Regression Objectives Of Multiple Regression

1. Group Member:
1. Hasnain Khan
2. Zain Ahmed
3. Fahad Ullah
Topic:
Dependence techniques

2. Dependence Techniques
Are types of multivariate analysis techniques that
are used when
one or more of the variable can be identified as
dependent
variable can identified as independent

3. Definition Of Regression
Regression analyze is the set of statically
processes for estimating relation among variables it
includes the many techniques for modeling
analyzing server variables.
When the focus is on the relationship between
independent or dependent variable

4. Objectives Of Multiple Regression
In selecting suitable application of multiple
regression the
researcher must consider three primary issue
• appropriateness of the research problem
• specification of statistical relationship
• selection of the dependent and independent
variable

5. Objective Of Multiple Regression
 Its help one understand how to typical value of
dependent variable.
 Change when any one dependent variable while the
other dependent variable are held fixed
 In regression analysis its also of interest characterize
of the dependent variable around the prediction of the
regression function
 Regression analyze used for prediction and
forecasting

6. Multiple Regression
 Is a statistical tool used to derive the value of
criterion from several other independent or
predictor variables.
 It is the combination of multiple factor to asses
how and what extent they effect of certain
outcome

7. Model Of Multiple Regression
MOTIVATION
SOCAIL SUPPORT
INTELLIGENCE
JOB PERFORMANCE

8. Assumption
Assumptions: A Think That Is Accepted As A True As Certain
To Happen With Out Prove And The Action Taking On
Power Or Responsibilities

9. Assumption Of Multiple Regression
Assumptions for any specific value of the
dependent variable , the values of the y variable
are normally distributed.
Each of the variables have relationship
Independent variable are not correlated when the
assumption is
violated we call the condition multi- co linearity

10. Assumption Of Multiple Regression
The probability distribution is normal the error of
term
dependent of each other this is assumption often
violated when
the time is involve we call the condition of auto
correlation

11. Estimated regression model
 The sample of regression line provides an
estimate of the population regression line
 Estimated or predict value Y
 Estimated of the regression

12. Estimation Regression Model
Logistic relationship describe earlier in both
estimating the logistics model and establishing
The relation between the dependent and
independent variable.
Result is a unique transformation of dependent
variables
Which impact not only the estimations processes
but also the resulting co efficient of dependent
variable

13. Assessing Overall Model Fit
 Calculating discriminate z score for each
observation
 Evaluating group difference on the discriminate z
score
 Assessing group membership prediction accuracy

14. Interpreting Regression
 In simple or multiply linear regression the size of
the coefficient f
 or the dependent variables
 The size of co efficient positive or negative

15. Interpreting Regression
 Give you the direction of effect
 The size of coefficient for each independent
variable gives you size of effect that variable is
having own your depended variable

16. Interpreting Regression Variable
The sign of regression co efficient tell you whether
there is a
positive or negative co relation between each
independent
variable the dependent variable