The Importance of Using Regression Analysis The values of the residual or error usually follows the normal distribution.The value of the error or residual is not correlated across all observations.The residual or error values is constants across all observations.The variables (the dependent and independent variables) depict a linear correlation between the slope (gradient) and the intercept.There are six basics assumptions made when employing the linear regression technique: It is important to note that when analysing data of multiple regressions, you have to differentiate the variables by using superscripts. In regression, the assessment of the relationship is by use of a straight line (linear regression) to approximate the values of other data. regression helps in determining the mathematical relationship between the two. In general, linear regression uses a group of different variables to predict the outcomes of other variables denoted by Y. X= the independent variable (the value(s) you are using to determine the value(s) of Y). Y = the dependent variable (the value you are trying to determine) Multiple linear regression: Y= a + b1X1 + b2X2 + b3X3 + … bzXz + c The general expression for linear regression is: Since the deviations are first squared, there is no cancellation of the negative and the positive values. The least Square method minimises the sum of the squares of deviations from each data point to the line. By using the method, one can calculate the line of best fit from the available observed data. The Least Square Method assists in formulating a fitting regression line. The line of best fit can be obtained by joining closely related points or by using the Least Square Method. Multiple linear regressions: the analysis uses more than one independent variable to predict, determine or understand the nature of dependent variables.Simple linear regression: the analysis uses a single (one) independent variable to predict or explain the nature of a dependent variable(y).The two types of linear regression include: The determination of the relationship is by using a line of best fit (a regression line). Linear regression determines the correlation between a dependent variable (Y) and either one or more independent variables (X).
The nature of the regression line is always linear, giving the technique the name linear regression.
In linear regression, the dependent variable is continuous, whereas the independent variable(s) is either discrete or continuous. This type of regression technique is among the first few techniques leant by data analysts while learning on predictive models. Linear regression is a commonly used modeling technique for data analysis.
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