1 Distribution Plots
1.1 Full Sample
Show code
TPOSampleJoined %>%
ggplot(aes(LOG_TOT_MCF)) +
geom_density() +
scale_x_continuous(limits = c(-2,5), breaks = c(seq(-2,5,1))) +
scale_y_continuous(limits = c, breaks = c(seq(0,.7,.1))) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "Logged Volume Distribution", x= "Log(10) MCF",y = "Density")
Show code
TPOSampleJoined %>%
ggplot(aes(Pro_Rad_SqRt))+
geom_density() +
scale_x_continuous(limits = c(0, 15), breaks = c(seq(0,15,1))) +
scale_y_continuous(limits = c(0,.25), breaks = c(seq(0,.25,.05))) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "SqRt Radius Distribution", x= "SqRt Procurement Radius",y = "Density")
Show code
TPOSampleJoined <- TPOSampleJoined %>%
filter(TOT_MCF < 16000 & !is.na(TOT_MCF)) %>%
mutate(MILL_COUNTYCD = as.character(MILL_COUNTYCD),
MILL_STATECD = as.character(MILL_STATECD),
MTC = as.character(MTC))
#Remove Outliers from Joined Sample, store as 'UnusedFinal'...These mills provided a Pro Rad during 2019-2021 and can be joined back following model creation
Unused1 <- TPOSampleJoined %>%
filter((Pro_Rad >= 200 | Pro_Rad <= 1))
Unused2 <- TPOSampleJoined %>%
filter(MILL_STATE == "OH" & volt == "low")
Unused3 <- TPOSampleJoined %>%
filter(MILL_STATE == "MI" & Pro_Rad > 150) %>%
filter(!(MILL_NAME %in% Unused1$MILL_NAME))
Unused4 <- TPOSampleJoined %>%
filter(MILL_NAME == "ANTHONY & CO., INC.")
Unused5 <- TPOSampleJoined %>%
filter((Region == "MidAtl" & LOG_TOT_MCF < 1 & Pro_Rad_SqRt > 10) | (Region == "MI" & LOG_TOT_MCF < -1 & Pro_Rad_SqRt >= 10) | (Region == "PA" & LOG_TOT_MCF < 1))
UnusedFinal <- Unused1 %>%
bind_rows(Unused2, Unused3, Unused4, Unused5)
rm(Unused1, Unused2, Unused3, Unused4, Unused5)
TPOSampleJoined <- TPOSampleJoined %>%
filter(!UnusedCheck %in% UnusedFinal$UnusedCheck)
1.2 Model-Obervations Only
Show code
TPOSampleJoined %>%
filter(!is.na(Pro_Rad_SqRt) & !is.na(volt) & !is.na(Region) & !is.na(LOG_TOT_MCF)) %>%
ggplot(aes(LOG_TOT_MCF)) +
geom_density() +
scale_x_continuous(limits = c(-2, 5), breaks = c(seq(-2,5,1))) +
scale_y_continuous(limits = c, breaks = c(seq(0,.7,.1))) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "Logged Volume Distribution", x= "Log(10) MCF",y = "Density")
Show code
TPOSampleJoined %>%
filter(!is.na(Pro_Rad_SqRt) & !is.na(volt) & !is.na(Region) & !is.na(LOG_TOT_MCF)) %>%
ggplot(aes(Pro_Rad_SqRt)) +
geom_density() +
scale_x_continuous(limits = c(0, 15), breaks = c(seq(0,15,1))) +
scale_y_continuous(limits = c(0,.25), breaks = c(seq(0,.25,.05))) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "SqRt Radius Distribution", x= "SqRt Procurement Radius",y = "Density")
2 Model Creation & Summary
Show code
Call:
lm(formula = TPOSampleJoined$Pro_Rad_SqRt ~ TPOSampleJoined$LOG_TOT_MCF *
TPOSampleJoined$Region)
Residuals:
Min 1Q Median 3Q Max
-5.0971 -1.2219 -0.2919 1.2442 6.7138
Coefficients:
Estimate
(Intercept) 5.1673
TPOSampleJoined$LOG_TOT_MCF 1.0110
TPOSampleJoined$RegionLake 1.8902
TPOSampleJoined$RegionMidAtl -0.1994
TPOSampleJoined$RegionNewEngland -1.4989
TPOSampleJoined$RegionPlains -0.4106
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionLake -0.6245
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionMidAtl -0.2587
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionNewEngland 0.2328
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionPlains -0.2247
Std. Error
(Intercept) 0.6690
TPOSampleJoined$LOG_TOT_MCF 0.2575
TPOSampleJoined$RegionLake 0.8133
TPOSampleJoined$RegionMidAtl 0.8785
TPOSampleJoined$RegionNewEngland 0.8727
TPOSampleJoined$RegionPlains 0.8260
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionLake 0.3130
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionMidAtl 0.3366
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionNewEngland 0.3300
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionPlains 0.3242
t value
(Intercept) 7.723
TPOSampleJoined$LOG_TOT_MCF 3.926
TPOSampleJoined$RegionLake 2.324
TPOSampleJoined$RegionMidAtl -0.227
TPOSampleJoined$RegionNewEngland -1.718
TPOSampleJoined$RegionPlains -0.497
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionLake -1.995
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionMidAtl -0.768
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionNewEngland 0.705
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionPlains -0.693
Pr(>|t|)
(Intercept) 0.0000000000000602
TPOSampleJoined$LOG_TOT_MCF 0.0000982166651530
TPOSampleJoined$RegionLake 0.0205
TPOSampleJoined$RegionMidAtl 0.8206
TPOSampleJoined$RegionNewEngland 0.0865
TPOSampleJoined$RegionPlains 0.6193
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionLake 0.0466
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionMidAtl 0.4426
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionNewEngland 0.4809
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionPlains 0.4885
(Intercept) ***
TPOSampleJoined$LOG_TOT_MCF ***
TPOSampleJoined$RegionLake *
TPOSampleJoined$RegionMidAtl
TPOSampleJoined$RegionNewEngland .
TPOSampleJoined$RegionPlains
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionLake *
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionMidAtl
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionNewEngland
TPOSampleJoined$LOG_TOT_MCF:TPOSampleJoined$RegionPlains
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.955 on 511 degrees of freedom
(2390 observations deleted due to missingness)
Multiple R-squared: 0.2041, Adjusted R-squared: 0.1901
F-statistic: 14.56 on 9 and 511 DF, p-value: < 0.00000000000000022Show code
Test_Model <- lm(TPOSampleJoined$Pro_Rad_SqRt ~ TPOSampleJoined$LOG_TOT_MCF + TPOSampleJoined$MILL_STATE + 0)
# summary(Test_Model)
# Test_Res <- mean(abs(residuals(Test_Model)^2))
Test_NE <- TPOSampleJoined %>%
filter(Region == "NewEngland")
NE_Model = lm(Test_NE$Pro_Rad_SqRt ~ Test_NE$LOG_TOT_MCF)
summary(NE_Model)
Call:
lm(formula = Test_NE$Pro_Rad_SqRt ~ Test_NE$LOG_TOT_MCF)
Residuals:
Min 1Q Median 3Q Max
-4.6144 -1.2219 -0.3449 1.2442 6.7138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.6683 0.5487 6.685 0.00000000297 ***
Test_NE$LOG_TOT_MCF 1.2438 0.2021 6.154 0.00000002935 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.914 on 79 degrees of freedom
(319 observations deleted due to missingness)
Multiple R-squared: 0.324, Adjusted R-squared: 0.3155
F-statistic: 37.87 on 1 and 79 DF, p-value: 0.00000002935Show code
Call:
lm(formula = Test_MidAtl$Pro_Rad_SqRt ~ Test_MidAtl$LOG_TOT_MCF)
Residuals:
Min 1Q Median 3Q Max
-4.4555 -1.0194 -0.2379 0.9882 4.8135
Coefficients:
Estimate Std. Error t value
(Intercept) 4.9679 0.4879 10.182
Test_MidAtl$LOG_TOT_MCF 0.7523 0.1858 4.049
Pr(>|t|)
(Intercept) < 0.0000000000000002 ***
Test_MidAtl$LOG_TOT_MCF 0.0000908 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.676 on 122 degrees of freedom
(499 observations deleted due to missingness)
Multiple R-squared: 0.1185, Adjusted R-squared: 0.1112
F-statistic: 16.39 on 1 and 122 DF, p-value: 0.00009079Show code
Call:
lm(formula = Test_Central$Pro_Rad_SqRt ~ Test_Central$LOG_TOT_MCF)
Residuals:
Min 1Q Median 3Q Max
-5.0971 -1.4679 -0.1721 1.4140 5.0554
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.1673 0.7316 7.063 0.000000000191
Test_Central$LOG_TOT_MCF 1.0110 0.2816 3.590 0.000506
(Intercept) ***
Test_Central$LOG_TOT_MCF ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.138 on 104 degrees of freedom
(396 observations deleted due to missingness)
Multiple R-squared: 0.1103, Adjusted R-squared: 0.1017
F-statistic: 12.89 on 1 and 104 DF, p-value: 0.000506Show code
Call:
lm(formula = Test_Lake$Pro_Rad_SqRt ~ Test_Lake$LOG_TOT_MCF)
Residuals:
Min 1Q Median 3Q Max
-4.5303 -1.1671 -0.3769 1.6801 5.0317
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.0574 0.4779 14.768 <0.0000000000000002
Test_Lake$LOG_TOT_MCF 0.3865 0.1839 2.102 0.0375
(Intercept) ***
Test_Lake$LOG_TOT_MCF *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.02 on 132 degrees of freedom
(690 observations deleted due to missingness)
Multiple R-squared: 0.03239, Adjusted R-squared: 0.02506
F-statistic: 4.418 on 1 and 132 DF, p-value: 0.03745Show code
Call:
lm(formula = Test_Plains$Pro_Rad_SqRt ~ Test_Plains$LOG_TOT_MCF)
Residuals:
Min 1Q Median 3Q Max
-3.5742 -1.4026 -0.2425 1.0928 5.9987
Coefficients:
Estimate Std. Error t value
(Intercept) 4.7566 0.5046 9.426
Test_Plains$LOG_TOT_MCF 0.7863 0.2052 3.832
Pr(>|t|)
(Intercept) 0.0000000000000262 ***
Test_Plains$LOG_TOT_MCF 0.000264 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.037 on 74 degrees of freedom
(486 observations deleted due to missingness)
Multiple R-squared: 0.1656, Adjusted R-squared: 0.1543
F-statistic: 14.68 on 1 and 74 DF, p-value: 0.0002644Show code
# Test_MI <- TPOSampleJoined %>%
# filter(Region == "MI")
#
# MI_Model = lm(Test_MI$Pro_Rad_SqRt ~ Test_MI$LOG_TOT_MCF)
# summary(MI_Model)
#
# Test_PA <- TPOSampleJoined %>%
# filter(Region == "PA")
#
# PA_Model = lm(Test_PA$Pro_Rad_SqRt ~ Test_PA$LOG_TOT_MCF)
# summary(PA_Model)
Intercepts <- c(summary(Central_Model)$coefficients[,4][1],
summary(Lake_Model)$coefficients[,4][1],
summary(MidAtl_Model)$coefficients[,4][1],
summary(NE_Model)$coefficients[,4][1],
summary(Plains_Model)$coefficients[,4][1])
Regions = c("Central", "Lake", "MidAtl", "NewEngland", "Plains")
PVals <- c(summary(Central_Model)$coefficients[,4][2],
summary(Lake_Model)$coefficients[,4][2],
summary(MidAtl_Model)$coefficients[,4][2],
summary(NE_Model)$coefficients[,4][2],
summary(Plains_Model)$coefficients[,4][2])
ModelStats <- data.frame(Region = Regions,Int = Intercepts, PVals = PVals)
# GAM <- gam(data = TPOSampleJoined, formula = Pro_Rad_SqRt ~ te(LOG_TOT_MCF, by = as.factor(Region), sp = .35, k = 3))
# summary(GAM)
# gam.check(GAM)
# plot.gam(GAM, residuals = TRUE, shade = TRUE, seWithMean = TRUE)
# GAM_Res <- residuals.gam(GAM)
# GAM_Res <- GAM_Res^2
# mean(GAM_Res)
# Central=function(x){coef(Best_Model)[2]*x+coef(Best_Model)[1]}
# Lake=function(x){coef(Best_Model)[2]*x+coef(Best_Model)[1]+coef(Best_Model)[3]}
# MI=function(x){coef(Best_Model)[2]*x+coef(Best_Model)[1]+coef(Best_Model)[4]}
# MidAtl=function(x){coef(Best_Model)[2]*x+coef(Best_Model)[1]+coef(Best_Model)[5]}
# NE=function(x){coef(Best_Model)[2]*x+coef(Best_Model)[1]+coef(Best_Model)[6]}
# PA=function(x){coef(Best_Model)[2]*x+coef(Best_Model)[1]+coef(Best_Model)[7]}
# Plains=function(x){coef(Best_Model)[2]*x+coef(Best_Model)[1]+coef(Best_Model)[8]}
#
# ggplot(TPOSampleJoined,aes(y=Pro_Rad_SqRt,x=LOG_TOT_MCF,color=Region))+
# geom_point()+
# stat_function(fun=Central,geom="line",color='yellow') +
# stat_function(fun=Lake,geom="line",color='blue') +
# stat_function(fun=MI,geom="line",color='red') +
# stat_function(fun=MidAtl,geom="line",color='black') +
# stat_function(fun=NE,geom="line",color='green') +
# stat_function(fun=PA,geom="line",color='orange') +
# stat_function(fun=Plains,geom="line",color='purple')
# TPOSampleJoined %>%
# filter(!is.na(Pro_Rad_SqRt)) %>%
# ggplot(aes(sample = Pro_Rad_SqRt)) +
# stat_qq_line() +
# stat_qq_point(size = 2) +
# theme_fivethirtyeight(base_size = 20, base_family = "serif") +
# theme(axis.title = element_text(family = 'serif', size = 20)) +
# labs(title = "Normal-QQ Plot", x= "Theoretical Quantiles",y = "Residuals")
3 Model/VIF Visualization
3.1 Preferred Model (Volume & Radius)
Show code
TPOSampleJoined <- TPOSampleJoined %>%
left_join(RegionCounts, by = "Region") %>%
left_join(ModelStats, by = "Region")
TPOSampleJoined %>%
filter(!is.na(Pro_Rad)) %>%
ggplot(aes(LOG_TOT_MCF, Pro_Rad_SqRt)) +
geom_point() +
geom_smooth(se = FALSE, method = "lm") +
scale_x_continuous(limits = c(-.5, 4.5), breaks = c(seq(-.5,4.5,.5))) +
scale_y_continuous(limits = c(0, 15), breaks = c(seq(0,14,2))) +
facet_wrap(~Region, nrow = 3) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "Linear Regression Test Plot", x = "Log(10) MCF",y = "Square Root of Procurement Radius") +
stat_regline_equation(label.x = -.5, label.y = 14, aes(label = ..rr.label..)) +
geom_text(x = -.25, y = 12, aes(label = paste0("n = ", n))) +
geom_text(x = -.2, y = 0, aes(label = paste0("P-Val = ", round(PVals, 8))))
Show code
ggplot(ModelStats, aes(Region, PVals)) +
geom_col() +
geom_text(aes(label = round(PVals, digits = 8)), size = 6 ,nudge_y = .001) +
scale_y_continuous(limits = c(0, .05), breaks = c(seq(0,.05,.01))) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "P-Values by Region", x = "Region", y = "P-Value")
Show code
VIFScores <- vif(Best_Model) %>%
as.data.frame(row.names = NULL)
VIFScores <- VIFScores %>%
dplyr::rename("VIF" = `GVIF^(1/(2*Df))`)
VIFs <- as.data.frame(VIFScores$VIF)
VIFs <- cbind(VIFs, Variable = c("Log(10) MCF", "Region", "Interaction"))
VIFs <- VIFs %>%
dplyr::rename('VIF' =`VIFScores$VIF`)
ggplot(VIFs, aes(Variable, VIF)) +
geom_col(position = "identity") +
geom_hline(yintercept = 4) +
geom_text(aes(label = round(VIF, digits = 2)), nudge_y = .12) +
scale_y_continuous(limits = c(0,5), breaks = c(seq(0,5,1))) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "Linear Regression Model - VIF Scores", y = "VIF Score", x = "Variable")
Show code
rm(VIFScores, VIFs)
Intercepts
(Intercept)
0.000000000190791855684621138398299189
(Intercept)
0.000000000000000000000000000009640239
(Intercept)
0.000000000000000005352235469197340064
(Intercept)
0.000000002969597000132149999980513133
(Intercept)
0.000000000000026187370596178335012058 3.2 Fall-Back Model (Volume & State)
Show code
TPOSampleJoined %>%
filter(!is.na(Pro_Rad)) %>%
ggplot(aes(LOG_TOT_MCF, Pro_Rad_SqRt)) +
geom_point() +
geom_smooth(se = FALSE, method = "lm") +
# scale_x_continuous(limits = c(0, 16000), breaks = c(seq(0,16000,2000))) +
scale_y_continuous(limits = c(0, 14), breaks = c(seq(0,14,2))) +
facet_wrap(~MILL_STATE, scales = "free_x", nrow = 3) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "Linear Regression Test Plot", x = "MCF",y = "Procurement Radius") +
stat_regline_equation(label.x = 1, label.y = 14, aes(label = ..rr.label..))
Show code
VIFScores <- vif(Test_Model) %>%
as.data.frame(row.names = NULL)
VIFScores <- VIFScores %>%
dplyr::rename("VIF" = `GVIF^(1/(2*Df))`)
VIFs <- as.data.frame(VIFScores$VIF)
VIFs <- cbind(VIFs, Variable = c("Log(10) MCF", "State"))
VIFs <- VIFs %>%
dplyr::rename('VIF' =`VIFScores$VIF`)
ggplot(VIFs, aes(Variable, VIF)) +
geom_col(position = "identity") +
geom_hline(yintercept = 4) +
geom_text(aes(label = round(VIF, digits = 2)), nudge_y = .12) +
scale_y_continuous(limits = c(0,5), breaks = c(seq(0,5,1))) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "Linear Regression Model - VIF Scores", y = "GVIF Value", x = "Variable")
Show code
rm(VIFScores, VIFs)
# TPOSampleJoined <- TPOSampleJoined %>%
# select(-c(BestPredictions, BestResiduals))
4 Chi-Squared Tests
Show code
TPOSampleJoined_Chi <- TPOSampleJoined %>%
filter(!is.na(Pro_Rad_SqRt) & !is.na(MILL_STATE) & !is.na(LOG_TOT_MCF))
# Chi Tests
Chi_Rad_Vol <- chisq.test(table(TPOSampleJoined_Chi$Pro_Rad_SqRt, TPOSampleJoined_Chi$LOG_TOT_MCF))
Chi_Rad_State <- chisq.test(table(TPOSampleJoined_Chi$Pro_Rad_SqRt, TPOSampleJoined_Chi$MILL_STATE))
Chi_Rad_Volt <- chisq.test(table(TPOSampleJoined_Chi$Pro_Rad_SqRt, TPOSampleJoined_Chi$volt))
Chi_Rad_Region <- chisq.test(table(TPOSampleJoined_Chi$Pro_Rad_SqRt, TPOSampleJoined_Chi$Region))
ChiStats <- c(Chi_Rad_Vol$statistic, Chi_Rad_State$statistic, Chi_Rad_Volt$statistic, Chi_Rad_Region$statistic)
ChiParams <- c(Chi_Rad_Vol$parameter, Chi_Rad_State$parameter, Chi_Rad_Volt$parameter, Chi_Rad_Region$parameter)
ChiPVals <- c(Chi_Rad_Vol$p.value, Chi_Rad_State$p.value, Chi_Rad_Volt$p.value, Chi_Rad_Region$p.value)
ChiJoined <- as.data.frame(cbind(Stats = ChiStats, Parameters = ChiParams, PValues = ChiPVals), row.names = c("Log(10) MCF", "State", "Volume Classification", "Region")) %>%
rownames_to_column(var = "Variable")
ggplot(ChiJoined, aes(PValues, Variable)) +
geom_col(position = "identity") +
geom_hline(yintercept = .05) +
geom_text(aes(label = round(PValues, digits = 10)), nudge_x = .005) +
scale_x_continuous(limits = c(0,.2), breaks = c(seq(0,.2,.02))) +
theme_fivethirtyeight(base_size = 20, base_family = "serif") +
theme(axis.title = element_text(family = 'serif', size = 20)) +
labs(title = "P-Value of Model Variables in Relation to SqRt Procurement Radius", y = "Variable", x = "P-Value") +
coord_flip()
4.1 Sqrt(Radius) & Log(10) MCF
Show code
Chi_Rad_Vol
Pearson's Chi-squared test
data: table(TPOSampleJoined_Chi$Pro_Rad_SqRt, TPOSampleJoined_Chi$LOG_TOT_MCF)
X-squared = 14796, df = 14250, p-value = 0.000693Show code
[1] "Correlation: 0.1965"4.2 Sqrt(Radius) & State
Show code
Chi_Rad_State
Pearson's Chi-squared test
data: table(TPOSampleJoined_Chi$Pro_Rad_SqRt, TPOSampleJoined_Chi$MILL_STATE)
X-squared = 296.51, df = 266, p-value = 0.096154.3 Sqrt(Radius) & Region
Show code
Chi_Rad_Region
Pearson's Chi-squared test
data: table(TPOSampleJoined_Chi$Pro_Rad_SqRt, TPOSampleJoined_Chi$Region)
X-squared = 176.64, df = 152, p-value = 0.08364.4 Sqrt(Radius) & Volume Classification (High/Low)
Show code
Chi_Rad_Volt
Pearson's Chi-squared test
data: table(TPOSampleJoined_Chi$Pro_Rad_SqRt, TPOSampleJoined_Chi$volt)
X-squared = 115.44, df = 38, p-value = 0.0000000009739Show code
rm(Chi_Rad_Region, Chi_Rad_State, Chi_Rad_Vol, Chi_Rad_Volt, ChiJoined, ChiParams, ChiPVals, ChiStats)