When I perform data summary I get a value for F that is different than when I run a hypothesis test. Is the data summary the true F statistic? What is the F the hypothesis test generates? Also, is this the right hypothesis test for an F test?

summary(mod1)

Call: lm(formula = voteA ~ lexpendA + logB_logA + prtystrA, data = vote1)

Residuals: Min 1Q Median 3Q Max -28.0390 -7.3311 -0.5119 7.6163 24.6760

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 21.32768 4.44185 4.802 3.45e-06 *** lexpendA 3.38604 0.55668 6.083 7.66e-09 *** logB_logA -0.02775 0.00266 -10.433 < 2e-16 *** prtystrA 0.24128 0.08038 3.002 0.00309 **

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 10.07 on 169 degrees of freedom Multiple R-squared: 0.6461, Adjusted R-squared: 0.6398 F-statistic: 102.9 on 3 and 169 DF, p-value: < 2.2e-16

---

Linear hypothesis test: lexpendA + logB_logA = 0

Model 1: restricted model Model 2: voteA ~ lexpendA + logB_logA + prtystrA

Res.Df RSS Df Sum of Sq F Pr(>F)
1 170 20825
2 169 17148 1 3677 36.239 1.055e-08