Normality Linearity Multicollinearity Prerequisites Test Analysis

The steps are as follows: 1 Empulate the Regression Line Equation with One Predictor Formula: Notes: Y = Criterion X = Predictor a = Coefficient Predictor Numbers K = Constant Numbers Sutrisno Hadi, 2004: 1-2 2 Empulate simple correlation between X 1 and X 2 with Y Formula: Notes: r xy = The correlation coefficient between x and y xy = Number of products between x and variable y x = Total score of predictor x y = Total score of predictor y Sutrisno Hadi, 2004:4 3 Empulate the coefficient determinant R 2 between predictor of X 1 with Y and X 2 with Y Formula: Notes: r x1y 2 = Determinant coefficient between y with x 1 r x2y 2 = Determinant coefficient between y with x 2 a 1 = Coefficient predictor of x 1 a 2 = Coefficient predictor of x 2 x 1 y = Total score of questions x 1 with y x 2 y = Total score of questions x 2 with y y 2 = Sum of squares criterion y Sutrisno Hadi, 2004:4 4 Significance Testing using T-test T-test conducted to test the constants significance of each independent variable that will affect the dependent variable. The formula is: Notes: t = t emp r = Correlation coefficient n = Number of samples r 2 = The square of correlation coefficient Sugiyono, 2010:230 The conclusions making is to compare the t emp score with t table . If temp is greater than or equal to the ttable with a significance level of 5, the variable is significant. Otherwise, if the score of t emp is smaller than t table , the variable is not significant. b. Multiple Regression Analysis Multiple regression used to determine the influences of Moving Class Implementation and Accounting Classrooms Facilities on Students Learning Motivation in Accounting Learning Hypothesis 3. The steps in Regression Analysis are: 1 Make a two-predictor regression equation Formula: Notes: Y = Criterion X 1 X 2 = Predictor 1 and 2 a 1 a 2 = Coefficient Predictors 1 and 2 K = Constant Numbers Sutrisno Hadi, 2004:18 2 Empulate the determinant coefficient between the criterion Y with predictors X 1 and X 2. Formula: Notes: R y1,2 = correlation coefficient between X 1 and X 2 with Y 1 a = predictor coefficients of X 1 2 a = predictor coefficients of X 2 Σx 1 y = number of products between X 1 with Y Σx 2 y = number of products between X 2 with Y Σy 2 = sum of squares criterion Y Sutrisno Hadi, 2004: 22