Predicting student proficiency test scores accounting for poverty and income measures: Data was collected on 608 school districts in Ohio (The Cincinnati Enquirer, Nov 30, 1997) using passage rates in 1996. The variables used in this analysis include: % passed – percent of all 4rth, 6th, 9th and 12th graders who passed the state’s exam eg: 93.85 means 93.85% % on ADC – percent of all students in the district receiving Aid for Dependent Children eg: 0.11 means 0.11% % Free Lunch – percent of all students who qualify for reduced- priced lunches Median Income – reported in dollars Now conduct a simple linear regression analysis for each of the variables predicting passing rate and fill in the table below. (posted in attachments). Question: Judging from the outputs summarized in above (analysis posted in attachments), the % free lunch variable provides the best linear regression equation available with this data to predict a county’s passing rate. Explain why this is the best linear regression equation.
Predicting student proficiency test scores accounting for poverty and income measures: Data was collected on 608 school districts in Ohio (The Cincinnati Enquirer, Nov 30, 1997) using passage rates in 1996. The variables used in this analysis include:
% passed – percent of all 4rth, 6th, 9th and 12th graders who passed the state’s exam eg: 93.85 means 93.85%
% on ADC – percent of all students in the district receiving Aid for Dependent Children eg: 0.11 means 0.11%
% Free Lunch – percent of all students who qualify for reduced- priced lunches
Now conduct a simple linear
Question: Judging from the outputs summarized in above (analysis posted in attachments), the % free lunch variable provides the best linear regression equation available with this data to predict a county’s passing rate. Explain why this is the best linear regression equation.
Step by step
Solved in 3 steps
