* 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).