 Applied Linear Regression Questions

Subject Mathematics Statistics-R Programming

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rm(list=ls())
library(car)
attach(Bfox)
time <- as.numeric( rownames(Bfox))
Bfox.new <- cbind(Bfox, time)
names(Bfox.new)
#  "partic"   "tfr"      "menwage" "womwage" "debt"    "parttime" "time"

# 1.
# (a)
lm.out <- lm(partic ~ ., data=Bfox.new)
summary(lm.out)
# Call:
#   lm(formula = partic ~ ., data = Bfox.new)
#
# Residuals:
#   Min       1Q   Median       3Q      Max
# -0.82202 -0.29852 0.01137 0.38882 1.01697
#
# Coefficients:
#   Estimate Std. Error t value Pr(>|t|)
# (Intercept) 5.043e+01 1.913e+02   0.264 0.79443
# tfr          2.618e-04 4.093e-04   0.640 0.52874
# menwage    -4.150e-02 1.478e-01 -0.281 0.78133
# womwage      5.724e-02 1.758e-01   0.325 0.74776
# debt         6.689e-02 1.558e-02   4.294 0.00027 ***
# parttime    6.865e-01 8.185e-02   8.387 1.89e-08 ***
# time       -1.806e-02 9.944e-02 -0.182 0.85745
# ---
#   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#
# Residual standard error: 0.5333 on 23 degrees of freedom
# Multiple R-squared: 0.9936, Adjusted R-squared: 0.992
# F-statistic: 597.8 on 6 and 23 DF, p-value: < 2.2e-16...

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