How do you test for heteroscedasticity in SPSS?


  1. Activate SPSS program, then click Variable View, then on the Name write X1, X2, and Y.
  2. Then click Data View, then enter the value for each variable.
  3. Next step click Analyze – Regression – Linear …

How do you test for Multicollinearity in SPSS?

To do so, click on the Analyze tab, then Regression, then Linear: In the new window that pops up, drag score into the box labelled Dependent and drag the three predictor variables into the box labelled Independent(s). Then click Statistics and make sure the box is checked next to Collinearity diagnostics.

How do I know if my data is Homoscedastic?

The general rule of thumb1 is: If the ratio of the largest variance to the smallest variance is 1.5 or below, the data is homoscedastic.

How do you test for heteroscedasticity?

To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically. Typically, the telltale pattern for heteroscedasticity is that as the fitted values increases, the variance of the residuals also increases.

How does Levene’s test work?

In statistics, Levene’s test is an inferential statistic used to assess the equality of variances for a variable calculated for two or more groups. It tests the null hypothesis that the population variances are equal (called homogeneity of variance or homoscedasticity).

How do you interpret VIF multicollinearity in SPSS?

Test muticollinearity as a basis the VIF value of multicollinearity test results using SPSS. If the VIF value lies between 1-10, then there is no multicollinearity. If the VIF <1 or> 10, then there is multicollinearity.

What is a Homoscedastic t test?

Homoscedastic t-tests are based on the assumption that variances between two sample data ranges are equal [σ2( Argument1 ) = σ2( Argument2 )]. The following conditions are invalid: Argument1 and Argument2 have a different number of data points, and Hypothesis type = 1 (paired). Offset or Hypothesis type is nonnumeric.

What if data is Heteroscedastic?

How to Deal with Heteroscedastic Data

  1. Give data that produces a large scatter less weight.
  2. Transform the Y variable to achieve homoscedasticity. For example, use the Box-Cox normality plot to transform the data.

What is excessive nonconstant variance in multiple linear regression?

Excessive nonconstant variance can create technical difficulties with a multiple linear regression model. For example, if the residual variance increases with the fitted values, then prediction intervals will tend to be wider than they should be at low fitted values and narrower than they should be at high fitted values.

Does SPSS use variance or sample formulas?

Instead, SPSS always uses the sample formula. This goes for the between subjects variance (discussed in this tutorial) as well as the within subjects variance. Relevant output is shown below. Regarding this output table, also note that the variance is indeed the squared standard deviation (apart from rounding).

How to interpret a regression error with constant variance?

There are various tests that may be performed on the residuals for testing if the regression errors have constant variance. It is usually sufficient to “visually” interpret a residuals versus fitted values plot. However, the tests we discuss can provide an added layer of justification to your analysis.

Does SPSS include any formal tests of heteroscedasticity?

Unfortunately, SPSS does not include any formal tests of heteroscedasticity. Users can create macros within SPSS to perform specific functions not built into the software, but that process is beyond the scope of this example. Example code for a macro that includes the Breusch–Pagen test, and a tutorial video on how to