library(readxl)
## Warning: package 'readxl' was built under R version 4.3.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.3.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(car)
## Warning: package 'car' was built under R version 4.3.3
## Loading required package: carData
## Warning: package 'carData' was built under R version 4.3.3
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
library(lmtest)
## Warning: package 'lmtest' was built under R version 4.3.3
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(sandwich)
## Warning: package 'sandwich' was built under R version 4.3.3
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
library(readxl)
thy_data <- read_excel("D:/MBA/MRP/data/thy_data.xlsx")
View(thy_data)
data <- read_excel("D:/MBA/MRP/data/thy_data.xlsx")
colnames(data) <- make.names(colnames(data))
data <- read_excel("D:/MBA/MRP/data/thy_data.xlsx")
colnames(data) <- make.names(colnames(data))
str(data)
## tibble [7 × 16] (S3: tbl_df/tbl/data.frame)
## $ Year : num [1:7] 2018 2019 2020 2021 2022 ...
## $ Total_Revenue : num [1:7] 12.86 13.23 6.73 10.69 18.43 ...
## $ Net_Profit : num [1:7] 753 788 -836 959 2725 ...
## $ Total_Passengers : num [1:7] 75.2 74.3 28 44.8 71.8 83.4 85.2
## $ Load_Factor : num [1:7] 82 81.6 71 67.9 80.6 82.6 82.2
## $ Cargo_Revenue : num [1:7] 1667 1688 2722 4015 3735 ...
## $ Current_Ratio : num [1:7] 0.87 0.8 0.65 0.73 0.88 0.94 1.01
## $ Debt_to_Equity_Ratio: num [1:7] 2.07 1.95 3.1 1.95 1.76 0.75 0.62
## $ Operating_Cash_Flow : num [1:7] 9613 13029 5603 49362 117012 ...
## $ Total_Assets : num [1:7] 109076 146871 187402 353708 578571 ...
## $ Leased_Fleet_Percent: num [1:7] 85.8 87.4 82.4 80.5 77.7 73 70.5
## $ Government_Support : num [1:7] 414 488 1032 416 966 ...
## $ GDP_Growth : num [1:7] 20.8 15.6 16.8 44.6 106.2 ...
## $ Inflation : num [1:7] 20.3 11.8 14.6 36.1 64.3 ...
## $ USD_TRY : num [1:7] 5.26 5.94 7.34 12.98 18.7 ...
## $ Stock_Return : num [1:7] 1.91 -10.57 -9.65 55.88 599.6 ...
summary(data)
## Year Total_Revenue Net_Profit Total_Passengers
## Min. :2018 Min. : 6.73 Min. :-836.0 Min. :28.0
## 1st Qu.:2020 1st Qu.:11.78 1st Qu.: 770.5 1st Qu.:58.3
## Median :2021 Median :13.23 Median : 959.0 Median :74.3
## Mean :2021 Mean :15.08 Mean :1976.4 Mean :66.1
## 3rd Qu.:2022 3rd Qu.:19.68 3rd Qu.:3075.0 3rd Qu.:79.3
## Max. :2024 Max. :22.67 Max. :6021.0 Max. :85.2
## Load_Factor Cargo_Revenue Current_Ratio Debt_to_Equity_Ratio
## Min. :67.90 Min. :1667 Min. :0.650 Min. :0.620
## 1st Qu.:75.80 1st Qu.:2142 1st Qu.:0.765 1st Qu.:1.255
## Median :81.60 Median :2722 Median :0.870 Median :1.950
## Mean :78.27 Mean :2845 Mean :0.840 Mean :1.743
## 3rd Qu.:82.10 3rd Qu.:3615 3rd Qu.:0.910 3rd Qu.:2.010
## Max. :82.60 Max. :4015 Max. :1.010 Max. :3.100
## Operating_Cash_Flow Total_Assets Leased_Fleet_Percent Government_Support
## Min. : 5603 Min. : 109076 Min. :70.50 Min. : 414
## 1st Qu.: 11321 1st Qu.: 167137 1st Qu.:75.35 1st Qu.: 452
## Median : 49362 Median : 353708 Median :80.50 Median : 966
## Mean : 67890 Mean : 546311 Mean :79.61 Mean :1927
## 3rd Qu.:126296 3rd Qu.: 813757 3rd Qu.:84.10 3rd Qu.:1327
## Max. :145032 Max. :1399606 Max. :87.40 Max. :8551
## GDP_Growth Inflation USD_TRY Stock_Return
## Min. : 15.60 Min. :11.84 Min. : 5.26 Min. :-10.57
## 1st Qu.: 18.80 1st Qu.:17.45 1st Qu.: 6.64 1st Qu.: -3.87
## Median : 44.60 Median :36.08 Median :12.98 Median : 23.47
## Mean : 49.34 Mean :36.61 Mean :16.41 Mean :103.27
## 3rd Qu.: 70.70 3rd Qu.:54.33 3rd Qu.:24.05 3rd Qu.: 59.06
## Max. :106.20 Max. :64.77 Max. :35.22 Max. :599.60
rev_2019 <- data$Total_Revenue[data$Year == 2019]
data$Revenue_Recovery <- data$Total_Revenue / rev_2019
pax_2019 <- data$Total_Passengers[data$Year == 2019]
data$Passenger_Recovery <- data$Total_Passengers / pax_2019
data <- data %>%
mutate(
z_revenue = as.numeric(scale(Revenue_Recovery)),
z_passenger = as.numeric(scale(Passenger_Recovery)),
z_profit = as.numeric(scale(Net_Profit)),
z_cashflow = as.numeric(scale(Operating_Cash_Flow))
)
data$SRI <- rowMeans(
cbind(
data$z_revenue,
data$z_passenger,
data$z_profit,
data$z_cashflow
),
na.rm = TRUE
)
summary(data$SRI)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.3642 -0.4907 -0.3340 0.0000 0.7354 1.2089
model_H1 <- lm(
SRI ~ Current_Ratio + GDP_Growth + Inflation + USD_TRY,
data = data
)
summary(model_H1)
##
## Call:
## lm(formula = SRI ~ Current_Ratio + GDP_Growth + Inflation + USD_TRY,
## data = data)
##
## Residuals:
## 1 2 3 4 5 6 7
## -0.211927 0.311734 -0.060252 -0.081475 -0.001843 0.127844 -0.084080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.753794 1.117493 -4.254 0.0511 .
## Current_Ratio 4.850891 1.536302 3.158 0.0874 .
## GDP_Growth -0.006698 0.013354 -0.502 0.6657
## Inflation 0.023834 0.022778 1.046 0.4052
## USD_TRY 0.008356 0.019791 0.422 0.7139
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2965 on 2 degrees of freedom
## Multiple R-squared: 0.9659, Adjusted R-squared: 0.8977
## F-statistic: 14.16 on 4 and 2 DF, p-value: 0.06705
model_H2 <- lm(
SRI ~ Leased_Fleet_Percent + GDP_Growth + Inflation + USD_TRY,
data = data
)
summary(model_H2)
##
## Call:
## lm(formula = SRI ~ Leased_Fleet_Percent + GDP_Growth + Inflation +
## USD_TRY, data = data)
##
## Residuals:
## 1 2 3 4 5 6 7
## 0.24162 -0.10993 0.03294 -0.25340 0.03481 0.02093 0.03303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -28.146450 7.392580 -3.807 0.0626 .
## Leased_Fleet_Percent 0.303293 0.083506 3.632 0.0681 .
## GDP_Growth -0.004424 0.011818 -0.374 0.7441
## Inflation 0.030929 0.020423 1.514 0.2691
## USD_TRY 0.188112 0.041707 4.510 0.0458 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2632 on 2 degrees of freedom
## Multiple R-squared: 0.9731, Adjusted R-squared: 0.9194
## F-statistic: 18.1 on 4 and 2 DF, p-value: 0.05303
model_H3 <- lm(
SRI ~ Government_Support + GDP_Growth + Inflation + USD_TRY,
data = data
)
summary(model_H3)
##
## Call:
## lm(formula = SRI ~ Government_Support + GDP_Growth + Inflation +
## USD_TRY, data = data)
##
## Residuals:
## 1 2 3 4 5 6 7
## 0.37403 0.52515 -0.62244 -0.47342 0.02433 0.15428 0.01806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.312e+00 6.940e-01 -1.890 0.199
## Government_Support 5.317e-06 5.071e-04 0.010 0.993
## GDP_Growth -3.682e-03 5.699e-02 -0.065 0.954
## Inflation 2.096e-02 1.409e-01 0.149 0.895
## USD_TRY 4.363e-02 1.892e-01 0.231 0.839
##
## Residual standard error: 0.7252 on 2 degrees of freedom
## Multiple R-squared: 0.7959, Adjusted R-squared: 0.3876
## F-statistic: 1.949 on 4 and 2 DF, p-value: 0.3666
full_model <- lm(
SRI ~ Current_Ratio + Leased_Fleet_Percent + Government_Support +
Debt_to_Equity_Ratio + GDP_Growth + Inflation + USD_TRY,
data = data
)
summary(full_model)
##
## Call:
## lm(formula = SRI ~ Current_Ratio + Leased_Fleet_Percent + Government_Support +
## Debt_to_Equity_Ratio + GDP_Growth + Inflation + USD_TRY,
## data = data)
##
## Residuals:
## ALL 7 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.6105840 NaN NaN NaN
## Current_Ratio 8.5612880 NaN NaN NaN
## Leased_Fleet_Percent -0.4079986 NaN NaN NaN
## Government_Support -0.0007125 NaN NaN NaN
## Debt_to_Equity_Ratio -0.8385289 NaN NaN NaN
## GDP_Growth 0.0679735 NaN NaN NaN
## Inflation -0.1868351 NaN NaN NaN
## USD_TRY NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 6 and 0 DF, p-value: NA
alias(full_model)
## Model :
## SRI ~ Current_Ratio + Leased_Fleet_Percent + Government_Support +
## Debt_to_Equity_Ratio + GDP_Growth + Inflation + USD_TRY
##
## Complete :
## (Intercept) Current_Ratio Leased_Fleet_Percent
## USD_TRY 46765677/146263 3448214474/136459775 -447647/125644
## Government_Support Debt_to_Equity_Ratio GDP_Growth
## USD_TRY -3445/989007 -569499402/81241069 884408/2206125
## Inflation
## USD_TRY -31022/27195
full_model_final <- lm(
SRI ~ Current_Ratio + Leased_Fleet_Percent + Government_Support +
Debt_to_Equity_Ratio + GDP_Growth + Inflation,
data = data
)
summary(full_model_final)
##
## Call:
## lm(formula = SRI ~ Current_Ratio + Leased_Fleet_Percent + Government_Support +
## Debt_to_Equity_Ratio + GDP_Growth + Inflation, data = data)
##
## Residuals:
## ALL 7 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.6105840 NaN NaN NaN
## Current_Ratio 8.5612880 NaN NaN NaN
## Leased_Fleet_Percent -0.4079986 NaN NaN NaN
## Government_Support -0.0007125 NaN NaN NaN
## Debt_to_Equity_Ratio -0.8385289 NaN NaN NaN
## GDP_Growth 0.0679735 NaN NaN NaN
## Inflation -0.1868351 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 6 and 0 DF, p-value: NA
vif(full_model_final)
## Warning in cov2cor(v): diag(.) had 0 or NA entries; non-finite result is
## doubtful
## Current_Ratio Leased_Fleet_Percent Government_Support
## NaN NaN NaN
## Debt_to_Equity_Ratio GDP_Growth Inflation
## NaN NaN NaN
bptest(full_model_final)
##
## studentized Breusch-Pagan test
##
## data: full_model_final
## BP = NaN, df = 6, p-value = NA
coeftest(full_model_final, vcov = vcovHC(full_model_final, type = "HC3"))
## Warning in meatHC(x, type = type, omega = omega): HC3 covariances are
## numerically unstable for hat values close to 1 (and undefined if exactly 1) as
## for observation(s) 1, 2, 3, 4, 5, 6, 7
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 31.61058396 NaN NaN NaN
## Current_Ratio 8.56128798 NaN NaN NaN
## Leased_Fleet_Percent -0.40799857 NaN NaN NaN
## Government_Support -0.00071245 NaN NaN NaN
## Debt_to_Equity_Ratio -0.83852893 NaN NaN NaN
## GDP_Growth 0.06797355 NaN NaN NaN
## Inflation -0.18683507 NaN NaN NaN
qqnorm(residuals(full_model_final))
qqline(residuals(full_model_final))
plot(full_model_final, which = 1)
summary(full_model_final)
##
## Call:
## lm(formula = SRI ~ Current_Ratio + Leased_Fleet_Percent + Government_Support +
## Debt_to_Equity_Ratio + GDP_Growth + Inflation, data = data)
##
## Residuals:
## ALL 7 residuals are 0: no residual degrees of freedom!
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.6105840 NaN NaN NaN
## Current_Ratio 8.5612880 NaN NaN NaN
## Leased_Fleet_Percent -0.4079986 NaN NaN NaN
## Government_Support -0.0007125 NaN NaN NaN
## Debt_to_Equity_Ratio -0.8385289 NaN NaN NaN
## GDP_Growth 0.0679735 NaN NaN NaN
## Inflation -0.1868351 NaN NaN NaN
##
## Residual standard error: NaN on 0 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: NaN
## F-statistic: NaN on 6 and 0 DF, p-value: NA
final_model <- lm(
SRI ~ Current_Ratio + Leased_Fleet_Percent + Government_Support,
data = data
)
summary(final_model)
##
## Call:
## lm(formula = SRI ~ Current_Ratio + Leased_Fleet_Percent + Government_Support,
## data = data)
##
## Residuals:
## 1 2 3 4 5 6 7
## -0.282914 0.304671 -0.059900 -0.028548 -0.002824 0.079314 -0.009798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.078e-01 2.523e+00 0.162 0.8818
## Current_Ratio 6.360e+00 1.144e+00 5.560 0.0115 *
## Leased_Fleet_Percent -6.958e-02 2.490e-02 -2.795 0.0682 .
## Government_Support -1.094e-04 5.253e-05 -2.082 0.1287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2474 on 3 degrees of freedom
## Multiple R-squared: 0.9644, Adjusted R-squared: 0.9287
## F-statistic: 27.06 on 3 and 3 DF, p-value: 0.0113
vif(final_model)
## Current_Ratio Leased_Fleet_Percent Government_Support
## 1.949191 2.408910 2.359442
bptest(final_model)
##
## studentized Breusch-Pagan test
##
## data: final_model
## BP = 6.5518, df = 3, p-value = 0.08764
coeftest(final_model, vcov = vcovHC(final_model, type = "HC1"))
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.40783498 3.47194552 0.1175 0.913914
## Current_Ratio 6.35986526 1.06911483 5.9487 0.009499 **
## Leased_Fleet_Percent -0.06957689 0.03470000 -2.0051 0.138640
## Government_Support -0.00010940 0.00002291 -4.7750 0.017455 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dwtest(final_model)
##
## Durbin-Watson test
##
## data: final_model
## DW = 2.6924, p-value = 0.3593
## alternative hypothesis: true autocorrelation is greater than 0
qqnorm(residuals(final_model))
qqline(residuals(final_model))
plot(final_model)
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
