#CALCULATING LINEAR REGRESSION EQUATION SOFTWARE#

Linear regression is a linear method for modelling the relationship between the independent variables and dependent variables. One variable will be considered to be an explanatory variable, while others will be considered to be a dependent variable.

It is very important and used for easy analysis of the dependency of two variables. One or more independent variable(s) (that is interval or ratio). One or more independent variable(s) (that is interval or ratio or dichotomous). One is the dependent variable (that is nominal). One or more independent variable(s) (that is nominal or dichotomous). One is the dependent variable (that is ordinal). Two or more independent variable(s) ( that is interval or ratio or dichotomous). One is the dependent variable (that is binary). Two or more independent variables ( that is interval or ratio or dichotomous). One is the independent variable (that is interval or ratio or dichotomous). One is the dependent variable (that is interval or ratio). For example, a modeller might want to relate the weights of individuals to their heights using the concept of linear regression.

In this concept, one variable is considered to be an explanatory variable, and the other variable is considered to be a dependent variable. Linear regression is known to be the most basic and commonly used predictive analysis. Linear regression formula helps to define this linear relation that is present between the two quantities and how they are interdependent. If these two quantities are further plotted on a graph, it is observed that there is a linear relation between them. If the scatter plot reveals non linear relationship, often a suitable transformation can be used to attain linearity.Ever been to a shop and have noticed how the size of an object directly affects its price as well? Well, a relation is seen when two quantities are compared and there is either an increase or decrease in the value of both of them or it can also be that one quantity increases while the other decreases and vice versa. One can construct the scatter plot to confirm this assumption. It finds the slope and the intercept assuming that the relationship between the independent and dependent variable can be best explained by a straight line. Linear regression does not test whether data is linear. Statistically, it is equivalent to testing the null hypothesis that the regression coefficient is zero. A related question is whether the independent variable significantly influences the dependent variable. The closer R2 is to 1, the better is the model and its prediction.

#CALCULATING LINEAR REGRESSION EQUATION SOFTWARE#

All software provides it whenever regression procedure is run. Once a line of regression has been constructed, one can check how good it is (in terms of predictive ability) by examining the coefficient of determination (R2). A similar interpretation can be given for the regression coefficient of X on Y. It represents change in the value of dependent variable (Y) corresponding to unit change in the value of independent variable (X).įor instance if the regression coefficient of Y on X is 0.53 units, it would indicate that Y will increase by 0.53 if X increased by 1 unit. The coefficient of X in the line of regression of Y on X is called the regression coefficient of Y on X. We would then be able to estimate crop yield given rainfall.Ĭareless use of linear regression analysis could mean construction of regression line of X on Y which would demonstrate the laughable scenario that rainfall is dependent on crop yield this would suggest that if you grow really big crops you will be guaranteed a heavy rainfall. Here construction of regression line of Y on X would make sense and would be able to demonstrate the dependence of crop yield on rainfall. Choice of Line of Regressionįor example, consider two variables crop yield (Y) and rainfall (X). Often, only one of these lines make sense.Įxactly which of these will be appropriate for the analysis in hand will depend on labeling of dependent and independent variable in the problem to be analyzed. On the other hand, the line of regression of X on Y is given by X = c + dY which is used to predict the unknown value of variable X using the known value of variable Y. This is used to predict the unknown value of variable Y when value of variable X is known. The line of regression of Y on X is given by Y = a + bX where a and b are unknown constants known as intercept and slope of the equation. There are two lines of regression- that of Y on X and X on Y.

Calculating linear regression equation