ScatterPlots
Note about VAM Plots: Any counties with N/A value for VAM were replaced with 0. These are so few that I believe it has minimal effect on the trend lines.
National Level
% Farm Tractor 1940 vs 1930
% Farm Tractor 1950 vs 1940
% Farm Tractor 1954 vs 1950
% Farm Tractor 1964 vs 1954
OHIO
## Warning in read.dta13("ohio1940.dta"):
## bpld:
## Duplicated factor levels detected - generating unique labels.## Warning in read.dta13("ohio1940.dta"):
## higrade:
## Missing factor labels - no labels assigned.
## Set option generate.factors=T to generate labels.## Warning: between() called on numeric vector with S3 class
## `geom_smooth()` using method = 'loess'
Transform ohio edu response on Log Scale
Ohio Mean Model
##
## Call:
## lm(formula = logAvg ~ percent_farm_tractor, data = plotSrc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.121748 -0.021526 0.008973 0.029746 0.066242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.280754 0.009254 246.458 < 2e-16 ***
## percent_farm_tractor 0.186915 0.035457 5.272 9.92e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04027 on 86 degrees of freedom
## Multiple R-squared: 0.2442, Adjusted R-squared: 0.2354
## F-statistic: 27.79 on 1 and 86 DF, p-value: 9.92e-07Ohio - Median Plots
## Warning: package 'bindrcpp' was built under R version 3.4.4
Ohio - Median Model
##
## Call:
## lm(formula = logMed ~ percent_farm_tractor, data = plotSrc_med)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23988 -0.01976 0.01381 0.03978 0.09668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.29588 0.01401 163.899 <2e-16 ***
## percent_farm_tractor 0.24231 0.05367 4.515 2e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06095 on 86 degrees of freedom
## Multiple R-squared: 0.1916, Adjusted R-squared: 0.1822
## F-statistic: 20.38 on 1 and 86 DF, p-value: 2.002e-05Ohio Median Model County Residuals
## residual county
## 1 -0.112959812 ADAMS
## 2 0.047486358 ALLEN
## 3 0.054977761 ASHLAND
## 4 0.046372762 ASHTABULA
## 5 0.085490938 ATHENS
## 6 0.025265991 AUGLAIZE
## 7 -0.011934577 BELMONT
## 8 -0.014356189 BROWN
## 9 0.012412747 BUTLER
## 10 -0.014992712 CARROLL
## 11 -0.058811759 CHAMPAIGN
## 12 0.034005912 CLARK
## 13 -0.026009483 CLERMONT
## 14 0.025047623 CLINTON
## 15 0.067953854 COLUMBIANA
## 16 0.081556658 COSHOCTON
## 17 0.030156259 CRAWFORD
## 18 0.033009156 CUYAHOGA
## 19 -0.063702913 DARKE
## 20 0.005843931 DEFIANCE
## 21 0.047879279 DELAWARE
## 22 0.013541789 ERIE
## 23 0.052254282 FAIRFIELD
## 24 -0.091052632 FAYETTE
## 25 0.028783465 FRANKLIN
## 26 0.022713891 FULTON
## 27 -0.106094148 GALLIA
## 28 -0.054258333 GEAUGA
## 29 0.028379673 GREENE
## 30 0.041666806 GUERNSEY
## 31 0.045707317 HAMILTON
## 32 0.008959789 HANCOCK
## 33 0.035480077 HARDIN
## 34 -0.013590681 HARRISON
## 35 -0.100047890 HENRY
## 36 0.061335824 HIGHLAND
## 37 -0.007908747 HOCKING
## 38 -0.133132918 HOLMES
## 39 0.017162441 HURON
## 40 -0.004124033 JACKSON
## 41 -0.024585427 JEFFERSON
## 42 0.069013115 KNOX
## 43 0.026507948 LAKE
## 44 -0.002983564 LAWRENCE
## 45 0.068379179 LICKING
## 46 0.045047582 LOGAN
## 47 0.010291784 LORAIN
## 48 0.027503513 LUCAS
## 49 -0.075959853 MADISON
## 50 0.040562067 MAHONING
## 51 0.011820089 MARION
## 52 0.034324309 MEDINA
## 53 -0.005313321 MEIGS
## 54 -0.067441037 MERCER
## 55 0.026239927 MIAMI
## 56 -0.107093190 MONROE
## 57 0.018598260 MONTGOMERY
## 58 0.093040307 MORGAN
## 59 0.064601751 MORROW
## 60 -0.013036366 MUSKINGUM
## 61 0.096681182 NOBLE
## 62 -0.003549149 OTTAWA
## 63 -0.099598348 PAULDING
## 64 -0.018867580 PERRY
## 65 -0.084324387 PICKAWAY
## 66 -0.239878392 PIKE
## 67 0.038503011 PORTAGE
## 68 0.020632806 PREBLE
## 69 -0.065773458 PUTNAM
## 70 -0.046237473 RICHLAND
## 71 -0.149859198 ROSS
## 72 0.012926822 SANDUSKY
## 73 -0.022438265 SCIOTO
## 74 0.009379834 SENECA
## 75 0.011881776 SHELBY
## 76 0.039515448 STARK
## 77 0.014084051 SUMMIT
## 78 0.048150217 TRUMBULL
## 79 0.068972682 TUSCARAWAS
## 80 0.043074903 UNION
## 81 0.029974464 VAN WERT
## 82 -0.111503976 VINTON
## 83 -0.059320063 WARREN
## 84 -0.007372566 WASHINGTON
## 85 0.043241264 WAYNE
## 86 0.031597172 WILLIAMS
## 87 -0.014165714 WOOD
## 88 0.034288107 WYANDOTOH EDU VS % SSE
Sum of Square error
## [1] 1.174032
VAM 1947 vs % 1940
VAM 1958 vs % 1950
##
## Call:
## lm(formula = vam_ohio1957$VAM ~ ohio1950$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -569.63 -189.42 -25.26 194.35 616.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 188.7 119.4 1.581 0.11950
## ohio1950$percent_farm_tractor 546.8 188.4 2.902 0.00526 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 273.3 on 57 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1288, Adjusted R-squared: 0.1135
## F-statistic: 8.424 on 1 and 57 DF, p-value: 0.005256
VAM 1947 vs % 1930
##
## Call:
## lm(formula = vam_ohio1947$VAM ~ ohio1930$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -447.78 -210.40 -43.62 235.08 562.43
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 214.00 62.37 3.431 0.000959 ***
## ohio1930$percent_farm_tractor 827.97 240.82 3.438 0.000938 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 269.5 on 79 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.1302, Adjusted R-squared: 0.1191
## F-statistic: 11.82 on 1 and 79 DF, p-value: 0.0009376
## Warning in read.dta13("cen1940A.dta"):
## bpld:
## Duplicated factor levels detected - generating unique labels.## Warning in read.dta13("cen1940A.dta"):
## higrade:
## Missing factor labels - no labels assigned.
## Set option generate.factors=T to generate labels.MICHIGAN
## [1] "use `stateicp` as locations\nage between 18,22\nhigrade < 20"## Warning: between() called on numeric vector with S3 class
## `geom_smooth()` using method = 'loess'
Transform response on Log Scale
##
## Call:
## lm(formula = logAvg ~ percent_farm_tractor, data = michPlotSrc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.219400 -0.036979 0.000761 0.041468 0.100975
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.25242 0.01312 171.704 <2e-16 ***
## percent_farm_tractor 0.17868 0.07160 2.496 0.0146 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05275 on 81 degrees of freedom
## Multiple R-squared: 0.07139, Adjusted R-squared: 0.05993
## F-statistic: 6.228 on 1 and 81 DF, p-value: 0.01461
Michigan - Median
## Warning: between() called on numeric vector with S3 class
Michigan - Median Model
##
## Call:
## lm(formula = logMed ~ percent_farm_tractor, data = plotSrc_med_mich)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.25098 -0.06656 0.02238 0.05473 0.14252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.23574 0.02284 97.892 < 2e-16 ***
## percent_farm_tractor 0.33943 0.12465 2.723 0.00792 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09184 on 81 degrees of freedom
## Multiple R-squared: 0.08386, Adjusted R-squared: 0.07255
## F-statistic: 7.415 on 1 and 81 DF, p-value: 0.007921Michigan Median Model County Residuals
## residual county
## 1 -0.187275178 ALCONA
## 2 -0.085014801 ALGER
## 3 0.008819658 ALLEGAN
## 4 -0.079919458 ALPENA
## 5 -0.077488032 ANTRIM
## 6 -0.200353056 ARENAC
## 7 0.025425850 BARAGA
## 8 0.110906381 BARRY
## 9 -0.012837076 BAY
## 10 0.031026497 BENZIE
## 11 0.107743596 BERRIEN
## 12 0.008707873 BRANCH
## 13 0.111953523 CALHOUN
## 14 0.120925711 CASS
## 15 0.036165627 CHARLEVOIX
## 16 -0.065297665 CHEBOYGAN
## 17 0.020599745 CHIPPEWA
## 18 0.033014005 CLARE
## 19 -0.042063207 CLINTON
## 20 0.014172802 CRAWFORD
## 21 0.012887339 DELTA
## 22 0.101942375 DICKINSON
## 23 0.072106438 EATON
## 24 -0.066056828 EMMET
## 25 0.055198261 GENESEE
## 26 -0.069792934 GLADWIN
## 27 0.142516707 GOGEBIC
## 28 0.011545445 GRAND TRAVERSE
## 29 -0.032579808 GRATIOT
## 30 0.100856541 HILLSDALE
## 31 0.039725469 HOUGHTON
## 32 -0.250981531 HURON
## 33 0.068038995 INGHAM
## 34 -0.128137603 IONIA
## 35 0.027636661 IOSCO
## 36 0.130371319 IRON
## 37 -0.101475239 ISABELLA
## 38 0.105659303 JACKSON
## 39 0.100879266 KALAMAZOO
## 40 -0.075924113 KALKASKA
## 41 0.104813894 KENT
## 42 -0.169611558 KEWEENAW
## 43 0.046514135 LAKE
## 44 -0.216084187 LAPEER
## 45 -0.075546269 LEELANAU
## 46 0.044995306 LENAWEE
## 47 0.076575847 LIVINGSTON
## 48 -0.001805789 LUCE
## 49 -0.093317083 MACKINAC/MICHILIM
## 50 0.061968971 MACOMB
## 51 0.039619066 MANISTEE
## 52 0.126050759 MARQUETTE
## 53 0.116743765 MASON
## 54 0.040682349 MECOSTA
## 55 0.025128222 MENOMINEE
## 56 -0.004886346 MIDLAND
## 57 -0.183214337 MISSAUKEE
## 58 -0.032406605 MONROE
## 59 0.024902446 MONTCALM
## 60 0.022383630 MONTMORENCY
## 61 0.114975307 MUSKEGON
## 62 -0.020210397 NEWAYGO
## 63 0.051362076 OAKLAND
## 64 0.027366523 OCEANA
## 65 -0.069876049 OGEMAW
## 66 -0.080123158 ONTONAGON
## 67 0.048305016 OSCEOLA
## 68 -0.010300206 OSCODA
## 69 -0.067060519 OTSEGO
## 70 0.025111206 OTTAWA
## 71 -0.192049448 PRESQUE ISLE
## 72 0.032899974 ROSCOMMON
## 73 -0.040745153 SAGINAW
## 74 -0.013826630 ST CLAIR
## 75 0.117125263 ST JOSEPH
## 76 -0.103298228 SANILAC
## 77 0.014549838 SCHOOLCRAFT
## 78 -0.043640768 SHIAWASSEE
## 79 -0.134888560 TUSCOLA
## 80 0.119313235 VAN BUREN
## 81 0.054252119 WASHTENAW
## 82 0.045686965 WAYNE
## 83 0.047936516 WEXFORDSCATTER DIAGRAM AND REGRESSION FOR MICHIGAN VAM VS EDU
## county avg year state name percent_farm_tractor stateAbb fips
## 44 870 7.881265 1930 26 LAPEER 0.1761294 MI 26087
## colorID logAvg modResid fitted
## 44 3 2.064488 -0.2194003 2.283889MI EDU VS % SSE
## [1] 1.276815VAM 1958 vs % 1950
##
## Call:
## lm(formula = vam_mich1957$VAM ~ mich1950$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -365.46 -196.66 -25.15 184.44 503.44
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 243.3 202.0 1.204 0.233
## mich1950$percent_farm_tractor 140.9 297.2 0.474 0.637
##
## Residual standard error: 241.4 on 62 degrees of freedom
## (19 observations deleted due to missingness)
## Multiple R-squared: 0.003611, Adjusted R-squared: -0.01246
## F-statistic: 0.2247 on 1 and 62 DF, p-value: 0.6372
VAM 1947 vs % 1930
##
## Call:
## lm(formula = vam_mich1947$VAM ~ mich1930$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -427.69 -191.52 -8.99 126.28 552.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.76 62.43 1.15 0.254
## mich1930$percent_farm_tractor 1542.55 349.03 4.42 3.24e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 243.3 on 76 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.2045, Adjusted R-squared: 0.194
## F-statistic: 19.53 on 1 and 76 DF, p-value: 3.24e-05
WISCONSIN
## Warning: between() called on numeric vector with S3 class
## `geom_smooth()` using method = 'loess'
##
## Call:
## lm(formula = logAvg ~ percent_farm_tractor, data = wisPlotSrc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.083969 -0.025500 -0.000201 0.020619 0.092817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.265011 0.009967 227.256 < 2e-16 ***
## percent_farm_tractor 0.100321 0.035121 2.856 0.00566 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03962 on 69 degrees of freedom
## Multiple R-squared: 0.1057, Adjusted R-squared: 0.09279
## F-statistic: 8.159 on 1 and 69 DF, p-value: 0.005656
## Warning in `[<-.factor`(`*tmp*`, thisvar, value = 0): invalid factor level,
## NA generated
WI EDU VS % SSE
## [1] 1.164601VAM 1958 vs % 1950
##
## Call:
## lm(formula = vam_wis1957$VAM ~ wis1950$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -333.26 -192.11 -27.48 103.04 676.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.341 237.072 -0.010 0.992
## wis1950$percent_farm_tractor 497.488 326.784 1.522 0.134
##
## Residual standard error: 251.5 on 54 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04115, Adjusted R-squared: 0.0234
## F-statistic: 2.318 on 1 and 54 DF, p-value: 0.1337
VAM 1947 vs % 1930
##
## Call:
## lm(formula = vam_wis1947$VAM ~ wis1930$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -484.24 -152.21 -90.96 135.29 674.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 55.46 62.58 0.886 0.379
## wis1930$percent_farm_tractor 1046.06 228.27 4.583 2.1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 243.9 on 66 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.2414, Adjusted R-squared: 0.2299
## F-statistic: 21 on 1 and 66 DF, p-value: 2.098e-05
INDIANA
## Warning: between() called on numeric vector with S3 class
## `geom_smooth()` using method = 'loess'
Transform response on Log Scale
##
## Call:
## lm(formula = logAvg ~ percent_farm_tractor, data = inPlotSrc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.128838 -0.020547 0.000339 0.027807 0.068493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.271406 0.009384 242.049 < 2e-16 ***
## percent_farm_tractor 0.243673 0.038546 6.322 9.74e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03588 on 90 degrees of freedom
## Multiple R-squared: 0.3075, Adjusted R-squared: 0.2998
## F-statistic: 39.96 on 1 and 90 DF, p-value: 9.74e-09
SCATTER DIAGRAM AND REGRESSION FOR Indiana VAM VS EDU
## Warning in `[<-.factor`(`*tmp*`, thisvar, value = 0): invalid factor level,
## NA generated
Indiana - Median
## Warning: between() called on numeric vector with S3 class
Indiana - Median Model
##
## Call:
## lm(formula = logMed ~ percent_farm_tractor, data = plotSrc_med_in)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.217651 -0.021463 -0.000837 0.035208 0.135319
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.27671 0.01582 143.9 < 2e-16 ***
## percent_farm_tractor 0.38349 0.06500 5.9 6.3e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0605 on 90 degrees of freedom
## Multiple R-squared: 0.2789, Adjusted R-squared: 0.2709
## F-statistic: 34.81 on 1 and 90 DF, p-value: 6.296e-08Indiana Median Model County Residuals
## residual county
## 1 -0.0039011654 ADAMS
## 2 -0.0045533470 ALLEN
## 3 0.0485600175 BARTHOLOMEW
## 4 -0.0556421083 BENTON
## 5 0.0397637243 BLACKFORD
## 6 0.0172311300 BOONE
## 7 -0.0974781526 BROWN
## 8 -0.0349227644 CARROLL
## 9 0.0353692131 CASS
## 10 -0.0304488200 CLARK
## 11 0.0702713112 CLAY
## 12 -0.0050793908 CLINTON
## 13 -0.2176513073 CRAWFORD
## 14 -0.0297271964 DAVIESS
## 15 -0.0268200706 DEARBORN
## 16 -0.0124768965 DECATUR
## 17 0.0184916342 DE KALB
## 18 0.0351542336 DELAWARE
## 19 -0.1312904249 DUBOIS
## 20 0.0608813534 ELKHART
## 21 -0.1196820773 FAYETTE
## 22 -0.0241780504 FLOYD
## 23 0.0115811945 FOUNTAIN
## 24 -0.1688033073 FRANKLIN
## 25 0.0746289970 FULTON
## 26 0.0253089310 GIBSON
## 27 -0.0007629611 GRANT
## 28 0.0866655157 GREENE
## 29 -0.0014830516 HAMILTON
## 30 0.0116994290 HANCOCK
## 31 -0.0128748566 HARRISON
## 32 0.0177421484 HENDRICKS
## 33 0.0178206224 HENRY
## 34 -0.0025762048 HOWARD
## 35 0.1042119985 HUNTINGTON
## 36 -0.0313739026 JACKSON
## 37 0.0069599110 JASPER
## 38 0.0473013013 JAY
## 39 -0.0122367435 JEFFERSON
## 40 -0.0157585697 JENNINGS
## 41 -0.0086250188 JOHNSON
## 42 0.0122629645 KNOX
## 43 0.0662756138 KOSCIUSKO
## 44 -0.0317447280 LAGRANGE
## 45 -0.0050625322 LAKE
## 46 0.0047870347 LA PORTE
## 47 -0.0144954635 LAWRENCE
## 48 0.0113978554 MADISON
## 49 0.0066256475 MARION
## 50 0.0609437234 MARSHALL
## 51 -0.0171457002 MARTIN
## 52 0.0190393905 MIAMI
## 53 -0.0009467869 MONROE
## 54 0.0065750034 MONTGOMERY
## 55 0.0373660608 MORGAN
## 56 -0.0211253141 NEWTON
## 57 0.0415637340 NOBLE
## 58 0.0745590830 OHIO
## 59 -0.0223773748 ORANGE
## 60 0.0909766279 OWEN
## 61 0.0448138997 PARKE
## 62 -0.1093545276 PERRY
## 63 -0.0145292159 PIKE
## 64 0.0201064860 PORTER
## 65 -0.1790683618 POSEY
## 66 -0.0667393709 PULASKI
## 67 0.0619769431 PUTNAM
## 68 0.0232313852 RANDOLPH
## 69 -0.0342631411 RIPLEY
## 70 -0.0356922635 RUSH
## 71 0.0262023096 ST JOSEPH
## 72 -0.0171502153 SCOTT
## 73 0.0008867328 SHELBY
## 74 -0.0079500668 SPENCER
## 75 -0.0655714442 STARKE
## 76 0.1309497809 STEUBEN
## 77 0.0687769074 SULLIVAN
## 78 -0.1054192161 SWITZERLAND
## 79 -0.0023580977 TIPPECANOE
## 80 0.0011144066 TIPTON
## 81 -0.0256990445 UNION
## 82 0.0413615048 VANDERBURGH
## 83 0.0276761811 VERMILLION
## 84 0.0607328271 VIGO
## 85 0.0055664474 WABASH
## 86 -0.0211584038 WARREN
## 87 -0.0186099550 WARRICK
## 88 -0.0263503943 WASHINGTON
## 89 -0.0009102887 WAYNE
## 90 0.0916333530 WELLS
## 91 -0.0102948964 WHITE
## 92 0.1353186218 WHITLEYIN EDU VS % SSE
## [1] 1.13942VAM 1958 vs % 1950
##
## Call:
## lm(formula = vam_in1957$VAM ~ in1950$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -450.65 -242.12 -46.76 201.85 583.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 142.0 173.0 0.821 0.415
## in1950$percent_farm_tractor 395.2 269.2 1.468 0.147
##
## Residual standard error: 289 on 69 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.03028, Adjusted R-squared: 0.01623
## F-statistic: 2.155 on 1 and 69 DF, p-value: 0.1467
VAM 1947 vs % 1930
##
## Call:
## lm(formula = vam_in1947$VAM ~ in1930$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -325.84 -212.46 -97.32 134.94 702.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 244.49 72.15 3.389 0.00108 **
## in1930$percent_farm_tractor 232.64 303.97 0.765 0.44627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 269.6 on 82 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.007092, Adjusted R-squared: -0.005016
## F-statistic: 0.5857 on 1 and 82 DF, p-value: 0.4463
Illinois
## Warning: between() called on numeric vector with S3 class
## Warning: between() called on numeric vector with S3 class
#LOESS REGRESSION on EDU vs % Tractor
## `geom_smooth()` using method = 'loess'
Transform response on Log Scale
##
## Call:
## lm(formula = logAvg ~ percent_farm_tractor, data = ilPlotSrc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.228625 -0.017533 0.009969 0.029582 0.071982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.22397 0.01067 208.505 < 2e-16 ***
## percent_farm_tractor 0.22051 0.03129 7.047 2.39e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04953 on 100 degrees of freedom
## Multiple R-squared: 0.3318, Adjusted R-squared: 0.3251
## F-statistic: 49.66 on 1 and 100 DF, p-value: 2.385e-10Residuals - Mean Model
SCATTER DIAGRAM AND REGRESSION FOR Illinois VAM VS EDU
## Warning in `[<-.factor`(`*tmp*`, thisvar, value = 0): invalid factor level,
## NA generated
Illinois - Median
## Warning: between() called on numeric vector with S3 class
Illinois - Median Model
##
## Call:
## lm(formula = logMed ~ percent_farm_tractor, data = plotSrc_med_il)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.249730 -0.045203 0.009157 0.059213 0.177344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.18273 0.01665 131.079 < 2e-16 ***
## percent_farm_tractor 0.40905 0.04885 8.373 3.56e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07732 on 100 degrees of freedom
## Multiple R-squared: 0.4121, Adjusted R-squared: 0.4063
## F-statistic: 70.11 on 1 and 100 DF, p-value: 3.56e-13Illinois Median Model County Residuals
## residual county
## 1 0.1159811021 ADAMS
## 2 -0.0219127354 ALEXANDER
## 3 0.0624933542 BOND
## 4 -0.0654303756 BOONE
## 5 -0.0574486943 BROWN
## 6 0.0242888950 BUREAU
## 7 -0.1739178024 CALHOUN
## 8 0.0087382195 CARROLL
## 9 0.0886314774 CASS
## 10 0.0685975463 CHAMPAIGN
## 11 0.0418905544 CHRISTIAN
## 12 0.0611637588 CLARK
## 13 0.0786393457 CLAY
## 14 -0.1803182776 CLINTON
## 15 -0.0684271244 COLES
## 16 0.0623256695 COOK
## 17 0.0456704740 CRAWFORD
## 18 -0.0679222565 CUMBERLAND
## 19 0.0003174398 DE KALB
## 20 0.0369388999 DE WITT
## 21 -0.0121108973 DOUGLAS
## 22 0.0011764439 DU PAGE
## 23 -0.0412973841 EDGAR
## 24 0.1528184335 EDWARDS
## 25 0.0628052418 EFFINGHAM
## 26 -0.0263809189 FAYETTE
## 27 -0.0386041590 FORD
## 28 0.0898362723 FRANKLIN
## 29 0.0884042556 FULTON
## 30 -0.1466355434 GALLATIN
## 31 0.0047574633 GREENE
## 32 -0.0950695919 GRUNDY
## 33 -0.1158571443 HAMILTON
## 34 0.1001316688 HANCOCK
## 35 -0.1132479844 HARDIN
## 36 -0.0219548279 HENDERSON
## 37 0.0420705665 HENRY
## 38 -0.0425400636 IROQUOIS
## 39 0.0372132464 JACKSON
## 40 -0.0460045118 JASPER
## 41 -0.0043966604 JEFFERSON
## 42 -0.0803185571 JERSEY
## 43 0.0003591042 JO DAVIESS
## 44 -0.0141440802 JOHNSON
## 45 -0.0135144444 KANE
## 46 -0.0472771443 KANKAKEE
## 47 -0.0259537624 KENDALL
## 48 0.0643324148 KNOX
## 49 0.0383327769 LAKE
## 50 -0.0812555084 LA SALLE
## 51 0.0350188042 LAWRENCE
## 52 -0.0578001067 LEE
## 53 -0.1113619253 LIVINGSTON
## 54 -0.0888060358 LOGAN
## 55 0.0665464003 MCDONOUGH
## 56 0.0518321475 MCHENRY
## 57 0.0181006265 MCLEAN
## 58 0.0459426482 MACON
## 59 0.0181559531 MACOUPIN
## 60 0.1161359591 MADISON
## 61 0.1773444851 MARION
## 62 0.0183762184 MARSHALL
## 63 -0.0209660543 MASON
## 64 -0.1304346238 MASSAC
## 65 -0.0118754277 MENARD
## 66 0.0700801133 MERCER
## 67 -0.2497303150 MONROE
## 68 0.0308361603 MONTGOMERY
## 69 -0.0019366361 MORGAN
## 70 -0.0427986618 MOULTRIE
## 71 0.0526453814 OGLE
## 72 0.0796242229 PEORIA
## 73 -0.0378438141 PERRY
## 74 -0.0068854448 PIATT
## 75 0.0363154089 PIKE
## 76 -0.1222652841 POPE
## 77 -0.0226174880 PULASKI
## 78 -0.0952511555 PUTNAM
## 79 -0.0634426356 RANDOLPH
## 80 0.0682259564 RICHLAND
## 81 0.0668327657 ROCK ISLAND
## 82 0.0123034269 ST CLAIR
## 83 0.0920240633 SALINE
## 84 0.0860022708 SANGAMON
## 85 0.0215368204 SCHUYLER
## 86 -0.0224760426 SCOTT
## 87 0.0249810386 SHELBY
## 88 0.0324385486 STARK
## 89 0.0598519133 STEPHENSON
## 90 0.0399708018 TAZEWELL
## 91 -0.0599806613 UNION
## 92 0.0572966517 VERMILION
## 93 0.1091509767 WABASH
## 94 0.0406126587 WARREN
## 95 -0.1551419151 WASHINGTON
## 96 -0.1299853400 WAYNE
## 97 0.0551008245 WHITE
## 98 -0.0288185389 WHITESIDE
## 99 -0.0702678619 WILL
## 100 0.0871343160 WILLIAMSON
## 101 0.0747176896 WINNEBAGO
## 102 0.0095765416 WOODFORDIL EDU VS % SSE
## [1] 1.239245VAM 1958 vs % 1950
##
## Call:
## lm(formula = vam_il1957$VAM ~ il1950$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -360.14 -182.35 -65.27 114.67 678.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -37.6 133.4 -0.282 0.7787
## il1950$percent_farm_tractor 455.0 186.6 2.438 0.0169 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 254.5 on 82 degrees of freedom
## (18 observations deleted due to missingness)
## Multiple R-squared: 0.06759, Adjusted R-squared: 0.05622
## F-statistic: 5.944 on 1 and 82 DF, p-value: 0.01693
VAM 1947 vs % 1930
##
## Call:
## lm(formula = il_vam$VAM ~ il1930$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -360.67 -162.22 -84.38 119.71 713.75
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 90.09 52.08 1.730 0.08673 .
## il1930$percent_farm_tractor 436.14 152.79 2.855 0.00524 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 241.8 on 100 degrees of freedom
## Multiple R-squared: 0.07535, Adjusted R-squared: 0.0661
## F-statistic: 8.149 on 1 and 100 DF, p-value: 0.00524
IOWA
## Warning: between() called on numeric vector with S3 class
LOESS REGRESSION on EDU vs % Tractor
## `geom_smooth()` using method = 'loess'
##
## Call:
## lm(formula = logAvg ~ percent_farm_tractor, data = iaPlotSrc)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07421 -0.02169 0.00363 0.02606 0.07078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.30712 0.01167 197.644 <2e-16 ***
## percent_farm_tractor 0.09378 0.03806 2.464 0.0155 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03359 on 97 degrees of freedom
## Multiple R-squared: 0.05892, Adjusted R-squared: 0.04922
## F-statistic: 6.073 on 1 and 97 DF, p-value: 0.01548Residuals
SCATTER DIAGRAM AND REGRESSION FOR Indiana VAM VS EDU
IA EDU VS % Sum of Squares Error
## [1] 1.128052VAM 1958 vs % 1950
##
## Call:
## lm(formula = vam_ia1957$VAM ~ ia1950$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -186.93 -116.61 -54.65 28.86 770.95
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -132.8 197.6 -0.672 0.503
## ia1950$percent_farm_tractor 384.9 249.0 1.546 0.126
##
## Residual standard error: 190 on 86 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.02703, Adjusted R-squared: 0.01572
## F-statistic: 2.389 on 1 and 86 DF, p-value: 0.1258
VAM 1947 vs % 1930
##
## Call:
## lm(formula = ia_vam$VAM ~ ia1930$percent_farm_tractor)
##
## Residuals:
## Min 1Q Median 3Q Max
## -132.30 -105.02 -80.41 -4.50 797.25
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 97.02 66.75 1.454 0.149
## ia1930$percent_farm_tractor 98.86 217.61 0.454 0.651
##
## Residual standard error: 192.1 on 97 degrees of freedom
## Multiple R-squared: 0.002123, Adjusted R-squared: -0.008164
## F-statistic: 0.2064 on 1 and 97 DF, p-value: 0.6506
Iowa - Median
## Warning: between() called on numeric vector with S3 class
Iowa - Median Model
##
## Call:
## lm(formula = logMed ~ percent_farm_tractor, data = plotSrc_med_ia)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17834 -0.02710 0.01407 0.02660 0.12513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.33110 0.02033 114.680 <2e-16 ***
## percent_farm_tractor 0.15680 0.06627 2.366 0.02 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05849 on 97 degrees of freedom
## Multiple R-squared: 0.05457, Adjusted R-squared: 0.04482
## F-statistic: 5.599 on 1 and 97 DF, p-value: 0.01996Iowa Median Model County Residuals
## residual county
## 1 -0.0698771308 ADAIR
## 2 -0.0626695952 ADAMS
## 3 -0.0577524624 ALLAMAKEE
## 4 -0.0425195858 APPANOOSE
## 5 -0.0703218871 AUDUBON
## 6 0.0135565295 BENTON
## 7 0.0168034522 BLACK HAWK
## 8 0.0183828271 BOONE
## 9 0.0166698887 BREMER
## 10 0.0393247618 BUCHANAN
## 11 0.0876183606 BUENA VISTA
## 12 -0.0639751372 BUTLER
## 13 0.0068539027 CALHOUN
## 14 -0.0807633513 CARROLL
## 15 0.0240589330 CASS
## 16 0.0178388902 CEDAR
## 17 0.0205481719 CERRO GORDO
## 18 0.0012142122 CHEROKEE
## 19 -0.0586279740 CHICKASAW
## 20 0.0326358223 CLARKE
## 21 0.0875095949 CLAY
## 22 -0.0779393929 CLAYTON
## 23 0.0130968125 CLINTON
## 24 -0.0896821411 CRAWFORD
## 25 0.0067905428 DALLAS
## 26 -0.0489229649 DAVIS
## 27 0.0487135367 DECATUR
## 28 -0.0626068575 DELAWARE
## 29 0.0167590460 DES MOINES
## 30 0.0142671137 DICKINSON
## 31 -0.0653086956 DUBUQUE
## 32 0.0084627390 EMMET
## 33 0.0379903618 FAYETTE
## 34 0.0193618671 FLOYD
## 35 0.0102304642 FRANKLIN
## 36 0.0313653750 FREMONT
## 37 0.0089974723 GREENE
## 38 0.0144647967 GRUNDY
## 39 0.0279032762 GUTHRIE
## 40 0.0021000562 HAMILTON
## 41 -0.0890211419 HANCOCK
## 42 0.0154828035 HARDIN
## 43 0.0202798136 HARRISON
## 44 0.0247915724 HENRY
## 45 -0.0648661216 HOWARD
## 46 0.0007743202 HUMBOLDT
## 47 0.0078110812 IDA
## 48 0.0084270726 IOWA
## 49 -0.1111447334 JACKSON
## 50 0.0226355695 JASPER
## 51 0.0375409642 JEFFERSON
## 52 0.0178091783 JOHNSON
## 53 -0.0669817399 JONES
## 54 0.0338417038 KEOKUK
## 55 -0.0001827888 KOSSUTH
## 56 0.0275708975 LEE
## 57 0.1251309801 LINN
## 58 0.0104525307 LOUISA
## 59 0.0466248762 LUCAS
## 60 -0.1783359639 LYON/BUNCOMBE
## 61 0.0256228815 MADISON
## 62 -0.0621280035 MAHASKA
## 63 -0.0591260627 MARION
## 64 0.0109899381 MARSHALL
## 65 0.0186983518 MILLS
## 66 0.0217683251 MITCHELL
## 67 0.0002400920 MONONA
## 68 0.0518968927 MONROE
## 69 0.1055478112 MONTGOMERY
## 70 -0.0777806925 MUSCATINE
## 71 0.0042294249 O BRIEN
## 72 -0.0844676618 OSCEOLA
## 73 0.0227912237 PAGE
## 74 0.0126635703 PALO ALTO
## 75 -0.0917707364 PLYMOUTH
## 76 -0.0116855854 POCAHONTAS
## 77 0.1114975678 POLK
## 78 0.0099677272 POTTAWATTAMIE
## 79 0.0967193630 POWESHIEK
## 80 0.0373701459 RINGGOLD
## 81 0.0026975988 SAC
## 82 0.0035293703 SCOTT
## 83 0.0146905446 SHELBY
## 84 -0.1742005144 SIOUX
## 85 0.1005775055 STORY
## 86 0.0161494265 TAMA
## 87 0.0400091263 TAYLOR
## 88 0.0384390773 UNION
## 89 0.0414439818 VAN BUREN
## 90 0.0353212688 WAPELLO
## 91 0.1232005205 WARREN
## 92 0.0191529246 WASHINGTON
## 93 0.0420458200 WAYNE
## 94 -0.0061101406 WEBSTER
## 95 0.0212120364 WINNEBAGO
## 96 -0.1694246938 WINNESHIEK
## 97 0.0140698129 WOODBURY
## 98 0.0144878191 WORTH
## 99 -0.0015285639 WRIGHT