* Analysis of Russian Casualties in Ukraine on Sun Apr 16 22:20:11 2023
- input data directory: ./data
- results directory: ./results
- transcript to: ./results/ukr-rus-casualties-transcript.txt
Archival of script and data:
----------------------------
* Archived analysis script(s) to ./results:
- ./data/russian-casualties-in-ukraine.tsv
- ./ukr-rus-casualties.r
Loading and QC of data:
-----------------------
* Loading and QCing data from spreadsheet
- Input file: ./data/russian-casualties-in-ukraine.tsv
- Got a 85-row dataframe of 15 columns:
DayNum, Date, Soldiers, Tanks, ArmoredCombatVehicles, Artillery, MultipleLaunchRocketSystems, AirDefenceSystems, MilitaryJets, Helicopters, Drones, CruiseMissiles, WarshipsAndBoats, VehiclesAndFuelTanks, SpecialEquipment
- Checking column types:
o DayNum and Date have appropriate sequence values
o All other columns are nondecreasing positive integers
Analysis of data:
-----------------
* Summarizing argument of uselessness of WarshipsAndBoats to ./results/warshipsAndBoats-useless.png.
* Range of correlations:
[1] 0.8283804 1.0000000
* Doing multivariate correlation chart to ./results/correlation-chart.png
* Biclustering the correlation matrix to ./results/bicluster.png
* Analyzing CruiseMissiles vs Helicopters
- Regression plot to ./results/regress-CruiseMissiles-on-Helicopters.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-44.303 -16.966 2.356 16.697 31.712
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3068.8195 162.3163 -18.91 <2e-16 ***
x 13.6683 0.5636 24.25 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 17.93 on 83 degrees of freedom
Multiple R-squared: 0.8763, Adjusted R-squared: 0.8748
F-statistic: 588.1 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing CruiseMissiles vs MilitaryJets
- Regression plot to ./results/regress-CruiseMissiles-on-MilitaryJets.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-43.349 -13.009 0.538 9.323 38.319
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1770.6376 87.2702 -20.29 <2e-16 ***
x 8.7774 0.2903 30.23 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 14.71 on 83 degrees of freedom
Multiple R-squared: 0.9168, Adjusted R-squared: 0.9157
F-statistic: 914 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing Helicopters vs MilitaryJets
- Regression plot to ./results/regress-Helicopters-on-MilitaryJets.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-2.68392 -0.48224 0.06818 0.57973 1.41692
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 103.9266 4.5685 22.75 <2e-16 ***
x 0.6124 0.0152 40.29 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7701 on 83 degrees of freedom
Multiple R-squared: 0.9514, Adjusted R-squared: 0.9508
F-statistic: 1624 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing CruiseMissiles vs DayNum
- Regression plot to ./results/regress-CruiseMissiles-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-47.959 -17.419 1.979 19.315 33.307
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 787.70084 4.82321 163.3 <2e-16 ***
x 1.85155 0.09742 19.0 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 22.04 on 83 degrees of freedom
Multiple R-squared: 0.8131, Adjusted R-squared: 0.8109
F-statistic: 361.2 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing MilitaryJets vs DayNum
- Regression plot to ./results/regress-MilitaryJets-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-4.6449 -0.6826 0.0343 1.0626 2.3739
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.912e+02 3.019e-01 964.55 <2e-16 ***
x 2.170e-01 6.098e-03 35.58 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.379 on 83 degrees of freedom
Multiple R-squared: 0.9385, Adjusted R-squared: 0.9377
F-statistic: 1266 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing Helicopters vs DayNum
- Regression plot to ./results/regress-Helicopters-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-5.4903 -0.3636 0.1502 0.6221 1.2983
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.822e+02 2.351e-01 1200.51 <2e-16 ***
x 1.338e-01 4.748e-03 28.18 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.074 on 83 degrees of freedom
Multiple R-squared: 0.9054, Adjusted R-squared: 0.9042
F-statistic: 794.1 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing Soldiers vs DayNum
- Regression plot to ./results/regress-Soldiers-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-2750.35 -583.62 96.94 491.15 1708.56
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.207e+05 2.071e+02 582.6 <2e-16 ***
x 7.547e+02 4.184e+00 180.4 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 946.4 on 83 degrees of freedom
Multiple R-squared: 0.9975, Adjusted R-squared: 0.9974
F-statistic: 3.254e+04 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing Tanks vs DayNum
- Regression plot to ./results/regress-Tanks-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-42.403 -7.603 -0.689 7.567 35.298
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.134e+03 3.468e+00 903.79 <2e-16 ***
x 6.652e+00 7.004e-02 94.98 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 15.84 on 83 degrees of freedom
Multiple R-squared: 0.9909, Adjusted R-squared: 0.9908
F-statistic: 9021 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing ArmoredCombatVehicles vs DayNum
- Regression plot to ./results/regress-ArmoredCombatVehicles-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-32.472 -11.259 -2.563 10.376 41.271
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.267e+03 3.771e+00 1661.9 <2e-16 ***
x 9.985e+00 7.617e-02 131.1 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 17.23 on 83 degrees of freedom
Multiple R-squared: 0.9952, Adjusted R-squared: 0.9951
F-statistic: 1.719e+04 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing Artillery vs DayNum
- Regression plot to ./results/regress-Artillery-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-33.120 -12.813 2.902 10.888 31.649
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.104e+03 3.346e+00 628.9 <2e-16 ***
x 8.069e+00 6.758e-02 119.4 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 15.29 on 83 degrees of freedom
Multiple R-squared: 0.9942, Adjusted R-squared: 0.9941
F-statistic: 1.426e+04 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing MultipleLaunchRocketSystems vs DayNum
- Regression plot to ./results/regress-MultipleLaunchRocketSystems-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-8.0468 -3.8249 -0.0793 4.0183 7.8911
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 439.5882 0.9705 452.94 <2e-16 ***
x 1.1746 0.0196 59.92 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.434 on 83 degrees of freedom
Multiple R-squared: 0.9774, Adjusted R-squared: 0.9771
F-statistic: 3590 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing AirDefenceSystems vs DayNum
- Regression plot to ./results/regress-AirDefenceSystems-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-5.9302 -2.1039 0.0189 2.2314 6.5009
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 214.7387 0.6290 341.40 <2e-16 ***
x 0.8832 0.0127 69.52 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.874 on 83 degrees of freedom
Multiple R-squared: 0.9831, Adjusted R-squared: 0.9829
F-statistic: 4833 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing Drones vs DayNum
- Regression plot to ./results/regress-Drones-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-34.399 -18.701 3.148 14.054 38.544
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.871e+03 4.230e+00 442.31 <2e-16 ***
x 5.340e+00 8.545e-02 62.49 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 19.33 on 83 degrees of freedom
Multiple R-squared: 0.9792, Adjusted R-squared: 0.9789
F-statistic: 3905 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing VehiclesAndFuelTanks vs DayNum
- Regression plot to ./results/regress-VehiclesAndFuelTanks-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-32.570 -9.145 2.269 8.905 20.147
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4951.8126 2.6040 1901.6 <2e-16 ***
x 8.2525 0.0526 156.9 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 11.9 on 83 degrees of freedom
Multiple R-squared: 0.9966, Adjusted R-squared: 0.9966
F-statistic: 2.462e+04 on 1 and 83 DF, p-value: < 2.2e-16
* Analyzing SpecialEquipment vs DayNum
- Regression plot to ./results/regress-SpecialEquipment-on-DayNum.png.
Call:
lm(formula = y ~ x, data = data.frame(x = xs, y = ys))
Residuals:
Min 1Q Median 3Q Max
-15.918 -5.413 1.480 5.188 12.946
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 178.5395 1.7427 102.45 <2e-16 ***
x 1.5825 0.0352 44.96 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 7.963 on 83 degrees of freedom
Multiple R-squared: 0.9606, Adjusted R-squared: 0.9601
F-statistic: 2021 on 1 and 83 DF, p-value: < 2.2e-16
* Analysis of Russian Casualties in Ukraine completed Sun Apr 16 22:20:15 2023 (3.6 sec elapsed).