Data Importing

library(readxl)
corporate_governance_data <- read_excel("C:/Users/Fakudze/Desktop/Pamela/corporate_governance_data.xlsx")
View(corporate_governance_data)

Data Review

library(tidyverse)
str(corporate_governance_data)
## tibble [15 × 9] (S3: tbl_df/tbl/data.frame)
##  $ Company           : chr [1:15] "RES" "RES" "RES" "RES" ...
##  $ Year              : num [1:15] 2024 2023 2022 2021 2020 ...
##  $ Board Size        : num [1:15] 12 12 12 12 12 12 12 12 12 12 ...
##  $ Indep (%)         : num [1:15] 16.7 16.7 16.7 18.2 18.2 16.7 15.4 15.4 16.7 16.7 ...
##  $ Meetings          : num [1:15] 4 4 4 4 4 4 4 4 4 4 ...
##  $ Net Profit (E'm)  : num [1:15] 64801 178533 302622 507564 299561 ...
##  $ Total Assets (E'm): num [1:15] 5279493 4617014 4193764 3962256 3492365 ...
##  $ ROA (%)           : num [1:15] 19.2 6.8 12.3 21.5 14.7 11.6 16.7 24.1 17.7 15.5 ...
##  $ ROE (%)           : num [1:15] 20.7 6.5 11.6 20.4 13.1 10.6 15.2 20.8 15.6 14.4 ...

Data Cleaning - Renamining Variables

library(dplyr)
company_data <- corporate_governance_data %>% 
  rename(BoardSize = `Board Size`,
         Indep = `Indep (%)`,
         NetProfit = `Net Profit (E'm)`,
         TotalAssets = `Total Assets (E'm)`,
         ROA = `ROA (%)`,
         ROE = `ROE (%)`)

Descriptive Statistics

library(summarytools)
company_data %>%
         select(-Company, -Year) %>% 
         descr(order = "preserve",
               stats = c("mean", "sd", "min", "q1", "med",
                         "q3", "max"),
               round.digits = 2)
## Descriptive Statistics  
## company_data  
## N: 15  
## 
##                 BoardSize   Indep   Meetings     NetProfit   TotalAssets      ROA      ROE
## ------------- ----------- ------- ---------- ------------- ------------- -------- --------
##          Mean        9.67   24.49       4.07     -60992.20   23294043.29     9.55     8.70
##       Std.Dev        3.42   11.37       0.26    1713243.72   28900176.74    10.93    10.60
##           Min        5.00   15.40       4.00   -3988015.00    2537118.00   -11.94   -13.50
##            Q1        5.00   16.70       4.00    -646767.00    2940061.00    -1.04    -1.05
##        Median       12.00   16.70       4.00     234214.00    4193764.00    12.30    11.60
##            Q3       12.00   40.00       4.00     302622.00   62758645.00    17.70    15.60
##           Max       12.00   40.00       5.00    4190130.00   64920000.00    24.10    20.80

Creating Numeric Variables sample

numeric_variables <- company_data %>% 
  select(-Company, - Year)

Normality test for each variable - Shapiro Wilk

library(nortest)
lapply(numeric_variables, shapiro.test)
## $BoardSize
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.60343, p-value = 2.738e-05
## 
## 
## $Indep
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.65113, p-value = 7.816e-05
## 
## 
## $Meetings
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.28413, p-value = 9.834e-08
## 
## 
## $NetProfit
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.7913, p-value = 0.002849
## 
## 
## $TotalAssets
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.63753, p-value = 5.752e-05
## 
## 
## $ROA
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.92924, p-value = 0.2658
## 
## 
## $ROE
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.91167, p-value = 0.1436

BoardSize W = 0.60343, p-value = 2.738e-05, Indep W = 0.65113, p-value = 7.816e-05, Meetings W = 0.28413, p-value = 9.834e-08, NetProfit W = 0.7913, p-value = 0.002849, TotalAssets W = 0.63753, p-value = 5.752e-05, ROA W = 0.92924, p-value = 0.2658, ROE W = 0.91167, p-value = 0.1436. The results from the normality test show that from our dataset, only ROA and ROE are normaly distributed (p > 0.05) while all the other variables, BoardSize, Indep, Meetings, NetProfit and TotalAssets are not normaly distributed (p < 0.05).

Skewness and kurtosis - Checking if distribution is normal

library(psych)
psych::describe(numeric_variables)
##             vars  n        mean          sd    median     trimmed        mad
## BoardSize      1 15        9.67        3.42      12.0        9.85       0.00
## Indep          2 15       24.49       11.37      16.7       24.00       1.93
## Meetings       3 15        4.07        0.26       4.0        4.00       0.00
## NetProfit      4 15   -60992.20  1713243.72  234214.0   -85922.92  236164.84
## TotalAssets    5 15 23294043.29 28900176.74 4193764.0 21688733.18 1860333.86
## ROA            6 15        9.55       10.93      12.3       10.08       8.45
## ROE            7 15        8.70       10.60      11.6        9.47       7.56
##                     min        max       range  skew kurtosis         se
## BoardSize          5.00       12.0        7.00 -0.64    -1.69       0.88
## Indep             15.40       40.0       24.60  0.63    -1.69       2.94
## Meetings           4.00        5.0        1.00  3.13     8.39       0.07
## NetProfit   -3988015.00  4190130.0  8178145.00  0.05     1.59  442357.63
## TotalAssets  2537118.00 64920000.0 62382882.00  0.64    -1.68 7461993.55
## ROA              -11.94       24.1       36.04 -0.55    -1.08       2.82
## ROE              -13.50       20.8       34.30 -0.65    -0.89       2.74

Board Size, skew = -0.64, kurtosis = -1.69, moderate left skew Indep, skew = 0.63, kurtosis = -1.69, moderate right skew Meetings, skew = 3.13, kurtosis = 8.39 highly right skewed Net Profit, skew = 0.05, kurtosis = 1.59, approximately symmetric Total Assets, skew = 0.64, kurtosis = -1.68, moderate right skewed ROA, skew = -0.55, kurtosis = -1.08, moderate left skew ROE, skew = -0.65, kurtosis = -0.89, moderate left skew

Outlier check using the IQR rule

sapply(numeric_variables, function(x){
  Q1 <- quantile(x, 0.25)
  Q3 <- quantile(x, 0.75)
  IQR <- Q3 - Q1
  sum(x < (Q1 - 1.5 * IQR) | x > (Q3 + 1.5 * IQR))
})
##   BoardSize       Indep    Meetings   NetProfit TotalAssets         ROA 
##           0           0           1           3           0           0 
##         ROE 
##           0

Meetings and the Net Profit variables has outliers, 1 and 3 consecutively.

checking multicollinearity with correlation matrix

cor(numeric_variables, use = "complete.obs") %>% 
  round(2)
##             BoardSize Indep Meetings NetProfit TotalAssets   ROA   ROE
## BoardSize        1.00 -1.00    -0.38      0.29       -1.00  0.87  0.86
## Indep           -1.00  1.00     0.38     -0.29        1.00 -0.87 -0.85
## Meetings        -0.38  0.38     1.00      0.69        0.39 -0.07 -0.05
## NetProfit        0.29 -0.29     0.69      1.00       -0.27  0.50  0.50
## TotalAssets     -1.00  1.00     0.39     -0.27        1.00 -0.87 -0.85
## ROA              0.87 -0.87    -0.07      0.50       -0.87  1.00  0.99
## ROE              0.86 -0.85    -0.05      0.50       -0.85  0.99  1.00

Board Size vs Indep has a correlation of -1.00, they have a perfect negative collinearity. Board Size vs Total Assets also has a correlation of -1.00, they have a perfect negative collinearity. Indep vs Total Assets has a correlation of 1.00, they have a perfect positive collinearity. ROA vs ROE has a correlation of 0.99, they have a near perfect collinearity.

Effect of Board Independence on financial perfomance - Correaltion and Regression Analysis

library(car)
library(broom)

cor.test(company_data$Indep, company_data$ROA, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  company_data$Indep and company_data$ROA
## S = 981, p-value = 0.001228
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.7517871
cor.test(company_data$Indep, company_data$ROE, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  company_data$Indep and company_data$ROE
## S = 973.61, p-value = 0.001661
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.7385979
cor.test(company_data$Indep, company_data$NetProfit, method = "kendal")
## 
##  Kendall's rank correlation tau
## 
## data:  company_data$Indep and company_data$NetProfit
## z = -1.5782, p-value = 0.1145
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.3314968

From the correlation tests Indep vs ROA: rho = -0.75, p = 0.001, strong negative significant Indep vs ROE: rho = -0.74, p = 0.002, styrong negative significant Indep vs NetProfit: tau = -0.33, p = 0.11, week negative, not significant This means that the Board Independence has a strong negative relationship with financial perfomance ratios but not with Profit. As Board Independence go up, ROA and ROE go down.

m1_ROA <- lm(ROA ~ Indep, data = company_data)
m1_ROE <- lm(ROE ~ Indep, data = company_data)
m1_NetProfit <- lm(NetProfit ~ Indep, data = company_data)

summary(m1_ROA)
## 
## Call:
## lm(formula = ROA ~ Indep, data = company_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.2373 -3.5577 -0.3881  2.7423  9.9619 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  29.9414     3.5672   8.393 1.32e-06 ***
## Indep        -0.8326     0.1329  -6.266 2.90e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.655 on 13 degrees of freedom
## Multiple R-squared:  0.7512, Adjusted R-squared:  0.7321 
## F-statistic: 39.26 on 1 and 13 DF,  p-value: 2.899e-05
summary(m1_ROE)
## 
## Call:
## lm(formula = ROE ~ Indep, data = company_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.8403 -3.2280 -0.5057  3.7341 10.3397 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  28.2122     3.6032   7.830 2.83e-06 ***
## Indep        -0.7968     0.1342  -5.936 4.93e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.712 on 13 degrees of freedom
## Multiple R-squared:  0.7305, Adjusted R-squared:  0.7098 
## F-statistic: 35.24 on 1 and 13 DF,  p-value: 4.932e-05
summary(m1_NetProfit)
## 
## Call:
## lm(formula = NetProfit ~ Indep, data = company_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3255051  -120615   -31840    73320  4923094 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  1000412    1074054   0.931    0.369
## Indep         -43334      40009  -1.083    0.298
## 
## Residual standard error: 1703000 on 13 degrees of freedom
## Multiple R-squared:  0.08277,    Adjusted R-squared:  0.01222 
## F-statistic: 1.173 on 1 and 13 DF,  p-value: 0.2984

From the regression models, the coeficient of indep = -0.83 from m1_ROA model means that for every 1% increase in Board Independence ROA drops by 0.83%. The coeficient of indep = -0.7968 from m1_ROE model means that for every 1% increase in Board Independence ROE drops by 0.80%. The coeficient of indep = -43334 from m1_NetProfit model means that for every 1% increase in Board Independence ROA drops by 43334.

Effect of number of meetings per year on financial performance

cor.test(company_data$Meetings, company_data$ROA, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  company_data$Meetings and company_data$ROA
## S = 663.92, p-value = 0.5079
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.1855769
cor.test(company_data$Meetings, company_data$ROE, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  company_data$Meetings and company_data$ROE
## S = 629.28, p-value = 0.6605
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.1237179
cor.test(company_data$Meetings, company_data$NetProfit, method = "kendal")
## 
##  Kendall's rank correlation tau
## 
## data:  company_data$Meetings and company_data$NetProfit
## z = 1.6202, p-value = 0.1052
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3651484

From the correlation tests Meetings vs ROA: rho = -0.18, p = 0.001, weak negative significant Meetings vs ROE: rho = -0.12, p = 0.002, weak negative significant Meetings vs NetProfit: tau = 0.37, p = 0.11, weak positive, not significant This means that the Meetings has a weak negative relationship with financial perfomance ratios but not with Profit. As Meetings go up, ROA and ROE go down.

m2_ROA <- lm(ROA ~ Meetings, data = company_data)
m2_ROE <- lm(ROE ~ Meetings, data = company_data)
m2_NetProfit <- lm(NetProfit ~ Meetings, data = company_data)

summary(m2_ROA)
## 
## Call:
## lm(formula = ROA ~ Meetings, data = company_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.699  -6.879   2.541   7.441  14.341 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   22.396     47.685    0.47    0.646
## Meetings      -3.159     11.704   -0.27    0.791
## 
## Residual standard error: 11.31 on 13 degrees of freedom
## Multiple R-squared:  0.005574,   Adjusted R-squared:  -0.07092 
## F-statistic: 0.07286 on 1 and 13 DF,  p-value: 0.7914
summary(m2_ROE)
## 
## Call:
## lm(formula = ROE ~ Meetings, data = company_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.340  -6.115   2.760   6.560  11.960 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)    17.48      46.34   0.377    0.712
## Meetings       -2.16      11.38  -0.190    0.852
## 
## Residual standard error: 10.99 on 13 degrees of freedom
## Multiple R-squared:  0.002766,   Adjusted R-squared:  -0.07394 
## F-statistic: 0.03606 on 1 and 13 DF,  p-value: 0.8523
summary(m2_NetProfit)
## 
## Call:
## lm(formula = NetProfit ~ Meetings, data = company_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3623371  -141062   582447   665035   872208 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -18583739    5452472  -3.408  0.00467 **
## Meetings      4554774    1338257   3.404  0.00471 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1293000 on 13 degrees of freedom
## Multiple R-squared:  0.4712, Adjusted R-squared:  0.4305 
## F-statistic: 11.58 on 1 and 13 DF,  p-value: 0.00471

From the regression models, the coefficient of Meetings = -3.159 from m2_ROA model means that for every extra meeting, ROA drops by 3.16%. The coefficient of Meetings = -2.16 from m2_ROE model means for every extra meeting, ROE drops by 2.16%. The coefficient of Meetings = 4554774 from m2_NetProfit model means that for every extra Meeting, Net Profit increase by by 43334.

The effect of Board size on the financial perfomamce

cor.test(company_data$BoardSize, company_data$ROA, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  company_data$BoardSize and company_data$ROA
## S = 101.74, p-value = 0.0001922
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.8183171
cor.test(company_data$BoardSize, company_data$ROE, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  company_data$BoardSize and company_data$ROE
## S = 120.07, p-value = 0.0005183
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.7855844
cor.test(company_data$BoardSize, company_data$NetProfit, method = "kendal")
## 
##  Kendall's rank correlation tau
## 
## data:  company_data$BoardSize and company_data$NetProfit
## z = 1.8371, p-value = 0.06619
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.4140393

From the correlation tests Board Size vs ROA: rho = 0.82, p = 0.0002, strong positive significant Board Size vs ROE: rho = 0.76, p = 0.0005, strong positive significant Board Size vs Net Profit: tau = 0.41, p = 0.066, moderate positive, not significant This means that the Board Size has a strong positive relationship with financial performance ratios. As Board Size go up, ROA,ROE and Net Profit also go up.

mo3_ROA <- lm(ROA ~ BoardSize, data = company_data)
mo3_ROE <- lm(ROE ~ BoardSize, data = company_data)
mo3_NetProfit <- lm(NetProfit ~ BoardSize, data = company_data)

summary(mo3_ROA)
## 
## Call:
## lm(formula = ROA ~ BoardSize, data = company_data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.210 -3.538 -0.376  2.762  9.974 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -17.2197     4.5368  -3.796  0.00223 ** 
## BoardSize     2.7691     0.4442   6.235 3.05e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.676 on 13 degrees of freedom
## Multiple R-squared:  0.7494, Adjusted R-squared:  0.7301 
## F-statistic: 38.87 on 1 and 13 DF,  p-value: 3.046e-05
summary(mo3_ROE)
## 
## Call:
## lm(formula = ROE ~ BoardSize, data = company_data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -9.808 -3.204 -0.088  4.076 10.372 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -16.9649     4.5601  -3.720  0.00257 ** 
## BoardSize     2.6546     0.4464   5.946 4.85e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.706 on 13 degrees of freedom
## Multiple R-squared:  0.7312, Adjusted R-squared:  0.7105 
## F-statistic: 35.36 on 1 and 13 DF,  p-value: 4.855e-05
summary(mo3_NetProfit)
## 
## Call:
## lm(formula = NetProfit ~ BoardSize, data = company_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3250430  -118593    -4086    58068  4927715 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1462506    1360316  -1.075    0.302
## BoardSize     144984     133177   1.089    0.296
## 
## Residual standard error: 1702000 on 13 degrees of freedom
## Multiple R-squared:  0.08355,    Adjusted R-squared:  0.01305 
## F-statistic: 1.185 on 1 and 13 DF,  p-value: 0.2961

From the regression models, the coefficient of Board Size = 2.7691 from mo3_ROA model means that for every additional Board member in Board Size, ROA increase by 2.77%. The coefficient of Board Size = 2.65 from mo3_ROE model means for every additional Board member, ROE drops by 2.65%. The coefficient of 144984 in mo3_NetProfit means that each extra Board member adds about 144984 thousand Net Profit.

The effect of company size on the relationship between corporate governance pand the financial perfomance

cor.test(company_data$TotalAssets, company_data$ROA, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  company_data$TotalAssets and company_data$ROA
## S = 968, p-value = 0.002927
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.7285714
cor.test(company_data$TotalAssets, company_data$ROE, method = "spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  company_data$TotalAssets and company_data$ROE
## S = 952, p-value = 0.004876
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##  rho 
## -0.7
cor.test(company_data$TotalAssets, company_data$NetProfit, method = "kendal")
## 
##  Kendall's rank correlation tau
## 
## data:  company_data$TotalAssets and company_data$NetProfit
## T = 37, p-value = 0.1395
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2952381

From the correlation tests Total Assets vs ROA: rho = -0.73, p = 0.003, strong negative significant Total Assets vs ROE: rho = -0.7, p = 0.005, strong negative significant Total Assets vs Net Profit: tau = -0.30, p = 0.1395, moderate negative, not significant This means that the Total Assets has a strong negative relationship with financial performance ratios. For an extra additional Asset, ROA,ROE and Net Profit also go up

mo4_ROA <- lm(ROA ~ TotalAssets, data = company_data)
mo4_ROE <- lm(ROE ~ TotalAssets, data = company_data)
mo4_NetProfit <- lm(NetProfit ~ TotalAssets, data = company_data)

summary(mo4_ROA)
## 
## Call:
## lm(formula = ROA ~ TotalAssets, data = company_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.8629 -4.0256 -0.2654  3.0424 10.2973 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.717e+01  1.907e+00   9.005 5.98e-07 ***
## TotalAssets -3.274e-07  5.245e-08  -6.241 3.01e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.672 on 13 degrees of freedom
## Multiple R-squared:  0.7498, Adjusted R-squared:  0.7305 
## F-statistic: 38.95 on 1 and 13 DF,  p-value: 3.013e-05
summary(mo4_ROE)
## 
## Call:
## lm(formula = ROE ~ TotalAssets, data = company_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.1498 -3.6781  0.0017  4.1340 10.6654 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.600e+01  1.924e+00   8.315 1.46e-06 ***
## TotalAssets -3.134e-07  5.291e-08  -5.923 5.04e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.722 on 13 degrees of freedom
## Multiple R-squared:  0.7297, Adjusted R-squared:  0.7089 
## F-statistic: 35.09 on 1 and 13 DF,  p-value: 5.039e-05
summary(mo4_NetProfit)
## 
## Call:
## lm(formula = NetProfit ~ TotalAssets, data = company_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3361014  -106518     8110    54882  4890167 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  3.069e+05  5.762e+05   0.533    0.603
## TotalAssets -1.579e-02  1.585e-02  -0.997    0.337
## 
## Residual standard error: 1714000 on 13 degrees of freedom
## Multiple R-squared:  0.07098,    Adjusted R-squared:  -0.0004846 
## F-statistic: 0.9932 on 1 and 13 DF,  p-value: 0.3371

From the regression models, the coefficient of Total Assets = -3.274e07, p = 0.00003 from mo4_ROA model means that for every 1000000 increase in Total Assets, ROA drops by 0.327%. The coefficient of Total Assets = -3.134e-07 from mo4_ROE model means that for every 1000000 increase in Total Assets, ROE drops by 0.313%. The coefficient of -0.0158 and the p value of 0.337 indicate not significant relationship between Total Assets and Net Profit. Mo4_NetProfit estimates that Net Profit drops by 0.016 for every increase in Assets.