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Home , Normality test

Multiple 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 Assumption 1.4.4 STATCAL’s Result: Test of Non- 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

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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.

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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

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1.3 Multiple Linear Regression with STATCAL

To perform multiple linear regression with STATCAL, choose => Linear

Regression ( 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

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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

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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

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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 Inflaction Factor (VIF)

Figure 1.4.2.2 Result of Multicolinearity Test Based on Correlation Matrix (Pearson Correlation)

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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

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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

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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

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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)

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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

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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 -2.18e-15 -1.022574 Maximum 8.320841 2 Minimum -5.231726 Std. Dev. 5.213715 0.547872 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

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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

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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

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