Object],[object Object]

6. SAMPLE SIZE PER CATEGORY OF THE INDEPENDENT VARIABLE ,[object Object]

STAGE 3 ASSUMPTIONS ,[object Object],[object Object],[object Object]

STAGE 4: 1 . ESTIMATION OF LOGISTIC REGRESSION MODEL ASSESSING OVERALL FIT ,[object Object],[object Object]

3. TRANSFORMING THE DEPENDENT VARIABLE ,[object Object],[object Object]

WHAT IS P? p = probability (or proportion)

What is the p of success or failure? Failure Success Total 1 - p p (1 - p ) + p = 1

What is the p of success or failure? Failure Success Total 250 750 = 1000

What is the p of success or failure? Failure Success Total 250/1000 750/1000 = 1000/1000

What is the p of success? Failure Success Total .25 .75 1

What is the p of success? Failure Success Total .25 = 1 - p .75 = p 1 = (1 - p ) + p

WHAT ARE ODDS? ,[object Object],[object Object],[object Object],[object Object]

What are the odds of success? ,[object Object],[object Object],[object Object],Failure Success Total .25 = (1 - p ) .75 = p 1 = (1 - p ) + p

WHAT IS AN ODDS RATIO? ,[object Object],[object Object],[object Object]

HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES Group Failure Success Total A (Male) 182 368 550 B (Female) 75 375 450 250 750 1000

HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES Group Failure Success Total A (Male) 182/550 368/550 550/500 B (Female) 75/450 375/450 450/450 250 750 1000

HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES Group Failure Success Total A (Male) .33 .67 1 B (Female) .17 83 1 250 750 1000

HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES Group Failure Success Total A (Male) (1 - p A ) = .33 p A = .67 1 B (Female) (1 - p B ) = .17 p B = .83 1 250 750 1000

HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES ,[object Object],[object Object],Group Failure Success Total A (Male) (1 - p A ) = .33 p A = .67 1 B (Female) (1 - p B ) = .17 p B = .83 1

HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES ,[object Object],[object Object],Group Failure Success Total Male .33 .67 1 Female .17 .83 1

HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES ,[object Object],[object Object],[object Object],Group Failure Success Total Male .33 .67 1 Female .17 .83 1

[object Object],[object Object],[object Object],[object Object],How can we compare the odds (ω) of males versus females

4. ESTIMATING THE COEFFICIENTS ,[object Object],[object Object]

STAGE 5 INTERPRETATION OF THE RESULTS

It is calculating by taking by logarithm of the odd. Odd is less then 1.0 will have negative logit value ,odd ratios have a greater the 1.0 will have positive ,[object Object]

Compile logistic1 Idrees waris IUGC

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Compile logistic1 Idrees waris IUGC