* Analysis of Russian Casualties in Ukraine on Tue May 09 11:27:42 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 107-row dataframe of 15 columns:
DayNum, Date, Soldiers, Tanks, ArmoredCombatVehicles, Artillery, MultipleLaunchRocketSystems, AirDefenceSystems, MilitaryJets, Helicopters, Drones, CruiseMissiles, WarshipsAndBoats, VehiclesAndFuelTanks, SpecialEquipment
- 106 rows had complete data
- 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] 0.8243576 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
-44.116 -15.974 7.618 16.884 29.659
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2708.670 135.314 -20.02 <2e-16 ***
x 12.408 0.468 26.51 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 18.55 on 104 degrees of freedom
Multiple R-squared: 0.8711, Adjusted R-squared: 0.8699
F-statistic: 703 on 1 and 104 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
-44.603 -13.081 -1.578 11.419 37.903
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1687.8833 78.3979 -21.53 <2e-16 ***
x 8.4982 0.2595 32.74 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 15.36 on 104 degrees of freedom
Multiple R-squared: 0.9116, Adjusted R-squared: 0.9107
F-statistic: 1072 on 1 and 104 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
-2.28580 -0.71821 -0.02689 0.74799 1.47950
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91.4910 4.1984 21.79 <2e-16 ***
x 0.6543 0.0139 47.08 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8227 on 104 degrees of freedom
Multiple R-squared: 0.9552, Adjusted R-squared: 0.9548
F-statistic: 2216 on 1 and 104 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
-57.574 -17.184 2.652 20.324 36.538
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 799.14543 4.56253 175.15 <2e-16 ***
x 1.48575 0.07388 20.11 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.37 on 104 degrees of freedom
Multiple R-squared: 0.7955, Adjusted R-squared: 0.7935
F-statistic: 404.5 on 1 and 104 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
-5.8375 -1.0732 0.3165 1.2093 2.9737
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.925e+02 3.498e-01 836.04 <2e-16 ***
x 1.780e-01 5.665e-03 31.43 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.792 on 104 degrees of freedom
Multiple R-squared: 0.9047, Adjusted R-squared: 0.9038
F-statistic: 987.6 on 1 and 104 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
-5.8994 -0.2517 0.1790 0.5796 1.4019
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.827e+02 2.085e-01 1355.53 <2e-16 ***
x 1.205e-01 3.376e-03 35.68 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.068 on 104 degrees of freedom
Multiple R-squared: 0.9245, Adjusted R-squared: 0.9238
F-statistic: 1273 on 1 and 104 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
-3284.2 -1291.6 100.2 1027.9 2976.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.220e+05 3.149e+02 387.5 <2e-16 ***
x 7.123e+02 5.099e+00 139.7 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1613 on 104 degrees of freedom
Multiple R-squared: 0.9947, Adjusted R-squared: 0.9946
F-statistic: 1.951e+04 on 1 and 104 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
-59.279 -19.522 -3.377 18.233 60.240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.160e+03 5.739e+00 550.55 <2e-16 ***
x 5.863e+00 9.292e-02 63.09 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 29.39 on 104 degrees of freedom
Multiple R-squared: 0.9745, Adjusted R-squared: 0.9743
F-statistic: 3981 on 1 and 104 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
-47.380 -14.908 -3.751 11.973 59.017
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.285e+03 4.676e+00 1344.1 <2e-16 ***
x 9.428e+00 7.571e-02 124.5 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.95 on 104 degrees of freedom
Multiple R-squared: 0.9933, Adjusted R-squared: 0.9933
F-statistic: 1.551e+04 on 1 and 104 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
-33.997 -11.369 0.171 7.790 40.482
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.099e+03 3.132e+00 670.3 <2e-16 ***
x 8.208e+00 5.071e-02 161.8 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 16.04 on 104 degrees of freedom
Multiple R-squared: 0.996, Adjusted R-squared: 0.996
F-statistic: 2.619e+04 on 1 and 104 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
-9.0401 -4.3569 0.0385 2.8477 10.9564
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 442.55002 0.98453 449.50 <2e-16 ***
x 1.08324 0.01594 67.95 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.042 on 104 degrees of freedom
Multiple R-squared: 0.978, Adjusted R-squared: 0.9778
F-statistic: 4617 on 1 and 104 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
-6.2533 -2.8822 -0.0945 2.0766 8.0753
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 216.26845 0.63709 339.46 <2e-16 ***
x 0.83317 0.01032 80.77 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.263 on 104 degrees of freedom
Multiple R-squared: 0.9843, Adjusted R-squared: 0.9842
F-statistic: 6523 on 1 and 104 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
-46.819 -24.051 -8.487 21.336 120.307
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.848e+03 6.088e+00 303.6 <2e-16 ***
x 6.033e+00 9.858e-02 61.2 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 31.18 on 104 degrees of freedom
Multiple R-squared: 0.973, Adjusted R-squared: 0.9727
F-statistic: 3746 on 1 and 104 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
-28.69 -18.27 -11.28 19.52 84.12
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.933e+03 4.902e+00 1006.3 <2e-16 ***
x 8.822e+00 7.937e-02 111.1 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 25.1 on 104 degrees of freedom
Multiple R-squared: 0.9917, Adjusted R-squared: 0.9916
F-statistic: 1.235e+04 on 1 and 104 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
-19.0919 -5.3853 0.9368 5.8729 21.5022
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 172.1394 1.8961 90.78 <2e-16 ***
x 1.7791 0.0307 57.95 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 9.711 on 104 degrees of freedom
Multiple R-squared: 0.97, Adjusted R-squared: 0.9697
F-statistic: 3358 on 1 and 104 DF, p-value: < 2.2e-16
Call:
lm(formula = DayNum ~ Soldiers, data = ukrRusCasualties)
Residuals:
Min 1Q Median 3Q Max
-4.1065 -1.4239 -0.2101 1.8040 4.8695
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.701e+02 1.617e+00 -105.2 <2e-16 ***
Soldiers 1.396e-03 9.998e-06 139.7 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.258 on 104 degrees of freedom
Multiple R-squared: 0.9947, Adjusted R-squared: 0.9946
F-statistic: 1.951e+04 on 1 and 104 DF, p-value: < 2.2e-16
fit lwr upr
1 2023-05-11 2023-05-06 2023-05-15
* Analysis of Russian Casualties in Ukraine completed Tue May 09 11:27:46 2023 (4.0 sec elapsed).