Correlation and Regression Assignment Help

Correlation and Regression Assignment Help
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Correlation and Regression Assignment Help

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Statistics Assignment on Correlation and Regression

Correlation analysis is the study of the relationship between two or more variables. If a change in one variable brings a change in the other variable, they are said to be correlated. The types of correlation are:

  1. Positive Correlation: The two variables are said to be positively correlated if the direction in the change of the variables is the same.
  2. Negative Correlation: When the direction in the change of the variables is opposite, it is a negative correlation.
  3. Linear Correlation: If there is a constant ratio between the amounts of change between the two variables, it is termed as Linear Correlation.
  4. Non-Linear Correlation: When the change in one variable in respect of the change in the other variable is not a constant ratio, it is termed as a non-linear correlation.
  5. Simple, Partial and Multiple Correlation: Simple Correlation is the study of the relation between two variables. When three and more than three variables are studies at the same time, it is a multiple correlation. In partial correlation, there are more two variables though the relation between two variables are studies and others are kept constant.

Regression is a Statistical concept that measures the relation between two variables. The two variables are response variable and a predictor variable. The predictor variable influences the response variable. It is a cause and effect study of one variable on the other variable. An increase in the price of one effects its demand is an example where regression concept is used. The study on regression was first conducted by Sir Francis Galton on the stature of parents and children. The study of Regression Analysis consists of the following steps:

  • The problem
  • Selection of the relevant variables
  • Collection of data
  • Model specification
  • Choice of the fitting method
  • Mode fitting
  • Model validation
  • Use the model chosen for solving the problem

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