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Correlation and Regression analysis chapter 6 (Pearson correlation…
Correlation and Regression analysis chapter 6
Regression analysis involves identifying the relationship between a dependent variable and one or more independent variables.Correlation and regression analysis are related in the sense that both deal with relationships among variables.
The bivariate Data
data that has two variables.[1] The quantities from these two variables are often represented using a scatter plot. This is done so that the relationship (if any) between the variables is easily seen.[2] For example, bivariate data on a scatter plot could be used to study the relationship between stride length and length of legs.
deals with two variables
independent variable
is a condition or piece of data in an experiment that can be controlled or changed
Dependent variable
is a condition or piece of data in an experiment that is controlled or influenced by an outside factor, most often the independent variable.
Pearson correlation coeficient
also referred to as the Pearson's r or Pearson product-moment correlation coefficient (PPMCC), is a measure of the linear dependence (correlation) between two variables X and Y. It has a value between +1 and −1 inclusive, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation. It is widely used in the sciences.
Examples of scatter diagrams with different values of correlation coefficient (ρ)
Linear Regression
is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression.
