Finally, run at least 2 different, appropriate regressions predicting the variable you indicated in Q10. These regressions should use different predictor(s). Organize and explain your code within the code chunk using comments. Identify the best model and provide a discussion of your observations in the *Q14 Discussion* section provided below. Be organized and detailed in your discussion.

dataset name =updated_brf

variables of interest = "PERSDOC2", "EMPLOY1", "LSATISFY", , and "EDUCA"

target variable to be predicted = LSATISFY

variables used to predict = PERSDOC2", "EMPLOY1 , EDUCA"

All variables have value labels, not actual value.

#For analysis of "PERSDOC2" column: Value Value Label Frequency ----- ------------------ --------- 1 Yes, only one 341,386 2 More than one 34,477 3 No 63,737 7 Don't know/Not Sure 1,165

EMPLOY1" column:

Value Value Label Frequency ----- ------------------------------------ --------- 1 Employed for wages 179,163 2 Self-employed 36,609 3 Out of work for 1 year or more 9,594 4 Out of work for less than 1 year 9,012 5 A homemaker 27,107 6 A student 11,551 7 Retired 132,648 8 Unable to work 31,977

# For analysis of 'LSATISFY' column: Value Value Label Frequency ----- ------------------- --------- 1 Very satisfied 9,066 2 Satisfied 9,721 3 Dissatisfied 827 4 Very dissatisfied 224

#For analysis of "EDUCA" column: Value Value Label Frequency ----- -------------------------------------------------------- --------- 1 Never attended school or only kindergarten 609 2 Grades 1 through 8 (Elementary) 11,187 3 Grades 9 through 11 (Some high school) 22,463 4 Grade 12 or GED (High school graduate) 123,227 5 College 1 year to 3 years (Some college or technical school) 120,528 6 College 4 years or more (College graduate) 161,631