* 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).