Best of problems in Regression Analysis especially handpicked for you.
If you are a student of ISC Mathematics in Class 12, this post will benefit you greatly. Even if you are not in Class 12, but a student of Mathematics, you would definitely benefit by learning from this post. Also, do share this post with Mathematics students of Class 12.
I am a Math educator and my Science students in Class 12 prefer this topic of Regression Analysis in comparison with Vectors and 3 Dimensional Geometry. This post gives you a gist of Regression Analysis covering all the types of problems needed for Class 12.
The statistical methods which help you to determine the value of 1 variable given the other variable is called Regression.
The course starts with teaching you the difference between dependent and independent variable.
Basic Formulas in Regression Analysis
You are introduced to the 2 regression coefficients. You will realise that the regression coefficient of x on y is the slope of the regression line of x on y and the regression coefficient of y on x is the slope of the regression line of y on x.
The relationships between the regression and correlation coefficients is also explained.
Note This important tip in Regression Analysis
You will learn the relationship between regression coefficients and standard deviations.
Another important point for you to remember is that the means of the 2 variables are obtained by solving the 2 regression equations simultaneously.
The first lesson gives you the overview of all these and teaches you to predict the value of 1 variable given the other.
The basic formulas of regression coefficients given correlated values of x and y are taught to you here.
Given 2 lines of regression, you will learn the techniques of identifying the lines of regression.
Learn this simple problem on Regression
The 2 lines of Regression intersect at the means
The next lesson richly enforces the previous lesson with a wide plethora of problems for you.
Problems pertaining to fitting a straight line to the data or determining the least square line of regression of y on x are also explained to you.
What happens if the values of x and y are very large? Then we take an assumed mean for x and y and proceed.
Calculating Regression coefficients with assumed means