Descriptive Statistics Classic Assumption

53 tests. One of the easiest ways to see the normality of residuals is to look at a histogram graph comparing observational data with the distribution of near-normal distribution. Normal distribution will create a straight line diagonal and plotting residual data will be compared with the diagonal line. If the residual data distribution is normal, then the line that describes the actual data will follow the diagonal line Ghazali, 2005:110. b. Multicolinearity Test According to Ghozali 2005:91 stated that multicolinearity test aimed to test whether regression model is founded correlation among independent variables. To detect the presence or least multicolinearity in the regression model is as follows: 1 The value of R 2 is generated by an empirical regression estimates are very high, but individually variable, independent variables are many that do not affect the dependent variable. 2 Analyzing the correlation matrix of variables-the independent variable. If there is a correlation between independent variables is quite high usually above 0.90, then this is an indication of multicollinearity. If below 0.90, the absence of multicollinearity. 3 Multicollinearity also can be seen from the value of tolerance and Variance Inflation Factor VIF. Both these measures indicate each independent variable that is explained by other independent variables. Tolerance measures the independent variables were selected that are not explained by other independent variables. Low tolerance value 54 equals to a high VIF value because VIF = 1Tolerance. Value commonly used to indicate the presence multicollinearity is tolerance value, 0.10 or equal to the value of VIF 10. Each investigator must determine the level of colinearity that it still can be tolerated. For example, the value of tolerance = 0.10 equal to the level colinearity 0.95 Ghozali, 2005:91. c. Heteroscedasticity Test Imam Ghozali 2005:105 stated that heteroscedasticity test aimed to test the regression model. There are differences on forms of observation from one observation to others. The prediction of heteroscedasticity in a certain model could be seen from the picture of its scatterplot model. In the scatterplot picture when it says there is no heteroscedasticity if: 1 The dot for the data is spreading above and below or around the number of 0. 2 The dots are not just grouping only above or below the number of 0. 3 The dots cannot spread like a wide wave and then narrow and again widening. 4 The spread should not have a pattern. One of the heteroscedasticity tests is the Glejser test. Glejser test is conducted by the regression way between independent variables and the value of absolute residual. If significance value between independent 55 variables and absolute residual more than 0.05, so it can be said that there is no heteroscedasticity problem Priyatno, 2012:158.

4. Multiple Regression Analysts

According to Cooper 2006:617, a multiple regression is statistical tool to develop a self – weighting estimating equation that predicts values for a dependent variable from the value of independent variables. A multiple regression is used as a descriptive tool in three types of situation. Firstly, it is often used to develop a self-weighting estimating equation, which predicts a value for a criterion variable from the values for several predictor variables. Secondly, a descriptive application of multiple linear regression calls fort- confounding variables to better evaluate the contribution of other variables. Thirdly, the use of a multiple linear regression is to test and explain casual theories. In order to count multiple linear regression for this research, the researcher using SPSS 20 for windows series. By using the formula of similarity as follows: Y = α + β1X1 + β2X2 + β3X3 + ℮. Notes: Y = Dependent Variable Purchase Intentions Α = Constant intercept Y if X = 0 β1… β4 = Coefficient regression that shows the numbers increase or decrease in dependent of variables based on the relationship of 56 independent variable. X 1 = Independent Variable Celebrity Athlete Endorser X 2 = Independent Variable Brand Awareness X 3 = Independent Variable Brand Association X 4 = Independent Variable Brand Personality E = Standard errors From the counting with SPSS 20 gain the information and explanation on the coefficient determination, F test, and T test to answer the formulation of the problems. The following explanations are connecting the problems above: a. The coefficient Determination R 2 The coefficient of determination R 2 essentially measures how far the ability of models to explain variation in the dependent variable. The value determination of coefficient is between zero and one. The R 2 is small means that the ability of independent variables in explaining variations in the dependent variable is very limited. Each additional independent variable then would increase R 2 , no matter whether these variables affect the dependent variable or not. Therefore, this study uses the R 2 that have been adapted or adjusted for the variables used. The adjusted R 2 value can rise or fall if an independent variable added into the model Ghazali, 2005.