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Multiple Linear Regression Using STATCAL (R), SPSS & Eviews

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Multiple Linear Regression Using STATCAL (R), SPSS & Eviews Multiple Linear Regression Using STATCAL (R), SPSS & EViews Prana Ugiana Gio Rezzy Eko Caraka Robert Kurniawan Sunu Widianto Download STATCAL in www.statcal.com Citations APA Gio, P. U., Caraka, R. E., Kurniawan, R., & Widianto, S. (2019, January 24). Multiple Linear Regression in STATCAL (R), SPSS and EViews. Retrieved from osf.io/preprints/inarxiv/krx6y MLA Gio, Prana U., et al. “Multiple Linear Regression in STATCAL (R), SPSS and Eviews.” INA-Rxiv, 24 Jan. 2019. Web. Chicago Gio, Prana U., Rezzy E. Caraka, Robert Kurniawan, and Sunu Widianto. 2019. “Multiple Linear Regression in STATCAL (R), SPSS and Eviews.” INA-Rxiv. January 24. osf.io/preprints/inarxiv/krx6y. i CONTENT 1.1 Data 1.2 Input Numeric Data in STATCAL 1.3 Multiple Linear Regression with STATCAL 1.4 STATCAL's Result 1.4.1 STATCAL’s Result: Normality Assumption Test Using Residual Data 1.4.2 STATCAL’s Result: Test of Multicolinearity 1.4.3 STATCAL’s Result: Test of Homoscedasticity Assumption 1.4.4 STATCAL’s Result: Test of Non-Autocorrelation Assumption 1.4.5 STATCAL’s Result: Multiple Linear Regression 1.4.6 STATCAL’s Result: Residual Check 1.5 Comparison with SPSS 1.6 Comparison with EViews ii In this article, we will explain step by step how to perform multiple linear regression with STATCAL. Beside that, we will compare STATCAL’s result with other software such as SPSS and EViews. 1.1 Data Table 1.1.1 is presented data of 10 persons based on score of variable Performance (풀), Motivation (푿ퟏ) and Stress (푿ퟐ). Table 1.1.1 Person Performance (푌) Motivation (푋1) Stress (푋2) 1 87 89 32 2 75 73 14 3 79 79 15 4 94 81 17 5 78 86 32 6 65 67 12 7 78 74 22 8 77 77 23 9 65 68 35 10 35 62 53 Based on the data in Table 1.1.1, variable Performance (풀) is dependent variable, while Motivation (푿ퟏ) and Stress (푿ퟐ) are independent variables. 1.2 Input Numeric Data in STATCAL Input numeric data in STATCAL as in Figure 1.2.1 until Figure 1.2.3. 1 Figure 1.2.1 Input Numeric Data Figure 1.2.2 Giving Name of Each Variable Figure 1.2.3 Your Numeric Data Must be Appeared in “Your Numeric Data” Part 2 1.3 Multiple Linear Regression with STATCAL To perform multiple linear regression with STATCAL, choose Statistics => Linear Regression (Ordinary Least Squares Method) (Figure 1.3.1). In Figure 1.3.2, variable Performance (풀) is moved to right of dependent variable box, while Motivation (푿ퟏ) and Stress (푿ퟐ) are moved to right of independent variable box. Figure 1.3.1 Linear Regression Menu in STATCAL Figure 1.3.2 Selection of Variable 3 1.4 STATCAL’s Result Result of multiple linear regression based on STATCAL can be seen in Result part. 1.4.1 STATCAL’s Result: Normality Assumption Test Using Residual Data Figure 1.4.1.1 Result of Normality Test Based on Kolmogorov-Smirnov Test Using Residual Data (Aymptotic Approach) Figure 1.4.1.2 Result of Nomal Q-Q Plot Using Residual Data 4 Figure 1.4.1.3 Result of Normality Test Based on Kolmogorov-Smirnov Test Using Residual Data (Exact Approach) Figure 1.4.1.4 Result of Normality Test Based on Jarque-Bera Test Using Residual Data Figure 1.4.1.5 Result of Normality Test Based on Shapiro-Wilk Test Using Residual Data 5 Figure 1.4.1.6 Result of Normality Test Based on Anderson-Darling Test Using Residual Data 1.4.2 STATCAL’s Result: Test of Multicolinearity Figure 1.4.2.1 Result of Multicolinearity Test Based on Variance Inflaction Factor (VIF) Figure 1.4.2.2 Result of Multicolinearity Test Based on Correlation Matrix (Pearson Correlation) 6 1.4.3 STATCAL’s Result: Test of Homoscedasticity Assumption Figure 1.4.3.1 Result of Homoscedasticity Assumption Test Based on Glejser Test Figure 1.4.3.2 Result of Homoscedasticity Assumption Test Based on Park Test Figure 1.4.3.3 Result of Homoscedasticity Assumption Test Based on Koenker-Bassett Test 7 Figure 1.4.3.4 Result of Homoscedasticity Assumption Test Based on Breusch-Pagan Test 1.4.4 STATCAL’s Result: Test of Non-Autocorrelation Assumption Figure 1.4.4.1 Result of Non-Autocorrelation Assumption Test Based on Durbin-Watson Test 8 1.4.5 STATCAL’s Result: Multiple Linear Regression Figure 1.4.5.1 Result of Multiple Linear Regression 1.4.6 STATCAL’s Result: Residual Check Figure 1.4.6.1 Residual Check 9 1.5 Comparison with SPSS Given SPSS’s result for multiple linear regression. We use SPSS version 17. Figure 1.5.1 Data in SPSS Figure 1.5.2 Normality Assumption Test Result Based on Kolmogorov-Smirnov Test Using Residual Data (Asymptotic & Exact Approach) 10 Figure 1.5.3 Result of Multicolinearity Test Based on Variance Inflaction Factor (VIF) Figure 1.5.4 Result of Non-Autocorrelation Assumption Test Based on Durbin-Watson Test Figure 1.5.5 Result of Multiple Linear Regression 11 1.6 Comparison with EViews Given EViews’s result for multiple linear regression. We use EViews version 7. Figure 1.6.1 Data in EViews 4 Series: Residuals Sample 1 10 Observations 10 3 Mean -2.18e-15 Median -1.022574 Maximum 8.320841 2 Minimum -5.231726 Std. Dev. 5.213715 Skewness 0.547872 Kurtosis 1.828112 1 Jarque-Bera 1.072490 Probability 0.584941 0 -7.5 -5.0 -2.5 0.0 2.5 5.0 7.5 10.0 Figure 1.6.2 Normality Assumption Test Result Based on Jarque-Bera Test Using Residual Data 12 Figure 1.6.3 Result of Multicolinearity Test Based on Variance Inflaction Factor (VIF) Figure 1.6.4 Result of Multicolinearity Test Based on Correlation Matrix (Pearson Correlation) Figure 1.6.5 Result of Homoscedasticity Assumption Test Based on Glejser Test 13 Figure 1.4.3.2 Result of Homoscedasticity Assumption Test Based on Park Test Figure 1.4.3.4 Result of Homoscedasticity Assumption Test Based on Breusch-Pagan Test 14 .
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