Regression in Data Analytics & Business Statistics
Statistical Analysis and Forecasting using Regression equations
What you will learn
Concept of Regression and Applications of Regression Analysis in Data Analytics and Management
Forecasting of Data using Regression Method
Concept of Least Squares Method and its calculation
Forecasting using Regression Coefficient Method
Typical problems and Case Studies on regression Analysis
Why take this course?
Simple regression is used to examine the relationship between one dependent and one independent variable. After performing an analysis, the regression statistics can be used to predict the dependent variable when the independent variable is known. Regression goes beyond correlation by adding prediction capabilities. Correlation shows the relation between two variables, e.g. X and Y
Regression takes this one step further. It predicts or estimates a score for an individual on one variable based his/her/its score on the other variable, and on the correlation between the two variables. Regression is a great and an effective method of forecasting.
In this course , we will explain:
(i) how to construct the regression equations using Method of Least squares.
(ii) Then the construction of regression equations using Regression coefficients.
(iii) Calculation of correlation coefficient using Regression equations
The regression equation gives you a valid basis for predicting different Y values for different individuals. The predicted scores will fall closer to the actual scores. Both correlation and simple linear regression can be used to examine the presence of a linear relationship between two variables providing certain assumptions. The results of the analysis, however, need to be interpreted with care, particularly when looking for a causal relationship or when using the regression equation for prediction. Regression allows practitioners and researchers to infer how an individual will score on some measure based on the analysis of a sample. Thus the interpretation of regression has a great significance in inferential statistics.