Why You Need To Comprehend Regression And Correlation Analysis

How often do we use regression and correlation analysis in our lives? People can relate a well-groomed entrepreneur to someone being financially successful. You guess that the energy you feel when you wake up early in the morning depends on how early you went to sleep the night before. A parent assumes that the more chocolate or sweets his/her child consumes, the more energetic his/her child is. Analysis of correlation and regression can give us an idea about the relationships between variables that can be used to determine future outcomes.

Regression and correlation analysis are statistical methods that are widely used in physical geography to analyze casual relationships between variables. Regression and correlation measure the scale of relationship among two or more variables in two variant but similar ways.

The linear relationship between two variables (dependent and independent variable) can be measured using the correlation coefficient. The values of the correlation coefficient can range from -1 to +1. A perfect positive correlation or +1 explains that as the independent variable increases the dependent variable can increase as well. A perfect negative correlation or -1 explains that as the independent variable increases the dependent variable decreases. A correlation of zero explains that there is no significant relationship among the two variables.

You can make use of regression analysis once you find the correlation between two variables. In forest biometry for example you are frequently faced with variables that are hard to measure and correlated. Using the regression equation allows you to use easy measurements to forecast the values that are complicated to measure. A linear regression equation shows a straight line relationship between the independent and dependent variables. Independent variable is usually assigned with the value x and dependent with the value y.

There are two regression coefficients and they are the constants b0 and b1. The constant b0 designates the intercept like the value on the y-axis where the regression line passes through. The slope of the line of regression is b1.

The data analyzed provides the actual value of these constants. The purpose of regression analysis is to determine the line that best fits the data. Once the optimum regression line is enforced to the data, the forecast of y based on the specified values of x are as similar to the true y values attainable.

You must remember that both regression and correlation analyses cannot be defined as building cause and effect relationships. They can only determine to what extent or how variables are affiliated with each other. Traders, investors and analysts are constantly seeking tools that can aid them with regression and correlation analysis. Though some of these tools can prove very powerful, they are still useless when not combined with effective human analysis.

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