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Uji Park Heteroskedastisitas SPSS - Cara Mengatasi Uji Heteroskedastisitas SPSS - YouTube
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Assalamualaikum warahmatullahi wabarakatuh
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Good evening all friends.
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On this occasion, I will teach material about the park test
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in heteroscedasticity.
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Before I practice in the SPSS program,
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it would be nice for all of you to
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know about the basic concepts in the park test.
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Park test is one of the methods
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in heteroscedasticity test.
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While the heteroscedasticity test
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is part of the classical assumption test
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or prerequisite test in regression analysis.
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Park test is one way
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to accurately detect heteroscedasticity symptoms.
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Park's test is carried out by
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raising the residual value
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and then transforming LN
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or natural logarithm and then
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regressing the independent variable.
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If there are symptoms or
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problems of heteroscedasticity, it will
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lead to a doubt or
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inaccuracy in a regression analysis result.
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A good regression model is that there are no symptoms of heteroscedasticity.
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Okay, then I will go to the SPSS program
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here I use SPSS version 23
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the data I have input
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here I use two independent variables,
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namely the X1 variable and the X2 variable and
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one dependent variable, namely the Y variable.
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Immediately I will do the data analysis.
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The way we can choose analyze and then select regression
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then we can choose linear.
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This is where a dialog box appears, my friends.
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For the Y variable, we put it in the dependent column
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then for the X1 and X2 variables, we put it in the independent column
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and then we can select save, we check list
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the unstandardized residuals then
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select continue and finally we can choose ok.
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Well, here it has appeared for regression output
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but we just ignore this output, why?
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Because the goal is to bring up
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a new variable, namely the RES_1 variable.
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After the RES_1 variable appears
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we can select the transform menu
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and then select compute variable.
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This is where a dialog box appears, my friends.
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For the target variable, we fill it with RES2
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then in the numeric expression we fill in unstandardized residuals
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double-click it, then select the star symbol,
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then we can click on unstandardized residuals twice
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In this numeric expression, it contains RES_1 times RES_1, then select ok.
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We just ignore the output.
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Here a new variable has emerged, namely the RES2 variable
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this is the variable that we will LN or transform
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in the form of a natural logarithm.
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The way we can select the transform menu and then
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select compute variable.
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Here the dialog box appears again, friends.
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For the target variable we fill it with LN_RES
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then we can go to the function group, we select arithmetic
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then in the functions and special variables section we can select Ln
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in the numeric expression LN appears then
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we enter the variable RES2, we double click
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then we can select ok .
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Let's just ignore this output.
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Here a new variable has appeared, namely the LN_RES variable
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this is the variable that we will regress with variables X1 and X2
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to find out the symptoms of heteroscedasticity using the park test method,
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how we can choose analyze and then we select regression
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then we can choose linear
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here we reset First,
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for the LN variable, we put it in the dependent column
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then for the X1 and X2 variables,
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we put it in the independent column,
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then finally we can choose ok.
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Well, let's zoom in first,
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here, the output regression on the park test method in heteroscedasticity
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friends can all focus on the coefficient table.
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Before we interpret this coefficient table,
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it would be nice if we had to know
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about the basis of decision making in the park test,
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we can go to the PDF file again, let's put it here first,
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here there is a basis for making decisions in the park test
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if the significance value is greater than 0.05
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so the conclusion is that there are no symptoms of heteroscedasticity
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and if the significance value is less than 0.05
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so the conclusion is that there are symptoms of heteroscedasticity.
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After we know the basis for making decisions
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in the Park test, then we can interpret the results of the SPSS output
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in the coefficient table, all of you can focus on the significance value
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for the X1 variable which has a significance value of 0.458
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while the X2 variable has a significance value of 0.241
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so it can be concluded that there are no symptoms heteroscedasticity
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because the significance value obtained is greater than 0.05.
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I think that's enough explanation about
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heteroscedasticity test using the park test method.
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If friends are all still confused about the Park test
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all of you can comment in the comments column
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and don't forget to like subscribe and
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share videos on my YouTube channel, I'm finish
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Wassalamualaikum warahmatullahi wabarakatuh
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