Fm Wcm Analysis
Transcript of Fm Wcm Analysis
BHUSHAN STEEL
Interpretations of the regressed output
Current ratio:Current ratio is defined as the total liquidity of the firm. The ideal current ratio is 2 :1 which means that ideally the current assets should be more , should be twice than that of current liabilities as the ratio states that current ratio = current assets /current liabilities higher the liquidity of the firm lower is the risk ,lower is the profit.
Regression Statistics
Multiple R 0.473085236
R Square 0.223809641
Adjusted R Square 0.029762051
Standard Error 1.633745573
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 3.07850161 3.078501605 1.153375008 0.343313
Residual 4 10.6764984 2.669124599
Total 5 13.755
CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0%
Upper 95.0%
Intercept 1.753263741 3.14134405 0.558125347 0.60652112 -6.968506 10.4750331 -6.968505583 10.475033
Current ratio (times) 2.872138457 2.67436145 1.073952982 0.343312664 -4.553079 10.2973562 -4.553079306 10.297356
Interpretations of the regressed output :
R square is roughly 22.2% which shows that the regressed model is not relevant because that means that 22.2% of the variation in ROCE IS explained by current ratio
Standard error basically shows that how much is the data dispersed which is around 1.63.
The x variable is in positive which clearly states that there is a direct relationship between current ratio and ROCE because an increase in the value of the current ratio will lead to a increase in the value of ROCE as the equation here is
Y=1.75 + 2.87X
X here stand for current ratio
The p value is around 0.34 which shows that the value of the slope is significant.
Correlation is coming out to be 0.47 which shows the direct relationship between the ROCE and the current ratio and further confirms the positive slope.
Raw material inventory holding period :Raw material inventory holding period is the time taken to convert the raw materials into work in progress; it is not advisable for the firm to show a higher raw material inventory holding period as it increases the time for the complete manufacture
Our analysis shows :
Regression Statistics
Multiple R 0.8333264
R Square 0.6944329
Adjusted R Square 0.6180412
Standard Error 1.0250701
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 9.55192508 9.55192508 9.0904162 0.03935502
Residual 4 4.20307492 1.05076873
Total 5 13.755
Coefficients Standard Error t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 8.42934 1.196406906 7.045546068 0.0021395 5.10758187 11.751098 5.10758187 11.751098
RIHP -0.030227 0.010025319 -3.01503171 0.039355 -0.0580614-0.0023919 -0.0580614
-0.0023919
Interpretations of the regressed output of raw material inventory holding period
As per the theory we know that greater is the raw material holding period , lesser is the profit or the ROCE
The regressed output also derives the same relationship between the two variables:
R square is approximately 70 percent which clearly states that the regressed model is a very good model because it clearly states that 70 percent of the changes in ROCE can be explained by the raw material inventory holding period.
Standard error shows that how much is the data scattered which is around 1.025
The x variable is in negative which clearly states that there is an inverse relationship between raw material inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=8.42 – 0.03 X
X here stand for raw material inventory holding period
The p value is around 0.039 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.83 which shows the inverse relationship between the ROCE and the raw material inventory holding period
FINISHED GOODS HOLDING PERIODFinished goods holding period is the period for which the finished goods are kept and not sold .the firm always tries to minimise the finished goods holding period
Regression Statistics
Multiple R 0.28699
R Square 0.0823633
Adjusted R Square -0.1470459
Standard Error 1.7763793
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 1.132907 1.132907 0.3590235 0.5813337
Residual 4 12.622093 3.1555233
Total 5 13.755
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 7.5217108 4.1883776 1.7958531 0.1469459 -4.1070897 19.150511-4.1070897 19.150511
FIHP -0.0711625 0.1187654-0.5991857 0.5813337 -0.400908 0.258583 -0.400908 0.258583
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 8% which clearly states that the regressed model is a not very good model because it clearly states that 92% of the changes in ROCE cannot be explained by the finished goods inventory holding period.
Standard error shows that how much is the data scattered which is around 1.77 which is essential in plotting the graph because the variation is very less.
The x variable is in negative which clearly states that there is an inverse relationship between finished inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=7.52—0.07X
X here stand for coefficient of finished goods inventory holding period
The p value is around 0.58 which shows that the value of the slope is not very significant. The lesser the p value the more significant is the slope but here p-value is more than 0.5 which shows irrelevancy.
Correlation is coming out to be negative –0.28 which shows the inverse relationship between the ROCE and finished goods inventory holding period
Average collection period :As per the theory we know that a firma always tries to minimise its average collection period because the lesser is the average collection period , more quickly is the cash generated in the business .
Our analysis shows that :
Regression Statistics
Multiple R 0.1647277
R Square 0.0271352
Adjusted R Square -0.216081
Standard Error 1.8290541
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 0.3732448 0.3732448 0.1115683 0.7551435
Residual 4 13.381755 3.3454388
Total 5 13.755
Coefficients
Standard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 6.1772802 3.4565238 1.7871366 0.1484448 -3.4195684 15.774129-3.4195684 15.774129
ACP -0.0276069 0.0826507-0.3340183 0.7551435 -0.2570821 0.2018684
-0.2570821 0.2018684
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 2.7 percent which clearly states that the regressed model is a not good model because it clearly states that 97.3 percent of the changes in ROCE cannot be explained by the average collection period.
Standard error shows that how much is the data scattered which is around 1.82 which means that around 1.82 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between average collection period and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE as the equation here is
Y=6.177 – 0.027X
X here stand for coefficient of average collection period
The p value is around 0.755 which shows that the value of the slope is not significant. The lesser the p value the more significant is the slope.
Correlation is coming out to be negative –0.164 which shows the inverse relationship between the ROCE and finished goods inventory holding period
Average payment period :The average payment period refers to the time which is taken by a company in order to pay off it’s creditor’s in a working capital cycle, the average payment period should always be prolonged , however, care should be taken that it does not effect the business at large .
Our analysis:
Regression Statistics
Multiple R 0.8798517
R Square 0.774139
Adjusted R Square 0.7176738
Standard Error 0.8812941
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 10.648283 10.648283 13.710011 0.0207862
Residual 4 3.1067174 0.7766794
Total 5 13.755
Coefficients
Standard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 14.302035 2.5244941 5.6653074 0.0047867 7.2929157 21.311154 7.2929157 21.311154
APP -0.086589 0.0233853-3.7027032 0.0207862 -0.1515171
-0.0216609
-0.1515171
-0.0216609
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 77 percent which clearly states that the regressed model is a good model which clearly states that 77 percent of the changes in ROCE can be explained by the average payment period
Standard error shows that how much is the data scattered which is around 6.41which means that around 6.41 percent of the data is scattered.
The x variable is in negative which clearly states that there is an indirect relationship between average payment period and ROCE because an increase in the value of the average payment period will lead to an decrease in the value of ROCE as the equation here is
Y=14.3 – 0.086X
X here stand for coefficient of average payment period
The p value is around 0.02 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative 0.879 which shows the indirect relationship between the ROCE and APP.
Net working capital:The net working capital refers to CURRENT ASSETS – CURRENT LIABILITIES , these are the short term assets and the short term liabilities of the company , higher the liquidity in the company lower is the profit or ROCE
Our analysis shows that :
Regression Statistics
Multiple R0.839847058
R Square0.705343081
Adjusted R Square
0.631678851
Standard Error1.006603934
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 9.7019940799.70199408
9.5751097
0.036419569
Residual 4 4.0530059211.01325148
Total 5 13.755
Coefficients Standard t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Error
Intercept6.947085749 0.738064195
9.41257657
0.00071011
4.897891026
8.996280471 4.897891
8.996280471
Net working capital
-9.4214E-05 3.04471E-05 -3.094367
0.03641957
-0.00017874
-9.6798E-06 -0.000179
-9.6798E-06
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 70.5 percent which clearly states that the regressed model is a good model because it clearly states that 70.5 percent of the changes in ROCE can be explained by the change in Net Working Capital
Standard error shows that how much is the data scattered which is around 1 which means that around 1 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between Net Working Capital and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE but the slope is approximately ~0, which means that the impact is not very large as the equation here is
Y=6.94 – (9.4 x 10-5X)
X here stand for coefficient of Net Working Capital
The p value is around 0.03 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.839 which shows the inverse relationship between the ROCE and Net Working Capital
ESSAR STEEL INDIA
Current ratio:Current ratio is defined as the total liquidity of the firm. The ideal current ratio is 2 :1 which means that ideally the current assets should be more , should be twice than that of current liabilities as the ratio states that current ratio = current assets /current liabilities higher the liquidity of the firm lower is the risk ,lower is the profit.
Regression Statistics
Multiple R 0.2032105
R Square 0.0412945
Adjusted R Square -0.1983819
Standard Error 4.4546163
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 3.4189085 3.4189085 0.1722927 0.69938
Residual 4 79.374425 19.843606
Total 5 82.793333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 3.4926128 11.133588 0.3137006 0.7694267 -27.419182 34.404408 -27.419182 34.404408
Current ratio (times) -7.3477509 17.701945 -0.4150816 0.69938 -56.496229 41.800728 -56.496229 41.800728
Interpretations of the regressed output :
R square is roughly 9 percent which shows that the regressed model is not relevant because that means that 9 percent of the variation in ROCE IS explained by current ratio
Standard error basically shows that how much is the data dispersed which is around 6.225
The x variable is in negative which clearly states that there is an inverse relationship between current ratio and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=44.21845--17.95660X
X here stand for current ratio
The p value is around 0.5 which shows that the value of the slope is significant.
Correlation is coming out to be negative –0.03 which shows the inverse relationship between the ROCE and the current ratio
This can also be studied by the scattered diagram above here we study the trend line between ROCE and current ratio the trend line is moving downwards which clearly states that with the increase in the current ratio the ROCE decreases.
Raw material inventory holding period :
Raw material inventory holding period is the time taken to convert the raw materials into work in progress; it is not advisable for the firm to show a higher raw material inventory holding period as it increases the time for the complete manufacture
Our analysis shows :
Regression Statistics
Multiple R 0.9490087
R Square 0.9006175
Adjusted R Square 0.8757719
Standard Error 1.4342428
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 74.565124 74.565124 36.248528 0.0038339
Residual 4 8.2282097 2.0570524
Total 5 82.793333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept -20.821824 3.3330534 -6.247072 0.0033471 -30.075864 -11.567785 -30.075864 -11.567785
RIHP 0.2493289 0.0414121 6.0206751 0.0038339 0.1343504 0.3643073 0.1343504 0.3643073
Interpretations of the regressed output of raw material inventory holding period
As per the theory we know that greater is the raw material holding period , lesser is the profit or the ROCE
The regressed output also derives the same relationship between the two variables:
R square is roughly 75 percent which clearly states that the regressed model is a very good model because it clearly states that 75 percent of the changes in ROCE can be explained by the raw material inventory holding period.
Standard error shows that how much is the data scattered which is around 4.306
The x variable is in negative which clearly states that there is an inverse relationship between raw material inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=55.120—0.519X
X here stand for raw material inventory holding period
The p value is around 0.085 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.75 which shows the inverse relationship between the ROCE and the raw material inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and raw material inventory holding period, the trend line is moving downwards which clearly states that with the increase in the the raw material inventory holding period the ROCE decreases.
FINISHED GOODS HOLDING PERIOD
Finished goods holding period is the period for which the finished goods are kept and not sold .the firm always tries to minimise the finished goods holding period
Regression Statistics
Multiple R 0.0126609
R Square 0.0001603
Adjusted R Square -0.2497996
Standard Error 4.5491774
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 0.0132716 0.0132716 0.0006413 0.9810097
Residual 4 82.780062 20.695015
Total 5 82.793333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept -1.3170378 10.059712 -0.130922 0.9021576 -29.247275 26.6132 -29.247275 26.6132
FIHP 0.0089472 0.35331 0.0253238 0.9810097 -0.9719986 0.9898929 -0.9719986 0.9898929
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 85 percent which clearly states that the regressed model is a very good model because it clearly states that 85 percent of the changes in ROCE can be explained by the finished goods inventory holding period.
Standard error shows that how much is the data scattered which is around 2.517 which is essential in plotting the graph because the variation is very less.
The x variable is in negative which clearly states that there is an inverse relationship between finished inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=72.46267—1.60424X
X here stand for coefficient of finished goods inventory holding period
The p value is around 0.008 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.992 which shows the inverse relationship between the ROCE and finished goods inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and finished goods inventory holding period, the trend line is moving downwards which clearly states that with the increase in the the finished goods inventory holding period the ROCE decreases.
Average collection period :
As per the theory we know that a firm always tries to minimise its average collection period because the lesser is the average collection period , more quickly is the cash generated in the business .
Our analysis shows that :
Regression Statistics
Multiple R 0.1198987
R Square 0.0143757
Adjusted R Square -0.2320304
Standard Error 4.5167223
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 1.1902113 1.1902113 0.0583415 0.8210138
Residual 4 81.603122 20.400781
Total 5 82.793333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 3.4650287 18.85209 0.1838008 0.8631111 -48.876765 55.806823 -48.876765 55.806823
ACP -0.3420147 1.4159769 -0.2415398 0.8210138 -4.273397 3.5893675 -4.273397 3.5893675
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 41 percent which clearly states that the regressed model is a good model because it clearly states that 41 percent of the changes in ROCE can be explained by the average collection period
Standard error shows that how much is the data scattered which is around 5 which means that around 5 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between average collection period and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE as the equation here is
Y=63.260—1.959X
X here stand for coefficient of average collection period
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.64271 which shows the inverse relationship between the ROCE and finished goods inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and AVERAGE COLLECTION PERIOD, the trend line is moving downwards which clearly states that with the increase in the average collection period the ROCE decreases.
Average payment period :
The average payment period refers to the time which is taken by a company in order to pay off it’s creditor’s in a working capital cycle, the average payment period should always be prolonged , however, care should be taken that it does not effect the business at large .
Our analysis:
Regression Statistics
Multiple R 0.7477088
R Square 0.5590684
Adjusted R Square 0.4488355
Standard Error 3.0210178
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 46.287139 46.287139 5.0717025 0.087447
Residual 4 36.506194 9.1265485
Total 5 82.793333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 16.269365 7.7960827 2.0868641 0.105203 -5.3760303 37.914761 -5.3760303 37.914761
APP -0.1321848 0.0586955 -2.2520441 0.087447 -0.2951495 0.03078 -0.2951495 0.03078
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 3 percent which clearly states that the regressed model is a model be which clearly states that 3 percent of the changes in ROCE can be explained by the average payment period
Standard error shows that how much is the data scattered which is around 6.41which means that around 6.5 percent of the data is scattered.
The x variable is in positive which clearly states that there is an direct relationship between average payment period and ROCE because an increase in the value of the average payment period will lead to an increase in the value of ROCE as the equation here is
Y=13.56+0.283X
X here stand for coefficient of average payment period
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be positive 0.188 which shows the direct relationship between the ROCE and APP
This can also be studied by the scattered diagram above here we study the trend line between ROCE and AVERAGE PAYMENT PERIOD, the trend line is moving UPWARDS which clearly states that with the increase in the average payment period the ROCE increases
Net working capital:
The net working capital refers to CURRENT ASSETS – CURRENT LIABILITIES , these are the short term assets and the short term liabilities of the company , higher the liquidity in the company lower is the profit or ROCE
Our analysis shows that :
Regression Statistics
Multiple R 0.8311292
R Square 0.6907758
Adjusted R Square 0.6134697
Standard Error 2.5299064
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 57.191628 57.191628 8.9355965 0.0403681
Residual 4 25.601706 6.4004264
Total 5 82.793333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept -2.4868823 1.1368665 -2.1874884 0.0939552 -5.6433299 0.6695652 -5.6433299 0.6695652
Net working capital 0.0002459 8.225E-05 2.9892468 0.0403681 1.75E-05 0.0004742 1.75E-05 0.0004742
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 41 percent which clearly states that the regressed model is a good model because it clearly states that 41 percent of the changes in ROCE can be explained by the change in Net Working Capital
Standard error shows that how much is the data scattered which is around 5 which means that around 5 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between Net Working Capital and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE as the equation here is
Y=63.260—1.959X
X here stand for coefficient of Net Working Capital
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.64271 which shows the inverse relationship between the ROCE and Net Working Capital This can also be studied by the scattered
diagram above here we study the trend line between ROCE and Net Working Capital, the trend line is moving downwards which clearly states that with the increase in the Net Working Capital the ROCE decreases.
JSW STEEL INDIA
Interpretations of the regressed output
Current ratio:
Current ratio is defined as the total liquidity of the firm. The ideal current ratio is 2 :1 which means that ideally the current assets should be more , should be twice than that of current liabilities as the ratio states that current ratio = current assets /current liabilities higher the liquidity of the firm lower is the risk ,lower is the profit.
Regression Statistics
Multiple R 0.0285401
R Square 0.0008145
Adjusted R Square
-0.2489818
Standard Error
4.539488
Observations
6
ANOVA
df SS MS FSignificance F
Regression 1 0.0671951 0.0671951 0.0032608 0.9572015
Residual 4 82.427805 20.606951
Total 5 82.495
CoefficientsStandard Error
t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 7.8496847 7.2405066 1.0841347 0.3392809 -12.253184 27.952554-12.253184
27.952554
Current ratio (times)
-0.7065729 12.373572-0.0571034
0.9572015 -35.061117 33.647971-35.061117
33.647971
Interpretations of the regressed output :
R square is roughly 9 percent which shows that the regressed model is not relevant because that means that 9 percent of the variation in ROCE IS explained by current ratio
Standard error basically shows that how much is the data dispersed which is around 6.225
The x variable is in negative which clearly states that there is an inverse relationship between current ratio and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=44.21845--17.95660X
X here stand for current ratio
The p value is around 0.5 which shows that the value of the slope is significant.
Correlation is coming out to be negative –0.03 which shows the inverse relationship between the ROCE and the current ratio
This can also be studied by the scattered diagram above here we study the trend line between ROCE and current ratio the trend line is moving downwards which clearly states that with the increase in the current ratio the ROCE decreases.
Raw material inventory holding period :
Raw material inventory holding period is the time taken to convert the raw materials into work in progress; it is not advisable for the firm to show a higher raw material inventory holding period as it increases the time for the complete manufacture
Our analysis shows :
Regression Statistics
Multiple R 0.8022975
R Square 0.6436812
Adjusted R Square
0.5546016
Standard Error
2.7108355
Observations
6
ANOVA
df SS MS FSignificance F
Regression 1 53.100484 53.100484 7.2259036 0.0547657
Residual 4 29.394516 7.348629
Total 5 82.495
CoefficientsStandard Error
t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept -39.890443 17.645829 -2.260616 0.0866214 -88.883118 9.1022311-88.883118
9.1022311
RIHP 1.0328824 0.384242 2.6881041 0.0547657 -0.0339443 2.0997091-0.0339443
2.0997091
Interpretations of the regressed output of raw material inventory holding period
As per the theory we know that greater is the raw material holding period , lesser is the profit or the ROCE
The regressed output also derives the same relationship between the two variables:
R square is roughly 75 percent which clearly states that the regressed model is a very good model because it clearly states that 75 percent of the changes in ROCE can be explained by the raw material inventory holding period.
Standard error shows that how much is the data scattered which is around 4.306
The x variable is in negative which clearly states that there is an inverse relationship between raw material inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=55.120—0.519X
X here stand for raw material inventory holding period
The p value is around 0.085 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.75 which shows the inverse relationship between the ROCE and the raw material inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and raw material inventory holding period, the trend line is moving downwards which clearly states that with the increase in the the raw material inventory holding period the ROCE decreases.
FINISHED GOODS HOLDING PERIOD
Finished goods holding period is the period for which the finished goods are kept and not sold .the firm always tries to minimise the finished goods holding period
Regression Statistics
Multiple R 0.1335984
R Square 0.0178485
Adjusted R Square
-0.2276893
Standard Error
4.5006274
Observations
6
ANOVA
df SS MS FSignificance F
Regression 1 1.4724137 1.4724137 0.0726915 0.8007947
Residual 4 81.022586 20.255647
Total 5 82.495
CoefficientsStandard Error
t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 8.8955898 5.6677915 1.5694984 0.1916157 -6.8407221 24.631902-6.8407221
24.631902
FIHP -0.0869964 0.3226705-0.2696136
0.8007947 -0.9828734 0.8088806-0.9828734
0.8088806
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 85 percent which clearly states that the regressed model is a very good model because it clearly states that 85 percent of the changes in ROCE can be explained by the finished goods inventory holding period.
Standard error shows that how much is the data scattered which is around 2.517 which is essential in plotting the graph because the variation is very less.
The x variable is in negative which clearly states that there is an inverse relationship between finished inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=72.46267—1.60424X
X here stand for coefficient of finished goods inventory holding period
The p value is around 0.008 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.992 which shows the inverse relationship between the ROCE and finished goods inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and finished goods inventory holding period, the trend line is moving downwards which clearly states that with the increase in the the finished goods inventory holding period the ROCE decreases.
Average collection period :
As per the theory we know that a firm always tries to minimise its average collection period because the lesser is the average collection period , more quickly is the cash generated in the business .
Our analysis shows that :
Regression Statistics
Multiple R 0.4451523
R Square 0.1981606
Adjusted R Square
-0.0022993
Standard Error
4.0665631
Observations
6
ANOVA
df SS MS FSignificance F
Regression 1 16.347258 16.347258 0.9885301 0.3763773
Residual 4 66.147742 16.536935
Total 5 82.495
CoefficientsStandard Error
t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 16.78565 9.5352903 1.7603711 0.1531505 -9.6885604 43.25986-9.6885604
43.25986
ACP -0.8461314 0.8510261-0.9942485
0.3763773 -3.2089585 1.5166957-3.2089585
1.5166957
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 41 percent which clearly states that the regressed model is a good model because it clearly states that 41 percent of the changes in ROCE can be explained by the average collection period
Standard error shows that how much is the data scattered which is around 5 which means that around 5 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between average collection period and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE as the equation here is
Y=63.260—1.959X
X here stand for coefficient of average collection period
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.64271 which shows the inverse relationship between the ROCE and finished goods inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and AVERAGE COLLECTION PERIOD, the trend line is moving downwards which clearly states that with the increase in the average collection period the ROCE decreases.
Average payment period :
The average payment period refers to the time which is taken by a company in order to pay off it’s creditor’s in a working capital cycle, the average payment period should always be prolonged , however, care should be taken that it does not effect the business at large .
Our analysis:
Regression Statistics
Multiple R 0.4447957
R Square 0.1978432
Adjusted R Square
-0.0026959
Standard Error
4.0673678
Observations
6
ANOVA
df SS MS FSignificance F
Regression 1 16.321078 16.321078 0.9865565 0.3768063
Residual 4 66.173922 16.54348
Total 5 82.495
CoefficientsStandard Error
t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 18.93834 11.684934 1.6207486 0.1803879 -13.504238 51.380918-13.504238
51.380918
APP -0.0795316 0.0800716-0.9932555
0.3768063 -0.3018461 0.1427829-0.3018461
0.1427829
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 3 percent which clearly states that the regressed model is a model be which clearly states that 3 percent of the changes in ROCE can be explained by the average payment period
Standard error shows that how much is the data scattered which is around 6.41which means that around 6.5 percent of the data is scattered.
The x variable is in positive which clearly states that there is an direct relationship between average payment period and ROCE because an increase in the value of the average payment period will lead to an increase in the value of ROCE as the equation here is
Y=13.56+0.283X
X here stand for coefficient of average payment period
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be positive 0.188 which shows the direct relationship between the ROCE and APP
This can also be studied by the scattered diagram above here we study the trend line between ROCE and AVERAGE PAYMENT PERIOD, the trend line is moving UPWARDS which clearly states that with the increase in the average payment period the ROCE increases
Net working capital:
The net working capital refers to CURRENT ASSETS – CURRENT LIABILITIES , these are the short term assets and the short term liabilities of the company , higher the liquidity in the company lower is the profit or ROCE
Our analysis shows that :
Regression Statistics
Multiple R 0.6129668
R Square 0.3757283
Adjusted R Square
0.2196604
Standard Error
3.5881504
Observations
6
ANOVA
df SS MS FSignificance F
Regression 1 30.995707 30.995707 2.4074666 0.1957043
Residual 4 51.499293 12.874823
Total 5 82.495
CoefficientsStandard Error
t Stat P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 13.0833 3.9150127 3.3418283 0.0287868 2.2134822 23.953118 2.2134822 23.953118
Net working capital
0.0001557 0.0001004 1.5516013 0.1957043 -0.0001229 0.0004344-0.0001229
0.0004344
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 41 percent which clearly states that the regressed model is a good model because it clearly states that 41 percent of the changes in ROCE can be explained by the change in Net Working Capital
Standard error shows that how much is the data scattered which is around 5 which means that around 5 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between Net Working Capital and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE as the equation here is
Y=63.260—1.959X
X here stand for coefficient of Net Working Capital
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.64271 which shows the inverse relationship between the ROCE and Net Working Capital This can also be studied by the scattered diagram above here we study the trend line between ROCE and Net Working Capital, the trend line is moving downwards which clearly states that with the increase in the Net Working Capital the ROCE decreases.
SAIL: STEEL AUTHORITY OF INDIA
Interpretations of the regressed output
Current ratio:
Current ratio is defined as the total liquidity of the firm. The ideal current ratio is 2 :1 which means that ideally the current assets should be more , should be twice than that of current liabilities as the ratio states that current ratio = current assets /current liabilities higher the liquidity of the firm lower is the risk ,lower is the profit.
Regression Statistics
Multiple R 0.7982821
R Square 0.6372543
Adjusted R Square 0.5465679
Standard Error 7.0185652
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 346.1523 346.1523 7.0270096 0.0569312
Residual 4 197.04103 49.260258
Total 5 543.19333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept -54.709995 26.252856-2.0839635 0.1055486 -127.59961 18.179619
-127.59961 18.179619
Current ratio (times) 42.697251 16.106999 2.6508507 0.0569312 -2.0229484 87.41745
-2.0229484 87.41745
Interpretations of the regressed output :
R square is roughly 9 percent which shows that the regressed model is not relevant because that means that 9 percent of the variation in ROCE IS explained by current ratio
Standard error basically shows that how much is the data dispersed which is around 6.225
The x variable is in negative which clearly states that there is an inverse relationship between current ratio and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=44.21845--17.95660X
X here stand for current ratio
The p value is around 0.5 which shows that the value of the slope is significant.
Correlation is coming out to be negative –0.03 which shows the inverse relationship between the ROCE and the current ratio
This can also be studied by the scattered diagram above here we study the trend line between ROCE and current ratio the trend line is moving downwards which clearly states that with the increase in the current ratio the ROCE decreases.
Raw material inventory holding period :
Raw material inventory holding period is the time taken to convert the raw materials into work in progress; it is not advisable for the firm to show a higher raw material inventory holding period as it increases the time for the complete manufacture
Our analysis shows :
Regression Statistics
Multiple R 0.4123749
R Square 0.1700531
Adjusted R Square -0.0374336
Standard Error 10.61628
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 92.371709 92.371709 0.8195854 0.4165004
Residual 4 450.82162 112.70541
Total 5 543.19333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 48.511375 37.854528 1.2815211 0.2692551 -56.589643 153.61239-56.589643 153.61239
RIHP -0.4124965 0.4556413-0.9053096 0.4165004 -1.6775596 0.8525667
-1.6775596 0.8525667
Interpretations of the regressed output of raw material inventory holding period
As per the theory we know that greater is the raw material holding period , lesser is the profit or the ROCE
The regressed output also derives the same relationship between the two variables:
R square is roughly 75 percent which clearly states that the regressed model is a very good model because it clearly states that 75 percent of the changes in ROCE can be explained by the raw material inventory holding period.
Standard error shows that how much is the data scattered which is around 4.306
The x variable is in negative which clearly states that there is an inverse relationship between raw material inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=55.120—0.519X
X here stand for raw material inventory holding period
The p value is around 0.085 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.75 which shows the inverse relationship between the ROCE and the raw material inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and raw material inventory holding period, the trend line is moving downwards which clearly states that with the increase in the the raw material inventory holding period the ROCE decreases.
FINISHED GOODS HOLDING PERIOD
Finished goods holding period is the period for which the finished goods are kept and not sold .the firm always tries to minimise the finished goods holding period
Regression Statistics
Multiple R 0.5410124
R Square 0.2926944
Adjusted R Square 0.115868
Standard Error 9.8005572
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 158.98965 158.98965 1.6552641 0.2676571
Residual 4 384.20369 96.050922
Total 5 543.19333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 61.106059 36.471069 1.6754666 0.1691523 -40.153862 162.36598-40.153862 162.36598
FIHP -0.7698387 0.5983648-1.2865707 0.2676571 -2.4311658 0.8914885
-2.4311658 0.8914885
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 85 percent which clearly states that the regressed model is a very good model because it clearly states that 85 percent of the changes in ROCE can be explained by the finished goods inventory holding period.
Standard error shows that how much is the data scattered which is around 2.517 which is essential in plotting the graph because the variation is very less.
The x variable is in negative which clearly states that there is an inverse relationship between finished inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=72.46267—1.60424X
X here stand for coefficient of finished goods inventory holding period
The p value is around 0.008 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.992 which shows the inverse relationship between the ROCE and finished goods inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and finished goods inventory holding period, the trend line is moving downwards which clearly states that with the increase in the the finished goods inventory holding period the ROCE decreases.
Average collection period :
As per the theory we know that a firm always tries to minimise its average collection period because the lesser is the average collection period , more quickly is the cash generated in the business .
Our analysis shows that :
Regression Statistics
Multiple R 0.9461511
R Square 0.8952018
Adjusted R Square 0.8690023
Standard Error 3.7724552
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 486.26766 486.26766 34.168602 0.0042715
Residual 4 56.925672 14.231418
Total 5 543.19333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 80.357963 11.377072 7.0631498 0.0021195 48.770147 111.94578 48.770147 111.94578
ACP -2.1651028 0.3703948-5.8453915 0.0042715 -3.1934838
-1.1367219
-3.1934838
-1.1367219
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 41 percent which clearly states that the regressed model is a good model because it clearly states that 41 percent of the changes in ROCE can be explained by the average collection period
Standard error shows that how much is the data scattered which is around 5 which means that around 5 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between average collection period and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE as the equation here is
Y=63.260—1.959X
X here stand for coefficient of average collection period
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.64271 which shows the inverse relationship between the ROCE and finished goods inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and AVERAGE COLLECTION PERIOD, the trend line is moving downwards which clearly states that with the increase in the average collection period the ROCE decreases.
Average payment period :
The average payment period refers to the time which is taken by a company in order to pay off it’s creditor’s in a working capital cycle, the average payment period should always be prolonged , however, care should be taken that it does not effect the business at large .
Our analysis:
Regression Statistics
Multiple R 0.8018099
R Square 0.6428991
Adjusted R Square
0.5536239
Standard Error 6.9637419
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 349.21853 349.21853 7.2013171 0.0550266
Residual 4 193.97481 48.493702
Total 5 543.19333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 53.202675 14.712034 3.6162692 0.0224313 12.355521 94.049829 12.355521 94.049829
APP -0.4832939 0.1800965 -2.683527 0.0550266 -0.9833221 0.0167342-0.9833221 0.0167342
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 3 percent which clearly states that the regressed model is a model be which clearly states that 3 percent of the changes in ROCE can be explained by the average payment period
Standard error shows that how much is the data scattered which is around 6.41which means that around 6.5 percent of the data is scattered.
The x variable is in positive which clearly states that there is an direct relationship between average payment period and ROCE because an increase in the value of the average payment period will lead to an increase in the value of ROCE as the equation here is
Y=13.56+0.283X
X here stand for coefficient of average payment period
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be positive 0.188 which shows the direct relationship between the ROCE and APP
This can also be studied by the scattered diagram above here we study the trend line between ROCE and AVERAGE PAYMENT PERIOD, the trend line is moving UPWARDS which clearly states that with the increase in the average payment period the ROCE increases
Net working capital:
The net working capital refers to CURRENT ASSETS – CURRENT LIABILITIES , these are the short term assets and the short term liabilities of the company , higher the liquidity in the company lower is the profit or ROCE
Our analysis shows that :
Regression Statistics
Multiple R 0.5393867
R Square 0.290938
Adjusted R Square 0.1136725
Standard Error 9.8127181
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 158.03559 158.03559 1.6412557 0.269384
Residual 4 385.15774 96.289436
Total 5 543.19333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 36.56418 17.70775 2.0648688 0.1078555 -12.600415 85.728776-12.600415 85.728776
Net working capital -0.0001914 0.0001494 -1.281115 0.269384 -0.0006061 0.0002233
-0.0006061 0.0002233
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 41 percent which clearly states that the regressed model is a good model because it clearly states that 41 percent of the changes in ROCE can be explained by the change in Net Working Capital
Standard error shows that how much is the data scattered which is around 5 which means that around 5 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between Net Working Capital and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE as the equation here is
Y=63.260—1.959X
X here stand for coefficient of Net Working Capital
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.64271 which shows the inverse relationship between the ROCE and Net Working Capital This can also be studied by the scattered diagram above here we study the trend line between ROCE and Net Working Capital, the trend line is moving downwards which clearly states that with the increase in the Net Working Capital the ROCE decreases.
TATA STEEL INDIA
Interpretations of the regressed output
Current ratio:
Current ratio is defined as the total liquidity of the firm. The ideal current ratio is 2 :1 which means that ideally the current assets should be more , should be twice than that of current liabilities as the ratio states that current ratio = current assets /current liabilities higher the liquidity of the firm lower is the risk ,lower is the profit.
Regression Statistics
Multiple R 0.5546638
R Square 0.307652
Adjusted R Square 0.134565
Standard Error 2.3168956
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 9.5413133 9.5413133 1.7774412 0.253326
Residual 4 21.47202 5.368005
Total 5 31.013333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 18.622102 6.9320608 2.6863731 0.0548642 -0.6243844 37.868588-0.6243844 37.868588
Current ratio (times) -14.672172 11.005171
-1.3332071 0.253326 -45.227424 15.88308
-45.227424 15.88308
Interpretations of the regressed output :
R square is roughly 9 percent which shows that the regressed model is not relevant because that means that 9 percent of the variation in ROCE IS explained by current ratio
Standard error basically shows that how much is the data dispersed which is around 6.225
The x variable is in negative which clearly states that there is an inverse relationship between current ratio and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=44.21845--17.95660X
X here stand for current ratio
The p value is around 0.5 which shows that the value of the slope is significant.
Correlation is coming out to be negative –0.03 which shows the inverse relationship between the ROCE and the current ratio
This can also be studied by the scattered diagram above here we study the trend line between ROCE and current ratio the trend line is moving downwards which clearly states that with the increase in the current ratio the ROCE decreases.
Raw material inventory holding period :
Raw material inventory holding period is the time taken to convert the raw materials into work in progress; it is not advisable for the firm to show a higher raw material inventory holding period as it increases the time for the complete manufacture
Our analysis shows :
Regression Statistics
Multiple R 0.2090685
R Square 0.0437096
Adjusted R Square -0.195363
Standard Error 2.7229466
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 1.3555816 1.3555816 0.18283 0.6909664
Residual 4 29.657752 7.4144379
Total 5 31.013333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 1.7010758 18.19545 0.0934891 0.9300106 -48.817593 52.219745-48.817593 52.219745
RIHP 0.0783875 0.1833257 0.4275862 0.6909664 -0.4306061 0.5873811-0.4306061 0.5873811
Interpretations of the regressed output of raw material inventory holding period
As per the theory we know that greater is the raw material holding period , lesser is the profit or the ROCE
The regressed output also derives the same relationship between the two variables:
R square is roughly 75 percent which clearly states that the regressed model is a very good model because it clearly states that 75 percent of the changes in ROCE can be explained by the raw material inventory holding period.
Standard error shows that how much is the data scattered which is around 4.306
The x variable is in negative which clearly states that there is an inverse relationship between raw material inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=55.120—0.519X
X here stand for raw material inventory holding period
The p value is around 0.085 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.75 which shows the inverse relationship between the ROCE and the raw material inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and raw material inventory holding period, the trend line is moving downwards which clearly states that with the increase in the the raw material inventory holding period the ROCE decreases.
FINISHED GOODS HOLDING PERIOD
Finished goods holding period is the period for which the finished goods are kept and not sold .the firm always tries to minimise the finished goods holding period
Regression Statistics
Multiple R 0.8726141
R Square 0.7614554
Adjusted R Square 0.7018192
Standard Error 1.3599691
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 23.615269 23.615269 12.768351 0.0233072
Residual 4 7.3980641 1.849516
Total 5 31.013333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept -7.5259621 4.7877676-1.5719147 0.1910704 -20.818936 5.7670119
-20.818936 5.7670119
FIHP 0.4983176 0.1394565 3.5732829 0.0233072 0.1111242 0.8855109 0.1111242 0.8855109
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 85 percent which clearly states that the regressed model is a very good model because it clearly states that 85 percent of the changes in ROCE can be explained by the finished goods inventory holding period.
Standard error shows that how much is the data scattered which is around 2.517 which is essential in plotting the graph because the variation is very less.
The x variable is in negative which clearly states that there is an inverse relationship between finished inventory holding period and ROCE because an increase in the value of the current ratio will lead to a decline in the value of ROCE as the equation here is
Y=72.46267—1.60424X
X here stand for coefficient of finished goods inventory holding period
The p value is around 0.008 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.992 which shows the inverse relationship between the ROCE and finished goods inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and finished goods inventory holding period, the trend line is moving downwards which clearly states that with the increase in the the finished goods inventory holding period the ROCE decreases.
Average collection period :
As per the theory we know that a firm always tries to minimise its average collection period because the lesser is the average collection period , more quickly is the cash generated in the business .
Our analysis shows that :
Regression Statistics
Multiple R 0.587305
R Square 0.3449271
Adjusted R Square 0.1811589
Standard Error 2.2536633
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 10.69734 10.69734 2.1061907 0.2203313
Residual 4 20.315994 5.0789985
Total 5 31.013333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 3.2759552 4.3638073 0.7507103 0.4945761 -8.8399161 15.391827-8.8399161 15.391827
ACP 0.7869548 0.5422518 1.4512721 0.2203313 -0.7185774 2.2924871-0.7185774 2.2924871
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 41 percent which clearly states that the regressed model is a good model because it clearly states that 41 percent of the changes in ROCE can be explained by the average collection period
Standard error shows that how much is the data scattered which is around 5 which means that around 5 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between average collection period and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE as the equation here is
Y=63.260—1.959X
X here stand for coefficient of average collection period
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.64271 which shows the inverse relationship between the ROCE and finished goods inventory holding period
This can also be studied by the scattered diagram above here we study the trend line between ROCE and AVERAGE COLLECTION PERIOD, the trend line is moving downwards which clearly states that with the increase in the average collection period the ROCE decreases.
Average payment period :
The average payment period refers to the time which is taken by a company in order to pay off it’s creditor’s in a working capital cycle, the average payment period should always be prolonged , however, care should be taken that it does not effect the business at large .
Our analysis:
Regression Statistics
Multiple R 0.8157587
R Square 0.6654623
Adjusted R Square 0.5818279
Standard Error 1.6105223
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 20.638205 20.638205 7.9568002 0.0477902
Residual 4 10.375128 2.593782
Total 5 31.013333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 25.53229 5.7332797 4.4533481 0.0112177 9.6141541 41.450427 9.6141541 41.450427
APP -0.1502162 0.0532534-2.8207801 0.0477902 -0.2980714 -0.002361
-0.2980714 -0.002361
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 3 percent which clearly states that the regressed model is a model be which clearly states that 3 percent of the changes in ROCE can be explained by the average payment period
Standard error shows that how much is the data scattered which is around 6.41which means that around 6.5 percent of the data is scattered.
The x variable is in positive which clearly states that there is an direct relationship between average payment period and ROCE because an increase in the value of the average payment period will lead to an increase in the value of ROCE as the equation here is
Y=13.56+0.283X
X here stand for coefficient of average payment period
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be positive 0.188 which shows the direct relationship between the ROCE and APP
This can also be studied by the scattered diagram above here we study the trend line between ROCE and AVERAGE PAYMENT PERIOD, the trend line is moving UPWARDS which clearly states that with the increase in the average payment period the ROCE increases
Net working capital:
The net working capital refers to CURRENT ASSETS – CURRENT LIABILITIES , these are the short term assets and the short term liabilities of the company , higher the liquidity in the company lower is the profit or ROCE
Our analysis shows that :
Regression Statistics
Multiple R 0.8046884
R Square 0.6475234
Adjusted R Square 0.5594042
Standard Error 1.6531391
Observations 6
ANOVA
df SS MS FSignificance F
Regression 1 20.081858 20.081858 7.3482697 0.0534947
Residual 4 10.931476 2.7328689
Total 5 31.013333
CoefficientsStandard Error t Stat P-value Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept 2.1566698 2.7798204 0.7758306 0.4811652 -5.561349 9.8746886 -5.561349 9.8746886
Net working capital 0.0003635 0.0001341 2.7107692 0.0534947 -8.808E-06 0.0007359
-8.808E-06 0.0007359
INTERPRETATIONS OF THE REGRESSED OUTPUT:
R square is 41 percent which clearly states that the regressed model is a good model because it clearly states that 41 percent of the changes in ROCE can be explained by the change in Net Working Capital
Standard error shows that how much is the data scattered which is around 5 which means that around 5 percent of the data is scattered.
The x variable is in negative which clearly states that there is an inverse relationship between Net Working Capital and ROCE because an increase in the value of the average collection period will lead to a decline in the value of ROCE as the equation here is
Y=63.260—1.959X
X here stand for coefficient of Net Working Capital
The p value is around 0.004 which shows that the value of the slope is significant. The lesser the p value the more significant is the slope
Correlation is coming out to be negative –0.64271 which shows the inverse relationship between the ROCE and Net Working Capital This can also be studied by the scattered diagram above here we study the trend line between ROCE and Net Working Capital, the trend line is moving downwards which clearly states that with the increase in the Net Working Capital the ROCE decreases.