municipality_analysis
Rogerio Luiz
2025-01-20
- Taking student data at the municipality level, first calculating the average gains (difference between 9th and 5th grade math scores) and 5th grade math scores separately for each cohort, then taking the mean of these cohort averages to get a single value per municipality.
This gives us final Municipality-Level metrics that represent the typical performance across all cohorts.
Then, scatter plot with regression line showing relationship between initial scores (x-axis) and gains (y-axis), with residuals indicating which municipality over/under-performed versus predictions.
Results
black_top_ses
Math
## Correlation between 5th Grade and Dropout Rate: -0.21## Correlation between Gain and Dropout Rate: 0.18## R2 Without drop out 0.3559526## R2 with drop out 0.3589996## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_math_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -119.673 -8.768 -0.349 8.434 108.192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 180.77489 3.51729 51.396 < 2e-16 ***
## avg_municipality_math_5th -0.68169 0.01689 -40.349 < 2e-16 ***
## avg_drop_out_rate 12.00428 3.07744 3.901 9.79e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.5 on 3201 degrees of freedom
## Multiple R-squared: 0.359, Adjusted R-squared: 0.3586
## F-statistic: 896.4 on 2 and 3201 DF, p-value: < 2.2e-16## # A tibble: 3,204 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_math…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 49.2 192. 0.222
## 2 1100023 58.4 189. 0.252
## 3 1100031 62.1 194. 0.200
## 4 1100049 59.3 193. 0.155
## 5 1100056 44.0 207. 0.203
## 6 1100064 42.2 204. 0.111
## 7 1100072 69.0 177. 0.223
## 8 1100080 38.6 195. 0.203
## 9 1100098 60.9 206. 0.212
## 10 1100106 43.7 193. 0.226
## # ℹ 3,194 more rows
## # ℹ abbreviated name: ¹avg_municipality_math_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>Reading
## Correlation between 5th Grade and Dropout Rate: -0.17## Correlation between Gain and Dropout Rate: 0.17## R2 Without drop out 0.2621545## R2 with drop out 0.2682422## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_leitura_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -108.244 -8.743 -0.045 9.050 105.892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 173.80893 3.68688 47.14 < 2e-16 ***
## avg_municipality_leitura_5th -0.64324 0.01982 -32.46 < 2e-16 ***
## avg_drop_out_rate 16.04155 3.10857 5.16 2.61e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.81 on 3201 degrees of freedom
## Multiple R-squared: 0.2682, Adjusted R-squared: 0.2678
## F-statistic: 586.7 on 2 and 3201 DF, p-value: < 2.2e-16## # A tibble: 3,204 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_leit…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 69.9 161. 0.222
## 2 1100023 69.8 172. 0.252
## 3 1100031 62.3 172. 0.200
## 4 1100049 67.3 178. 0.155
## 5 1100056 54.8 182. 0.203
## 6 1100064 51.5 184. 0.111
## 7 1100072 80.7 160. 0.223
## 8 1100080 89.9 171. 0.203
## 9 1100098 68.6 183. 0.212
## 10 1100106 63.6 173. 0.226
## # ℹ 3,194 more rows
## # ℹ abbreviated name: ¹avg_municipality_leitura_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>black_bottom_ses
Math
## Correlation between 5th Grade and Dropout Rate: -0.3## Correlation between Gain and Dropout Rate: 0.11## R2 Without drop out 0.3466142## R2 with drop out 0.3507156## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_math_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.661 -9.251 -0.510 8.109 104.192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 163.1157 2.8675 56.885 < 2e-16 ***
## avg_municipality_math_5th -0.5872 0.0145 -40.503 < 2e-16 ***
## avg_drop_out_rate -9.4700 2.1216 -4.464 8.34e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.51 on 3154 degrees of freedom
## Multiple R-squared: 0.3507, Adjusted R-squared: 0.3503
## F-statistic: 851.8 on 2 and 3154 DF, p-value: < 2.2e-16## # A tibble: 3,157 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_math…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 58.3 181. 0.315
## 2 1100072 68.8 183. 0.220
## 3 1100080 58.5 179. 0.292
## 4 1100098 61.5 195. 0.173
## 5 1100130 55.2 192. 0.399
## 6 1100148 63.8 203. 0.313
## 7 1100262 63.1 182. 0.281
## 8 1100296 77.8 184. 0.334
## 9 1100320 51.0 184. 0.151
## 10 1100338 59.9 188. 0.375
## # ℹ 3,147 more rows
## # ℹ abbreviated name: ¹avg_municipality_math_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>Reading
## Correlation between 5th Grade and Dropout Rate: -0.3## Correlation between Gain and Dropout Rate: 0.15## R2 Without drop out 0.2813124## R2 with drop out 0.2813688## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_leitura_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -109.263 -8.411 0.025 8.618 89.068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 158.61562 2.99048 53.040 <2e-16 ***
## avg_municipality_leitura_5th -0.56614 0.01682 -33.666 <2e-16 ***
## avg_drop_out_rate -1.04697 2.10527 -0.497 0.619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.39 on 3154 degrees of freedom
## Multiple R-squared: 0.2814, Adjusted R-squared: 0.2809
## F-statistic: 617.4 on 2 and 3154 DF, p-value: < 2.2e-16## # A tibble: 3,157 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_leit…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 71.5 157. 0.315
## 2 1100072 87.1 155. 0.220
## 3 1100080 67.5 156. 0.292
## 4 1100098 72.9 180. 0.173
## 5 1100130 75.1 169. 0.399
## 6 1100148 71.1 183. 0.313
## 7 1100262 81.8 168. 0.281
## 8 1100296 81.9 155. 0.334
## 9 1100320 65.7 167. 0.151
## 10 1100338 63.5 168. 0.375
## # ℹ 3,147 more rows
## # ℹ abbreviated name: ¹avg_municipality_leitura_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>white_top_ses
Math
## Correlation between 5th Grade and Dropout Rate: -0.28## Correlation between Gain and Dropout Rate: 0.25## R2 Without drop out 0.3366747## R2 with drop out 0.3453524## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_math_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77.901 -6.621 -0.381 6.134 68.114
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 132.93820 2.58770 51.373 < 2e-16 ***
## avg_municipality_math_5th -0.41282 0.01125 -36.697 < 2e-16 ***
## avg_drop_out_rate 12.99287 2.02036 6.431 1.46e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.03 on 3120 degrees of freedom
## Multiple R-squared: 0.3454, Adjusted R-squared: 0.3449
## F-statistic: 823 on 2 and 3120 DF, p-value: < 2.2e-16## # A tibble: 3,123 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_math…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 47.8 204. 0.251
## 2 1100023 59.9 198. 0.252
## 3 1100031 38.6 203. 0.200
## 4 1100049 51.7 206. 0.155
## 5 1100056 46.1 221. 0.203
## 6 1100064 42.6 218. 0.111
## 7 1100072 79.5 191. 0.223
## 8 1100080 43.1 196. 0.203
## 9 1100098 48.0 214. 0.212
## 10 1100106 47.5 196. 0.226
## # ℹ 3,113 more rows
## # ℹ abbreviated name: ¹avg_municipality_math_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>Reading
## Correlation between 5th Grade and Dropout Rate: -0.25## Correlation between Gain and Dropout Rate: 0.23## R2 Without drop out 0.3055326## R2 with drop out 0.3154128## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_leitura_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -71.098 -5.131 0.080 4.967 61.075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 136.60236 2.35750 57.94 < 2e-16 ***
## avg_municipality_leitura_5th -0.39540 0.01145 -34.53 < 2e-16 ***
## avg_drop_out_rate 11.43398 1.70393 6.71 2.3e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.385 on 3120 degrees of freedom
## Multiple R-squared: 0.3154, Adjusted R-squared: 0.315
## F-statistic: 718.7 on 2 and 3120 DF, p-value: < 2.2e-16## # A tibble: 3,123 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_leit…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 59.1 189. 0.251
## 2 1100023 74.2 181. 0.252
## 3 1100031 57.4 185. 0.200
## 4 1100049 65.1 188. 0.155
## 5 1100056 58.9 200. 0.203
## 6 1100064 55.2 199. 0.111
## 7 1100072 91.6 165. 0.223
## 8 1100080 87.4 172. 0.203
## 9 1100098 66.3 196. 0.212
## 10 1100106 64.6 179. 0.226
## # ℹ 3,113 more rows
## # ℹ abbreviated name: ¹avg_municipality_leitura_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>white_bottom_ses
Math
## Correlation between 5th Grade and Dropout Rate: -0.44## Correlation between Gain and Dropout Rate: 0.19## R2 Without drop out 0.2013975## R2 with drop out 0.2014288## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_math_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -66.589 -7.628 -0.396 7.159 90.977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 111.70789 2.63708 42.360 <2e-16 ***
## avg_municipality_math_5th -0.31394 0.01238 -25.366 <2e-16 ***
## avg_drop_out_rate -0.64958 1.84872 -0.351 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.99 on 3142 degrees of freedom
## Multiple R-squared: 0.2014, Adjusted R-squared: 0.2009
## F-statistic: 396.3 on 2 and 3142 DF, p-value: < 2.2e-16## # A tibble: 3,145 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_math…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 45.1 206. 0.243
## 2 1100072 55.6 196. 0.220
## 3 1100080 65.2 171. 0.292
## 4 1100098 55.0 209. 0.173
## 5 1100130 58.8 196. 0.399
## 6 1100148 64.3 197. 0.313
## 7 1100262 45.0 193. 0.308
## 8 1100296 45.6 208. 0.334
## 9 1100320 54.6 195. 0.151
## 10 1100338 51.4 191. 0.375
## # ℹ 3,135 more rows
## # ℹ abbreviated name: ¹avg_municipality_math_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>Reading
## Correlation between 5th Grade and Dropout Rate: -0.45## Correlation between Gain and Dropout Rate: 0.22## R2 Without drop out 0.1509072## R2 with drop out 0.1538163## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_leitura_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -103.468 -7.253 0.044 7.279 84.824
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 110.37868 2.63489 41.891 < 2e-16 ***
## avg_municipality_leitura_5th -0.27059 0.01375 -19.683 < 2e-16 ***
## avg_drop_out_rate 5.70705 1.73646 3.287 0.00103 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.17 on 3142 degrees of freedom
## Multiple R-squared: 0.1538, Adjusted R-squared: 0.1533
## F-statistic: 285.6 on 2 and 3142 DF, p-value: < 2.2e-16## # A tibble: 3,145 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_leit…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 61.6 184. 0.243
## 2 1100072 72.4 179. 0.220
## 3 1100080 74.1 155. 0.292
## 4 1100098 65.2 189. 0.173
## 5 1100130 73.3 178. 0.399
## 6 1100148 69.1 178. 0.313
## 7 1100262 79.5 167. 0.308
## 8 1100296 65.1 186. 0.334
## 9 1100320 65.6 176. 0.151
## 10 1100338 62.3 176. 0.375
## # ℹ 3,135 more rows
## # ℹ abbreviated name: ¹avg_municipality_leitura_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>black_brown_top_ses
Math
## Correlation between 5th Grade and Dropout Rate: -0.27## Correlation between Gain and Dropout Rate: 0.28## R2 Without drop out 0.3784317## R2 with drop out 0.3926537## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_math_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -83.561 -6.617 -0.470 5.804 77.929
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 147.76667 2.81534 52.486 <2e-16 ***
## avg_municipality_math_5th -0.51132 0.01276 -40.087 <2e-16 ***
## avg_drop_out_rate 17.42232 2.03601 8.557 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.19 on 3127 degrees of freedom
## Multiple R-squared: 0.3927, Adjusted R-squared: 0.3923
## F-statistic: 1011 on 2 and 3127 DF, p-value: < 2.2e-16## # A tibble: 3,130 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_math…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 49.3 201. 0.251
## 2 1100023 56.6 198. 0.252
## 3 1100031 47.7 205. 0.200
## 4 1100049 52.5 204. 0.155
## 5 1100056 40.9 216. 0.203
## 6 1100064 38.7 216. 0.111
## 7 1100072 57.7 201. 0.223
## 8 1100080 51.4 205. 0.203
## 9 1100098 43.3 218. 0.212
## 10 1100106 38.6 200. 0.226
## # ℹ 3,120 more rows
## # ℹ abbreviated name: ¹avg_municipality_math_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>Reading
## Correlation between 5th Grade and Dropout Rate: -0.24## Correlation between Gain and Dropout Rate: 0.27## R2 Without drop out 0.2611636## R2 with drop out 0.2855907## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_leitura_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -103.493 -5.477 -0.033 5.627 68.628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 138.46111 2.85423 48.51 <2e-16 ***
## avg_municipality_leitura_5th -0.43901 0.01446 -30.36 <2e-16 ***
## avg_drop_out_rate 19.43117 1.87920 10.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.43 on 3127 degrees of freedom
## Multiple R-squared: 0.2856, Adjusted R-squared: 0.2851
## F-statistic: 625 on 2 and 3127 DF, p-value: < 2.2e-16## # A tibble: 3,130 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_leit…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 73.9 170. 0.251
## 2 1100023 70.2 182. 0.252
## 3 1100031 63.1 186. 0.200
## 4 1100049 64.6 187. 0.155
## 5 1100056 57.5 192. 0.203
## 6 1100064 53.1 196. 0.111
## 7 1100072 71.0 181. 0.223
## 8 1100080 74.8 185. 0.203
## 9 1100098 60.3 197. 0.212
## 10 1100106 58.9 182. 0.226
## # ℹ 3,120 more rows
## # ℹ abbreviated name: ¹avg_municipality_leitura_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>black_brown_bottom_ses
Math
## Correlation between 5th Grade and Dropout Rate: -0.42## Correlation between Gain and Dropout Rate: 0.24## R2 Without drop out 0.247296## R2 with drop out 0.2485391## `geom_smooth()` using formula = 'y ~ x'
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_math_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.703 -6.287 -0.433 5.364 74.317
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 111.67778 2.43988 45.772 <2e-16 ***
## avg_municipality_math_5th -0.33104 0.01177 -28.126 <2e-16 ***
## avg_drop_out_rate 3.33025 1.46398 2.275 0.023 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.37 on 3128 degrees of freedom
## Multiple R-squared: 0.2485, Adjusted R-squared: 0.2481
## F-statistic: 517.3 on 2 and 3128 DF, p-value: < 2.2e-16## # A tibble: 3,131 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_math…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 35.9 208. 0.243
## 2 1100072 53.3 201. 0.220
## 3 1100080 55.0 179. 0.292
## 4 1100098 53.9 207. 0.173
## 5 1100130 50.8 201. 0.399
## 6 1100148 58.6 201. 0.313
## 7 1100262 63.3 180. 0.308
## 8 1100296 59.3 195. 0.334
## 9 1100320 50.1 194. 0.151
## 10 1100338 50.8 197. 0.375
## # ℹ 3,121 more rows
## # ℹ abbreviated name: ¹avg_municipality_math_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>Reading
## Correlation between 5th Grade and Dropout Rate: -0.43## Correlation between Gain and Dropout Rate: 0.29## R2 Without drop out 0.1753144## R2 with drop out 0.1889349
##
## Call:
## lm(formula = avg_municipality_gain ~ avg_municipality_leitura_5th +
## avg_drop_out_rate, data = municipality_averages)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.956 -5.615 0.126 5.639 56.658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 104.61838 2.37767 44.000 < 2e-16 ***
## avg_municipality_leitura_5th -0.25839 0.01273 -20.297 < 2e-16 ***
## avg_drop_out_rate 9.60733 1.32557 7.248 5.32e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.331 on 3128 degrees of freedom
## Multiple R-squared: 0.1889, Adjusted R-squared: 0.1884
## F-statistic: 364.3 on 2 and 3128 DF, p-value: < 2.2e-16## # A tibble: 3,131 × 6
## COD_MUNICIPIO avg_municipality_gain avg_municipality_leit…¹ avg_drop_out_rate
## <int> <dbl> <dbl> <dbl>
## 1 1100015 51.5 185. 0.243
## 2 1100072 70.7 181. 0.220
## 3 1100080 65.6 160. 0.292
## 4 1100098 67.2 188. 0.173
## 5 1100130 65.6 179. 0.399
## 6 1100148 70.3 182. 0.313
## 7 1100262 82.4 164. 0.308
## 8 1100296 61.9 185. 0.334
## 9 1100320 66.6 175. 0.151
## 10 1100338 64.6 178. 0.375
## # ℹ 3,121 more rows
## # ℹ abbreviated name: ¹avg_municipality_leitura_5th
## # ℹ 2 more variables: predicted_gain <dbl>, residuals <dbl>