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 :



R Square 0.6944329



Observations 6

ANOVA


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


Multiple R 0.28699

R Square 0.0823633

Adjusted R Square -0.1470459


Observations 6

ANOVA


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 :



R Square 0.0271352



Observations 6

ANOVA


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


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.


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:



R Square 0.774139



Observations 6

ANOVA


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


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


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



Multiple R0.839847058

R Square0.705343081

Adjusted R Square

0.631678851

Standard Error1.006603934

Observations 6

ANOVA


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


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


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.



R Square 0.0412945



Observations 6

ANOVA


Regression 1 3.4189085 3.4189085 0.1722927 0.69938

Residual 4 79.374425 19.843606

Total 5 82.793333


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


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



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




R Square 0.9006175



Observations 6

ANOVA


Regression 1 74.565124 74.565124 36.248528 0.0038339

Residual 4 8.2282097 2.0570524

Total 5 82.793333


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




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.



Y=55.120—0.519X




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



R Square 0.0001603



Observations 6

ANOVA


Regression 1 0.0132716 0.0132716 0.0006413 0.9810097

Residual 4 82.780062 20.695015

Total 5 82.793333


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


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.



Y=72.46267—1.60424X




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 .




R Square 0.0143757



Observations 6

ANOVA


Regression 1 1.1902113 1.1902113 0.0583415 0.8210138

Residual 4 81.603122 20.400781

Total 5 82.793333


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


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



Y=63.260—1.959X




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:



R Square 0.5590684



Observations 6

ANOVA


Regression 1 46.287139 46.287139 5.0717025 0.087447

Residual 4 36.506194 9.1265485

Total 5 82.793333


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


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


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



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




R Square 0.6907758



Observations 6

ANOVA


Regression 1 57.191628 57.191628 8.9355965 0.0403681

Residual 4 25.601706 6.4004264

Total 5 82.793333


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


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


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



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


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.



R Square 0.0008145

Adjusted R Square

-0.2489818

Standard Error

4.539488

Observations

6

ANOVA


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





Y=44.21845--17.95660X










R Square 0.6436812

Adjusted R Square

0.5546016

Standard Error

2.7108355

Observations

6

ANOVA


Regression 1 53.100484 53.100484 7.2259036 0.0547657

Residual 4 29.394516 7.348629

Total 5 82.495



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







Y=55.120—0.519X









R Square 0.0178485

Adjusted R Square

-0.2276893

Standard Error

4.5006274

Observations

6

ANOVA


Regression 1 1.4724137 1.4724137 0.0726915 0.8007947

Residual 4 81.022586 20.255647

Total 5 82.495



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





Y=72.46267—1.60424X










R Square 0.1981606

Adjusted R Square

-0.0022993

Standard Error

4.0665631

Observations

6

ANOVA


Regression 1 16.347258 16.347258 0.9885301 0.3763773

Residual 4 66.147742 16.536935

Total 5 82.495



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





Y=63.260—1.959X







Our analysis:



R Square 0.1978432

Adjusted R Square

-0.0026959

Standard Error

4.0673678

Observations

6

ANOVA


Regression 1 16.321078 16.321078 0.9865565 0.3768063

Residual 4 66.173922 16.54348

Total 5 82.495



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





Y=13.56+0.283X










R Square 0.3757283

Adjusted R Square

0.2196604

Standard Error

3.5881504

Observations

6

ANOVA


Regression 1 30.995707 30.995707 2.4074666 0.1957043

Residual 4 51.499293 12.874823

Total 5 82.495



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





Y=63.260—1.959X



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


Current ratio:




R Square 0.6372543



Observations 6

ANOVA


Regression 1 346.1523 346.1523 7.0270096 0.0569312

Residual 4 197.04103 49.260258

Total 5 543.19333


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





Y=44.21845--17.95660X










R Square 0.1700531



Observations 6

ANOVA


Regression 1 92.371709 92.371709 0.8195854 0.4165004

Residual 4 450.82162 112.70541

Total 5 543.19333


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







Y=55.120—0.519X









R Square 0.2926944



Observations 6

ANOVA


Regression 1 158.98965 158.98965 1.6552641 0.2676571

Residual 4 384.20369 96.050922

Total 5 543.19333


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





Y=72.46267—1.60424X










R Square 0.8952018



Observations 6

ANOVA


Regression 1 486.26766 486.26766 34.168602 0.0042715

Residual 4 56.925672 14.231418

Total 5 543.19333


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





Y=63.260—1.959X







Our analysis:



R Square 0.6428991

Adjusted R Square

0.5536239


Observations 6

ANOVA


Regression 1 349.21853 349.21853 7.2013171 0.0550266

Residual 4 193.97481 48.493702

Total 5 543.19333


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





Y=13.56+0.283X










R Square 0.290938



Observations 6

ANOVA


Regression 1 158.03559 158.03559 1.6412557 0.269384

Residual 4 385.15774 96.289436

Total 5 543.19333


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





Y=63.260—1.959X




TATA STEEL INDIA


Current ratio:




R Square 0.307652



Observations 6

ANOVA


Regression 1 9.5413133 9.5413133 1.7774412 0.253326

Residual 4 21.47202 5.368005

Total 5 31.013333


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





Y=44.21845--17.95660X










R Square 0.0437096



Observations 6

ANOVA


Regression 1 1.3555816 1.3555816 0.18283 0.6909664

Residual 4 29.657752 7.4144379

Total 5 31.013333


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







Y=55.120—0.519X









R Square 0.7614554



Observations 6

ANOVA


Regression 1 23.615269 23.615269 12.768351 0.0233072

Residual 4 7.3980641 1.849516

Total 5 31.013333


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





Y=72.46267—1.60424X









Multiple R 0.587305

R Square 0.3449271



Observations 6

ANOVA


Regression 1 10.69734 10.69734 2.1061907 0.2203313

Residual 4 20.315994 5.0789985

Total 5 31.013333


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





Y=63.260—1.959X







Our analysis:



R Square 0.6654623



Observations 6

ANOVA


Regression 1 20.638205 20.638205 7.9568002 0.0477902

Residual 4 10.375128 2.593782

Total 5 31.013333


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





Y=13.56+0.283X










R Square 0.6475234



Observations 6

ANOVA


Regression 1 20.081858 20.081858 7.3482697 0.0534947

Residual 4 10.931476 2.7328689

Total 5 31.013333


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





Y=63.260—1.959X




fm wcm analysis

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