I used the historic data, used liner regression model Linear...

I used the historic data, used liner regression model Linear

Linear Regression Equation:

The predicted closing price can be estimated using the equation:

$$\text{Predicted Price}=(\text{Slope}\times \text{Date in Ordinal Format})+\text{Intercept}$$

An R-squared value of 0.71 indicates that about 71% of the variance in Yancoal's closing prices can be explained by the linear regression model. This suggests that the model has a moderate level of accuracy in predicting price movements based on historical trendsfactors, such as market conditions, economic events, and company-specific news, may also impact the stock price, so it has some 29 percent variance level..