Correlation between two variables helps us identify the  relation between the two variables.  It basically means how much variance or change in one variable effects the other. The value of Correlation lies between  -1 & +1 . If the value of Correlation is -1 , it means that the two variables  are  perfectly inversely related  & +1 means that two variables are perfectly directly proprtional meaning that increase in one would lead to similar increase in other variable.

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In this data all the predictors are positively correlated with the response variable,meaning that increase in one will lead to increase in the final  marks of the student.

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We can see that all the predictors are positively Correlated with the final .We can notice that total & percent are the two variables which are highly positively related with final .This is quite obvious as final is a part of the total & percent. Higher the final score higher is total or percent. According to the Scatter plots we can say that the final and preditors are related to each other through a linear line Hence the linearity assumption is true for all. [ Assumption in order to predict the values through Regression model ]