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regression model. Lower signiﬁcance levels indicate Table 2: Coeﬃcients P-value

that stronger evidence before is required to reject the null 0.0000 1.0000

hypothesis. For the purpose of the study the signiﬁcance Intercept 0.3372 0.0056 *

level is 5% or 0.05. Import –0.5315 0.0005 *

Despatch –0.4052 0.0063 *

However, it is worthwhile to note that there are Demand –0.0015 0.9889

certain limitations to this study. The article essentially OD

attempts to study the movement of the variables

during a period of shock. Hence, by nature, such a Regression Statistics

study will have restrictions on the size of the sample

set. This limits the accuracy of the model in terms of R Square 0.85747

the coefficients of the model (βi). However, the models

being considered here is solely for the purpose of Adjusted R Square 0.81675

identifying the movement and dependency of the

dependent variable on the independent variables, that F-stat 21.05707

is, the sign (+/–) of the βi’s and the p-value of the null

hypothesis that the βi = 0. Significance F 8.37E-06

Observations 19

* indicates that the respective variables are signiﬁcant at a

signiﬁcance level of 0.05

RESULT As seen from the regression statistics, Import, Despatch

and Demand are the variables that aﬀect the movement

Prior to the multiple linear regression model, we test for of Stock of coal in days at an aggregate level across India,

correlation between the independent variables. Table 1 as evidenced by the p-value. The F-statistic conﬁrms

shows the correlation matrix between the independent that the regression model is improved by the inclusion

variables. There is a moderate and positive correlation of the independent variables. We note that the variable

between Demand and Despatch, and similarly for OD corresponding to overdues do not aﬀect the aggregate

and Demand. The correlation between Despatch and coal stock position, as evidenced by the corresponding

Demand is intuitive, as it shows that with increasing p-value. This conﬁrms the hypothesis that increasing

power demand, there is a pull for coal from suppliers, overdues to gencos (and subsequently to coal suppliers)

thereby increasing the despatch volumes. However, we did not aﬀect the coal stock of power plants during this

see that for none of the independent variables there is a period. This result is expected, as increasing overdues

strong correlation with the others. is unlikely to lead to curtailment of supplies from coal

companies.

Table 1: Import Despatch Demand OD The results show that the coeﬃcient of the variable

1 1 corresponding to demand is negative signifying that with

Import 1 1 increased demand, there is depletion in the coal stock.

Despatch – 0.0750 0.4968 0.3561 This result is as expected, as an increase in power demand

Demand – 0.2027 0.1005 is likely to be met by higher coal consumption. This trend

OD – 0.0931 can also be observed graphically as shown in Figure 1.

Similarly, the coeﬃcient of the variable corresponding

We proceed to formulate the multiple linear regression to volume of imported coal is positive, showing that a

model with these independent variables. The summary decrease in the volume of imported coal is associated

statistics of the multiple regression model is laid out in with a decrease in number of days of coal stock with

Table 2. thermal power plants.

18

that stronger evidence before is required to reject the null 0.0000 1.0000

hypothesis. For the purpose of the study the signiﬁcance Intercept 0.3372 0.0056 *

level is 5% or 0.05. Import –0.5315 0.0005 *

Despatch –0.4052 0.0063 *

However, it is worthwhile to note that there are Demand –0.0015 0.9889

certain limitations to this study. The article essentially OD

attempts to study the movement of the variables

during a period of shock. Hence, by nature, such a Regression Statistics

study will have restrictions on the size of the sample

set. This limits the accuracy of the model in terms of R Square 0.85747

the coefficients of the model (βi). However, the models

being considered here is solely for the purpose of Adjusted R Square 0.81675

identifying the movement and dependency of the

dependent variable on the independent variables, that F-stat 21.05707

is, the sign (+/–) of the βi’s and the p-value of the null

hypothesis that the βi = 0. Significance F 8.37E-06

Observations 19

* indicates that the respective variables are signiﬁcant at a

signiﬁcance level of 0.05

RESULT As seen from the regression statistics, Import, Despatch

and Demand are the variables that aﬀect the movement

Prior to the multiple linear regression model, we test for of Stock of coal in days at an aggregate level across India,

correlation between the independent variables. Table 1 as evidenced by the p-value. The F-statistic conﬁrms

shows the correlation matrix between the independent that the regression model is improved by the inclusion

variables. There is a moderate and positive correlation of the independent variables. We note that the variable

between Demand and Despatch, and similarly for OD corresponding to overdues do not aﬀect the aggregate

and Demand. The correlation between Despatch and coal stock position, as evidenced by the corresponding

Demand is intuitive, as it shows that with increasing p-value. This conﬁrms the hypothesis that increasing

power demand, there is a pull for coal from suppliers, overdues to gencos (and subsequently to coal suppliers)

thereby increasing the despatch volumes. However, we did not aﬀect the coal stock of power plants during this

see that for none of the independent variables there is a period. This result is expected, as increasing overdues

strong correlation with the others. is unlikely to lead to curtailment of supplies from coal

companies.

Table 1: Import Despatch Demand OD The results show that the coeﬃcient of the variable

1 1 corresponding to demand is negative signifying that with

Import 1 1 increased demand, there is depletion in the coal stock.

Despatch – 0.0750 0.4968 0.3561 This result is as expected, as an increase in power demand

Demand – 0.2027 0.1005 is likely to be met by higher coal consumption. This trend

OD – 0.0931 can also be observed graphically as shown in Figure 1.

Similarly, the coeﬃcient of the variable corresponding

We proceed to formulate the multiple linear regression to volume of imported coal is positive, showing that a

model with these independent variables. The summary decrease in the volume of imported coal is associated

statistics of the multiple regression model is laid out in with a decrease in number of days of coal stock with

Table 2. thermal power plants.

18