E-View Beginner needs help!!
Posted: Mon Nov 09, 2015 8:11 pm
Hello fellow E-Views users,
I know the bare-basics of using E-Views as I look to advance my knowledge on creating efficient modelling practices. I am running a regression on Male vs. Female farm workers as my independent variables and total farms as my dependent variable. There are 10 observations denoted from 1-10, each number representing an individual state. I am looking to run a regression model and test the significance of male vs. female workers based on total farms.
As of right now I have basic descriptive statistics, OLS regression (total_farms c male female) and a very basic bar graph. I'm looking to enhance by doing the following however, I am limited due to my knowledge set of the program.
1) Specifying a proper regression model based on the data properties
2) Carry diagnostic tests on your estimated equation based on potential CLR violations
Is there any tips or tricks you guys could help me out with? I hope to hear from someone soon!
Edit for the record this is considered Cross-Sectional data :)
I know the bare-basics of using E-Views as I look to advance my knowledge on creating efficient modelling practices. I am running a regression on Male vs. Female farm workers as my independent variables and total farms as my dependent variable. There are 10 observations denoted from 1-10, each number representing an individual state. I am looking to run a regression model and test the significance of male vs. female workers based on total farms.
As of right now I have basic descriptive statistics, OLS regression (total_farms c male female) and a very basic bar graph. I'm looking to enhance by doing the following however, I am limited due to my knowledge set of the program.
1) Specifying a proper regression model based on the data properties
2) Carry diagnostic tests on your estimated equation based on potential CLR violations
Is there any tips or tricks you guys could help me out with? I hope to hear from someone soon!
Edit for the record this is considered Cross-Sectional data :)