New INC Analysis
Harshit
2024-06-11
1 Lost Margin
1.1 2019 Lost Margin
## Rows: 91669 Columns: 45
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (19): State_Name, Candidate, Sex, Party, Candidate_Type, Constituency_Na...
## dbl (20): Assembly_No, Constituency_No, Year, month, Poll_No, DelimID, Posit...
## lgl (6): last_poll, Same_Constituency, Same_Party, Turncoat, Incumbent, Rec...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 8902 Columns: 51
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (20): State_Code, State_Name, idi, Constituency_Name, Constituency_Type,...
## dbl (31): Constituency_No, Position, Vote_Share_Percentage, Margin_Percentag...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.| margin_range | N |
|---|---|
| 0-5 | 111 |
| 10.1-20 | 44 |
| 20.1-30 | 67 |
| 5.1-10 | 30 |
| above 30 | 117 |
1.2 2024 Lost Margin
| margin_range | N |
|---|---|
| 0-5 | 44 |
| 10.1-20 | 25 |
| 20.1-30 | 40 |
| 5.1-10 | 13 |
| above 30 | 107 |
1.3 Losing margin in 2019 and Win in 2024
| margin_range | N |
|---|---|
| 0-5 | 1 |
| 10.1-20 | 8 |
| 20.1-30 | 12 |
| 5.1-10 | 1 |
| above 30 | 36 |
| Retained | 41 |
4 Regression Multivariate
4.1 Wrangling
Constituency Type- Dummy SC=1, Rest=0
Rural Population- continuous
Vote share- continuous
Margin- continuous
4.2 Model 1 with Position=Vote Share+Rural Population+ SC
##
## Call:
## glm(formula = position_dummy ~ vote_share_percentage + constituency_type_new +
## rural_pop, family = "binomial", data = .)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## Intercept -9.224e+00 1.247e+00 -7.400 1.37e-13 ***
## Vote Share 2.172e-01 2.830e-02 7.676 1.64e-14 ***
## SC 4.553e-01 4.387e-01 1.038 0.299
## Rural Population -4.122e-07 2.573e-07 -1.602 0.109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 385.89 on 312 degrees of freedom
## Residual deviance: 226.14 on 309 degrees of freedom
## (15 observations deleted due to missingness)
## AIC: 234.14
##
## Number of Fisher Scoring iterations: 6## Data were 'prettified'. Consider using `terms="vote_share_percentage
## [all]"` to get smooth plots.
##Model 2
##
## Call:
## glm(formula = position_dummy ~ vote_share_percentage, family = "binomial",
## data = .)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## Intercept -10.04785 1.24249 -8.087 6.12e-16 ***
## Vote Share 0.22330 0.02826 7.903 2.73e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 401.74 on 327 degrees of freedom
## Residual deviance: 235.84 on 326 degrees of freedom
## AIC: 239.84
##
## Number of Fisher Scoring iterations: 6## Data were 'prettified'. Consider using `terms="vote_share_percentage
## [all]"` to get smooth plots.
4.3 Model 3
##
## Call:
## glm(formula = position_dummy ~ vote_share_percentage + constituency_type_new +
## rural_pop + margin_percentage, family = "binomial", data = .)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## Intercept -1.632e+01 4.345e+00 -3.757 0.000172 ***
## Vote Share 6.564e-01 1.664e-01 3.945 8e-05 ***
## SC 3.171e-01 1.417e+00 0.224 0.822961
## Rural Population -8.775e-07 1.060e-06 -0.828 0.407784
## Margin -4.532e-01 1.244e-01 -3.643 0.000270 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 385.893 on 312 degrees of freedom
## Residual deviance: 25.441 on 308 degrees of freedom
## (15 observations deleted due to missingness)
## AIC: 35.441
##
## Number of Fisher Scoring iterations: 10## Data were 'prettified'. Consider using `terms="vote_share_percentage
## [all]"` to get smooth plots.
5 Turnout Change
5.1 NDA
##
## Call:
## glm(formula = category ~ turnout_change_1924 + state_name, family = "binomial",
## data = df_pc_24_1)
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 18.42855 6522.63814 0.003
## turnout_change_1924 -0.13890 0.04602 -3.018
## state_nameAndhra_Pradesh -16.91717 6522.63816 -0.003
## state_nameArunachal_Pradesh -0.33511 7988.19374 0.000
## state_nameBihar -17.72150 6522.63815 -0.003
## state_nameChandigarh -37.35855 9224.40365 -0.004
## state_nameChhattisgarh -15.94233 6522.63822 -0.002
## state_nameDadra_&_Nagar_Haveli_and_Daman_&_Diu -19.13280 6522.63830 -0.003
## state_nameDelhi -17.41999 6522.63819 -0.003
## state_nameGoa -18.29729 6522.63829 -0.003
## state_nameGujarat -16.48032 6522.63818 -0.003
## state_nameHaryana -19.18255 6522.63817 -0.003
## state_nameHimachal_Pradesh -0.04243 7290.27773 0.000
## state_nameJharkhand -17.90375 6522.63816 -0.003
## state_nameKarnataka -17.82311 6522.63815 -0.003
## state_nameKerala -22.33532 6522.63822 -0.003
## state_nameLadakh -36.81683 9224.40365 -0.004
## state_nameLakshadweep -37.13630 9224.40365 -0.004
## state_nameMadhya_Pradesh -16.88872 6522.63816 -0.003
## state_nameMaharashtra -18.96574 6522.63814 -0.003
## state_nameManipur -37.47975 7491.39123 -0.005
## state_nameMeghalaya -36.31409 7470.80030 -0.005
## state_nameMizoram -37.85305 7988.56780 -0.005
## state_nameNagaland -40.49361 9224.40371 -0.004
## state_nameOdisha -16.35323 6522.63817 -0.003
## state_namePuducherry -37.31966 9224.40365 -0.004
## state_namePunjab -37.44600 6720.07375 -0.006
## state_nameRajasthan -18.84492 6522.63815 -0.003
## state_nameSikkim -18.58690 6522.63829 -0.003
## state_nameTamil_Nadu -37.37244 6600.55130 -0.006
## state_nameTelangana -18.18845 6522.63816 -0.003
## state_nameTripura -0.05741 7982.31940 0.000
## state_nameUttar_Pradesh -19.02062 6522.63814 -0.003
## state_nameUttarakhand -0.47071 7139.57116 0.000
## state_nameWest_Bengal -19.69124 6522.63815 -0.003
## Pr(>|z|)
## (Intercept) 0.99775
## turnout_change_1924 0.00254 **
## state_nameAndhra_Pradesh 0.99793
## state_nameArunachal_Pradesh 0.99997
## state_nameBihar 0.99783
## state_nameChandigarh 0.99677
## state_nameChhattisgarh 0.99805
## state_nameDadra_&_Nagar_Haveli_and_Daman_&_Diu 0.99766
## state_nameDelhi 0.99787
## state_nameGoa 0.99776
## state_nameGujarat 0.99798
## state_nameHaryana 0.99765
## state_nameHimachal_Pradesh 1.00000
## state_nameJharkhand 0.99781
## state_nameKarnataka 0.99782
## state_nameKerala 0.99727
## state_nameLadakh 0.99682
## state_nameLakshadweep 0.99679
## state_nameMadhya_Pradesh 0.99793
## state_nameMaharashtra 0.99768
## state_nameManipur 0.99601
## state_nameMeghalaya 0.99612
## state_nameMizoram 0.99622
## state_nameNagaland 0.99650
## state_nameOdisha 0.99800
## state_namePuducherry 0.99677
## state_namePunjab 0.99555
## state_nameRajasthan 0.99769
## state_nameSikkim 0.99773
## state_nameTamil_Nadu 0.99548
## state_nameTelangana 0.99778
## state_nameTripura 0.99999
## state_nameUttar_Pradesh 0.99767
## state_nameUttarakhand 0.99995
## state_nameWest_Bengal 0.99759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 772.15 on 556 degrees of freedom
## Residual deviance: 523.89 on 522 degrees of freedom
## (21 observations deleted due to missingness)
## AIC: 593.89
##
## Number of Fisher Scoring iterations: 17## Data were 'prettified'. Consider using `terms="turnout_change_1924
## [all]"` to get smooth plots.
5.2 INDIA
##
## Call:
## glm(formula = category ~ turnout_change_1924 + state_name, family = "binomial",
## data = df_pc_24_1_new)
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) -18.48688 4612.20194 -0.004
## turnout_change_1924 0.07999 0.04317 1.853
## state_nameAndhra_Pradesh -0.11782 4715.69908 0.000
## state_nameArunachal_Pradesh 0.19306 6522.49689 0.000
## state_nameBihar 17.26318 4612.20195 0.004
## state_nameChandigarh 37.26253 7988.56815 0.005
## state_nameChhattisgarh 15.88458 4612.20206 0.003
## state_nameDadra_&_Nagar_Haveli_and_Daman_&_Diu 0.37260 5948.63285 0.000
## state_nameDelhi 0.08277 5098.50762 0.000
## state_nameGoa 18.41128 4612.20216 0.004
## state_nameGujarat 15.48274 4612.20205 0.003
## state_nameHaryana 18.73203 4612.20198 0.004
## state_nameHimachal_Pradesh 0.02527 5647.86465 0.000
## state_nameJharkhand 17.35181 4612.20197 0.004
## state_nameKarnataka 17.43004 4612.20196 0.004
## state_nameKerala 18.87553 4612.20196 0.004
## state_nameLadakh -0.18158 7988.56815 0.000
## state_nameLakshadweep 18.56847 4612.20216 0.004
## state_nameMadhya_Pradesh 0.25647 4735.63110 0.000
## state_nameMaharashtra 18.33295 4612.20195 0.004
## state_nameManipur 37.40793 6498.87451 0.006
## state_nameMeghalaya 17.55451 4612.20211 0.004
## state_nameMizoram 0.41516 7988.56815 0.000
## state_nameNagaland 39.06796 7988.56821 0.005
## state_nameOdisha 15.15003 4612.20205 0.003
## state_namePuducherry 37.24013 7988.56816 0.005
## state_namePunjab 17.92078 4612.20196 0.004
## state_nameRajasthan 18.24564 4612.20196 0.004
## state_nameSikkim 0.01200 7988.56815 0.000
## state_nameTamil_Nadu 37.27403 4728.47258 0.008
## state_nameTelangana 17.23293 4612.20196 0.004
## state_nameTripura 0.03436 6520.26312 0.000
## state_nameUttar_Pradesh 18.53661 4612.20194 0.004
## state_nameUttarakhand 0.27378 5454.94407 0.000
## state_nameWest_Bengal 18.04039 4612.20195 0.004
## Pr(>|z|)
## (Intercept) 0.9968
## turnout_change_1924 0.0639 .
## state_nameAndhra_Pradesh 1.0000
## state_nameArunachal_Pradesh 1.0000
## state_nameBihar 0.9970
## state_nameChandigarh 0.9963
## state_nameChhattisgarh 0.9973
## state_nameDadra_&_Nagar_Haveli_and_Daman_&_Diu 1.0000
## state_nameDelhi 1.0000
## state_nameGoa 0.9968
## state_nameGujarat 0.9973
## state_nameHaryana 0.9968
## state_nameHimachal_Pradesh 1.0000
## state_nameJharkhand 0.9970
## state_nameKarnataka 0.9970
## state_nameKerala 0.9967
## state_nameLadakh 1.0000
## state_nameLakshadweep 0.9968
## state_nameMadhya_Pradesh 1.0000
## state_nameMaharashtra 0.9968
## state_nameManipur 0.9954
## state_nameMeghalaya 0.9970
## state_nameMizoram 1.0000
## state_nameNagaland 0.9961
## state_nameOdisha 0.9974
## state_namePuducherry 0.9963
## state_namePunjab 0.9969
## state_nameRajasthan 0.9968
## state_nameSikkim 1.0000
## state_nameTamil_Nadu 0.9937
## state_nameTelangana 0.9970
## state_nameTripura 1.0000
## state_nameUttar_Pradesh 0.9968
## state_nameUttarakhand 1.0000
## state_nameWest_Bengal 0.9969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 888.25 on 705 degrees of freedom
## Residual deviance: 643.77 on 671 degrees of freedom
## (31 observations deleted due to missingness)
## AIC: 713.77
##
## Number of Fisher Scoring iterations: 17## Data were 'prettified'. Consider using `terms="turnout_change_1924
## [all]"` to get smooth plots.
6 INC Contested Seats and Performance
6.1 2024
| Category | INC(vs BJP) | INC(vs NDA) | INC(vs Regional) |
|---|---|---|---|
| Contested Seats | 287.0 | 328.00 | 41.00 |
| Total Won | 84.0 | 99.00 | 15.00 |
| Vote Share | 36.2 | 34.63 | 23.13 |