ASSIGNMENT

Assignment

Case 5.3 - Multiple Regression

5.1.

5.1 a.

ANOVA is not similar from both MANCOVA and MANOVA as ANOVA has only one dependent variable; however both MANCOVA and MANOVA have several dependent variables. MANOVA has only one predictor which is a categorical variable and has several dependent variables. In addition to this, MANCOVA is similar to MANOVA; however, it has several independent variables that is covariates.

5.1 b.

The given statement is correct as Repeated Meassure ANOVA is applied to categorical variable which is the case in the given statement that is demographics is used independent variables in the study.

5.1 c.

Yes, the statement is correct that is Multiple Factor MANOVA should be used by Maddox (1999) as there are several independent variables that are age and gender.

5.1 d.

Yes, the given statement is correct because Maddox (1999) should use the Repeated Meassure ANOVA as the predictors that are, age and gender are categorical variables.

5.1 e.

With reference to the statement, MANOVA should be used as both age and gender are categorical in nature.

5.1 f.

No, the given statement is not correct as the using 1, 2, 3, and 4 for AGE and, 0 and 1 for Gender; the multiple regression analysis should not be applied. The reason of this statement is that for multiple regression, the variables should be scale and there should be 1 dependent variable and should be two and more independent variables.

5.2.

5.2 a.

Stepwise regression is the method of multiple regression analysis in which the predictors or independent variables which are not significant are eliminated step by step. Thus, in the end only significant independent variables remains in the regression model that is the variables with level of significance less than 0.05.

5.2 b.

In accordance with Table 2, as the predictor that is Hours Worked Per Week and sales volume is not significant predictor, thus, it is important that another step of stepwise regression should be applied on the data.

5.2 c.

Multi-collinearity is a statistical observable fact that occurs in the multiple regression analysis, in which two or more independent variables are highly correlated with each other which affects the relationship with the independent variable.

5.2 d.

In multiple regression analysis, muli-colinearity should be eliminated from the independent variables because due to the interrelationship of the predictors, the relationship of independent variables with the dependent variables gets affected.

5.3.

5.3 a.

Wit reference to given article that is the relationship of training motivation to participation in training and development, the author chooses multiple regression analysis because the objectives of multiple regression analysis include the following:

Explaining the variance in the dependent variable Y. For this purpose, statistics R 2 is crucial;

Estimating the effect of each independent variable X on the dependent variable. The power to influence communicated a standardized regression coefficient that is beta. The influence of each independent variable is estimated by the controlled action of the other independent variables that come into model. In this particular study, multiple regression analysis through standardized regression coefficients (beta) also helps determine the relative strength of the different variables on the dependent ...