* Analysis of Russian Casualties in Ukraine on Wed May 17 13:20:41 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: ----------------------- * Using already-loaded value of ukrRusCasualties. Analysis of data: ----------------- * Summarizing argument of uselessness of WarshipsAndBoats to ./results/warshipsAndBoats-useless.png. * Range of correlations: [1] 0.5058673 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 -26.643 -16.638 3.373 9.357 44.357 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2003.978 542.567 -3.694 0.000524 *** x 10.006 1.853 5.398 1.61e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 18.43 on 53 degrees of freedom Multiple R-squared: 0.3548, Adjusted R-squared: 0.3426 F-statistic: 29.14 on 1 and 53 DF, p-value: 1.61e-06 * 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 -23.084 -12.786 -2.487 12.916 47.916 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2237.800 740.803 -3.021 0.00388 ** x 10.298 2.412 4.269 8.15e-05 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 19.8 on 53 degrees of freedom Multiple R-squared: 0.2559, Adjusted R-squared: 0.2419 F-statistic: 18.23 on 1 and 53 DF, p-value: 8.153e-05 * 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 -0.7411 -0.4652 0.2589 0.2589 0.6728 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -56.75027 17.58318 -3.228 0.00214 ** x 1.13796 0.05725 19.876 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4698 on 53 degrees of freedom Multiple R-squared: 0.8817, Adjusted R-squared: 0.8795 F-statistic: 395 on 1 and 53 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 -24.742 -9.508 1.212 9.675 24.819 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 826.2865 9.5569 86.46 < 2e-16 *** x 1.1284 0.1075 10.50 1.49e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 13.08 on 53 degrees of freedom Multiple R-squared: 0.6753, Adjusted R-squared: 0.6692 F-statistic: 110.2 on 1 and 53 DF, p-value: 1.487e-14 * 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 -0.82884 -0.41420 -0.00831 0.38493 1.03449 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.019e+02 3.805e-01 793.37 <2e-16 *** x 5.983e-02 4.279e-03 13.98 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5206 on 53 degrees of freedom Multiple R-squared: 0.7867, Adjusted R-squared: 0.7827 F-statistic: 195.5 on 1 and 53 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 -0.8989 -0.3867 0.0123 0.3475 0.9234 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.861e+02 3.698e-01 773.73 <2e-16 *** x 7.593e-02 4.158e-03 18.26 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.5059 on 53 degrees of freedom Multiple R-squared: 0.8629, Adjusted R-squared: 0.8603 F-statistic: 333.5 on 1 and 53 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 -475.56 -183.96 -20.29 235.73 333.09 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.333e+05 1.777e+02 750.1 <2e-16 *** x 5.773e+02 1.999e+00 288.8 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 243.2 on 53 degrees of freedom Multiple R-squared: 0.9994, Adjusted R-squared: 0.9994 F-statistic: 8.343e+04 on 1 and 53 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 -19.6017 -3.4931 0.5379 5.5069 12.6154 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.382e+03 4.659e+00 725.84 <2e-16 *** x 3.246e+00 5.239e-02 61.96 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 6.375 on 53 degrees of freedom Multiple R-squared: 0.9864, Adjusted R-squared: 0.9861 F-statistic: 3839 on 1 and 53 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 -20.623 -9.228 -3.870 6.686 37.871 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6436.364 9.335 689.46 <2e-16 *** x 7.679 0.105 73.15 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 12.77 on 53 degrees of freedom Multiple R-squared: 0.9902, Adjusted R-squared: 0.99 F-statistic: 5351 on 1 and 53 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 -38.126 -17.626 -2.129 15.101 66.151 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2013.6903 17.5989 114.42 <2e-16 *** x 9.3634 0.1979 47.31 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 24.08 on 53 degrees of freedom Multiple R-squared: 0.9769, Adjusted R-squared: 0.9764 F-statistic: 2239 on 1 and 53 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 -8.0639 -2.2461 0.7431 2.2702 4.1552 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 470.86011 2.30969 203.86 <2e-16 *** x 0.77006 0.02597 29.65 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.16 on 53 degrees of freedom Multiple R-squared: 0.9431, Adjusted R-squared: 0.9421 F-statistic: 879 on 1 and 53 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 -6.8451 -3.5903 0.4292 2.6970 6.6127 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 224.1992 2.6949 83.2 <2e-16 *** x 0.7516 0.0303 24.8 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 3.687 on 53 degrees of freedom Multiple R-squared: 0.9207, Adjusted R-squared: 0.9192 F-statistic: 615.2 on 1 and 53 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 -50.148 -29.512 -1.605 24.187 82.580 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1596.1174 24.4442 65.30 <2e-16 *** x 9.2181 0.2749 33.53 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 33.44 on 53 degrees of freedom Multiple R-squared: 0.955, Adjusted R-squared: 0.9541 F-statistic: 1125 on 1 and 53 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 -38.705 -16.205 1.774 19.280 36.382 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4748.9217 16.0844 295.25 <2e-16 *** x 11.0745 0.1809 61.23 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 22.01 on 53 degrees of freedom Multiple R-squared: 0.9861, Adjusted R-squared: 0.9858 F-statistic: 3749 on 1 and 53 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 -8.5882 -2.9436 -0.9941 3.7752 13.5286 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 121.62565 3.53363 34.42 <2e-16 *** x 2.42970 0.03974 61.15 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 4.835 on 53 degrees of freedom Multiple R-squared: 0.986, Adjusted R-squared: 0.9858 F-statistic: 3739 on 1 and 53 DF, p-value: < 2.2e-16 Call: lm(formula = DayNum ~ Soldiers, data = ukrRusCasualties) Residuals: Min 1Q Median 3Q Max -0.58954 -0.41084 0.03804 0.32166 0.80579 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -2.307e+02 1.103e+00 -209.2 <2e-16 *** Soldiers 1.731e-03 5.993e-06 288.8 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.4211 on 53 degrees of freedom Multiple R-squared: 0.9994, Adjusted R-squared: 0.9994 F-statistic: 8.343e+04 on 1 and 53 DF, p-value: < 2.2e-16 fit lwr upr 1 2023-05-17 2023-05-16 2023-05-18 * Analysis of Russian Casualties in Ukraine completed Wed May 17 13:20:42 2023 (1.1 sec elapsed).