Hi everyone,
I have student scores for two test components (Component1 & Component2) from 485 schools. Using a linear regression model of Component1 on Component2, I want to predict scores for students who missed Component1 or whose marks were not recorded. The regression equation will be school-specific and used to generate predicted scores, recorded in a variable called "Predicted_Scores." The plan is for Stata to regress Component1 on Component2, predict missing Component1 scores for each school one-by-one, and store the predictions in a single variable named "Predicted_Scores." I've written the code bellow to accomplish this, but Stata is applying the same regression coefficients to all schools. Can you assist me in resolving this issue?
Code:
gen Predicted_Scores = .
forvalues i = 1/ 485 {
regress Component1 Component2 i.School_Code if School_Code == `i'
predict predicted_values, xb
replace Predicted_Scores = predicted_values if School_Code == `i'
}
Related Posts with Predicting values using Simple Linear Regression on Categorical Data
hierarchical random effect meta-analysisHi everyone, I am doing a meta-analysis and I want to use hierarchical random effect meta-analysis.…
Comparing dummy variables regarding another variableHello, My data set consists of funds and their historical returns from 2004-2018. My goal is in a f…
MICE with ordinal categorical variablesHello, I'm doing multiple imputation using chained equations (MICE) on a set of ordinal variables: …
Creating daily average and hourly levels from hourly dataHi everyone! I'm new to Stata and I would like to have some of your advice! I have gathered hourly…
How much computer specs do I have to analyze large data for fixed effectHi I have very large daily data. The number of rows is 1 million and the number of columns is 50. Ho…
Subscribe to:
Post Comments (Atom)
0 Response to Predicting values using Simple Linear Regression on Categorical Data
Post a Comment