SPSS Statistics

Hello Groupe!

I Have questions about the Logistic regression model assumption:

• How does one verify that the expected value of the error term is zero? is it that important?
• How does one verify no correlation between the error and the independent variables? how does it take form if some of the independent variables are categorical?
• Is it acceptable to use VIF with categorical variables?

Thanks!

Hello, Meni.

About your questions:

1) You can compute the model residuals and build a scatterplot with another variable (e.g., the case ID). In logistic regression models, due to the non-conventional form of the error, the assumption of lack of significative multicolinearity between predictors is more important than that one. The model accuracy itself is also more important.

2) The first part is easy when you compute the residuals. When some predictor is categorical, you should explore some descriptive statistics of the error by category of this predictor and conclude if the values are too different when you compare the categories.

3) To me, it makes no sense because VIF is the inverse of the tolerance that is, by itself, a R-squared derived measure.

Hello Groupe!

I Have questions about the Logistic regression model assumption:

• How does one verify that the expected value of the error term is zero? is it that important?
• How does one verify no correlation between the error and the independent variables? how does it take form if some of the independent variables are categorical?
• Is it acceptable to use VIF with categorical variables?

Thanks!

Hello Groupe!

I Have questions about the Logistic regression model assumption:

• How does one verify that the expected value of the error term is zero? is it that important?
• How does one verify no correlation between the error and the independent variables? how does it take form if some of the independent variables are categorical?
• Is it acceptable to use VIF with categorical variables?

Thanks!

------------------------------
Meni Berger
------------------------------

Hello Groupe!

I Have questions about the Logistic regression model assumption:

• How does one verify that the expected value of the error term is zero? is it that important?
• How does one verify no correlation between the error and the independent variables? how does it take form if some of the independent variables are categorical?
• Is it acceptable to use VIF with categorical variables?

Thanks!

------------------------------
Meni Berger
------------------------------

• Logistic regression, the error structure is different due to the binary outcome, but it's still important to ensure that the independent variables are not correlated with the error term .
• For second question you can use deviance residuals or Pearson residuals, net net  link tests, and multicollinearity checks should help you
• You can use boxplots or strip plots of the residuals for each category (level) of the categorical variable. 
  You should observe that the residuals are centered around zero and do not display any systematic pattern or differences across categories.
  now if residuals for one or more categories consistently deviate from zero, this could indicate correlation between the errors and the independent variable, suggesting possible misspecification of the model. The third one yes you can but Iam assuming you are using dummy variables and in such case if VIF > 10 is a concern, it indicates that this dummy variable is highly collinear with other predictors, which could cause problems in estimating the model.  
  Where are we using this Linear regression model ? Is it finance or Mechanical engg ? 

Hello Groupe!

I Have questions about the Logistic regression model assumption:

• How does one verify that the expected value of the error term is zero? is it that important?
• How does one verify no correlation between the error and the independent variables? how does it take form if some of the independent variables are categorical?
• Is it acceptable to use VIF with categorical variables?

Thanks!

------------------------------
Meni Berger
------------------------------

Thank you, Anil (and also Jon and Estefano !)

I am updating my lecture material for logistic regression and delving deeper into my old assumption-checking part. I am trying to seek what's relevant and make the presentation more informative.

do you have any examples for the boxplots or strip plots of the residuals?

• Logistic regression, the error structure is different due to the binary outcome, but it's still important to ensure that the independent variables are not correlated with the error term .
• For second question you can use deviance residuals or Pearson residuals, net net  link tests, and multicollinearity checks should help you
• You can use boxplots or strip plots of the residuals for each category (level) of the categorical variable. 
  You should observe that the residuals are centered around zero and do not display any systematic pattern or differences across categories.
  now if residuals for one or more categories consistently deviate from zero, this could indicate correlation between the errors and the independent variable, suggesting possible misspecification of the model. The third one yes you can but Iam assuming you are using dummy variables and in such case if VIF > 10 is a concern, it indicates that this dummy variable is highly collinear with other predictors, which could cause problems in estimating the model.  
  Where are we using this Linear regression model ? Is it finance or Mechanical engg ? 

Hello Groupe!

I Have questions about the Logistic regression model assumption:

• How does one verify that the expected value of the error term is zero? is it that important?
• How does one verify no correlation between the error and the independent variables? how does it take form if some of the independent variables are categorical?
• Is it acceptable to use VIF with categorical variables?

Thanks!

------------------------------
Meni Berger
------------------------------

Thank you, Anil (and also Jon and Estefano !)

I am updating my lecture material for logistic regression and delving deeper into my old assumption-checking part. I am trying to seek what's relevant and make the presentation more informative.

do you have any examples for the boxplots or strip plots of the residuals?

• Logistic regression, the error structure is different due to the binary outcome, but it's still important to ensure that the independent variables are not correlated with the error term .
• For second question you can use deviance residuals or Pearson residuals, net net  link tests, and multicollinearity checks should help you
• You can use boxplots or strip plots of the residuals for each category (level) of the categorical variable. 
  You should observe that the residuals are centered around zero and do not display any systematic pattern or differences across categories.
  now if residuals for one or more categories consistently deviate from zero, this could indicate correlation between the errors and the independent variable, suggesting possible misspecification of the model. The third one yes you can but Iam assuming you are using dummy variables and in such case if VIF > 10 is a concern, it indicates that this dummy variable is highly collinear with other predictors, which could cause problems in estimating the model.  
  Where are we using this Linear regression model ? Is it finance or Mechanical engg ? 

Hello Groupe!

I Have questions about the Logistic regression model assumption:

• How does one verify that the expected value of the error term is zero? is it that important?
• How does one verify no correlation between the error and the independent variables? how does it take form if some of the independent variables are categorical?
• Is it acceptable to use VIF with categorical variables?

Thanks!

------------------------------
Meni Berger
------------------------------

Thank you, Anil (and also Jon and Estefano !)

I am updating my lecture material for logistic regression and delving deeper into my old assumption-checking part. I am trying to seek what's relevant and make the presentation more informative.

do you have any examples for the boxplots or strip plots of the residuals?

• Logistic regression, the error structure is different due to the binary outcome, but it's still important to ensure that the independent variables are not correlated with the error term .
• For second question you can use deviance residuals or Pearson residuals, net net  link tests, and multicollinearity checks should help you
• You can use boxplots or strip plots of the residuals for each category (level) of the categorical variable. 
  You should observe that the residuals are centered around zero and do not display any systematic pattern or differences across categories.
  now if residuals for one or more categories consistently deviate from zero, this could indicate correlation between the errors and the independent variable, suggesting possible misspecification of the model. The third one yes you can but Iam assuming you are using dummy variables and in such case if VIF > 10 is a concern, it indicates that this dummy variable is highly collinear with other predictors, which could cause problems in estimating the model.  
  Where are we using this Linear regression model ? Is it finance or Mechanical engg ? 

Hello Groupe!

I Have questions about the Logistic regression model assumption:

• How does one verify that the expected value of the error term is zero? is it that important?
• How does one verify no correlation between the error and the independent variables? how does it take form if some of the independent variables are categorical?
• Is it acceptable to use VIF with categorical variables?

Thanks!

------------------------------
Meni Berger
------------------------------

Thank you, Anil (and also Jon and Estefano !)

I am updating my lecture material for logistic regression and delving deeper into my old assumption-checking part. I am trying to seek what's relevant and make the presentation more informative.

do you have any examples for the boxplots or strip plots of the residuals?

• Logistic regression, the error structure is different due to the binary outcome, but it's still important to ensure that the independent variables are not correlated with the error term .
• For second question you can use deviance residuals or Pearson residuals, net net  link tests, and multicollinearity checks should help you
• You can use boxplots or strip plots of the residuals for each category (level) of the categorical variable. 
  You should observe that the residuals are centered around zero and do not display any systematic pattern or differences across categories.
  now if residuals for one or more categories consistently deviate from zero, this could indicate correlation between the errors and the independent variable, suggesting possible misspecification of the model. The third one yes you can but Iam assuming you are using dummy variables and in such case if VIF > 10 is a concern, it indicates that this dummy variable is highly collinear with other predictors, which could cause problems in estimating the model.  
  Where are we using this Linear regression model ? Is it finance or Mechanical engg ? 

Hello Groupe!

I Have questions about the Logistic regression model assumption:

• How does one verify that the expected value of the error term is zero? is it that important?
• How does one verify no correlation between the error and the independent variables? how does it take form if some of the independent variables are categorical?
• Is it acceptable to use VIF with categorical variables?

Thanks!

------------------------------
Meni Berger
------------------------------