Math Tutoring on Performing Linear Regression
In statistics Regression Analysis involves any technique for modeling and analyzing trends between a dependent variable, often y and an independent variable, often x. Regression analysis helps us make predictions outside of the given data. If the prediction is within the range of the given data, it is called Interpolation and if the prediction is outside the range of the given data, it is called Extrapolation. When analyzing data, the first step is usually to create a Scatter Diagram or Scatter Plot. This is just graph using the coordinate plane to display the values of the two variables as a collection of points. It is important to remember to label the x and y axes to know what they represent. So when we are talking about linear regression, there is the term called Correlation Coefficient, identified as R or r. The linear correlation coefficient measures the strength and direction of the linear relationship between the two variables. The regression line or line of best fit is graphed over the scatter plot. So R with a value of positive 1is a perfect linear fit with a positive slope and the points are tightly clustered along a line. R with a negative slope means that R would be close to the value of -1. If there is no correlation at all, R will have a value close to zero.
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1. What is the difference between interpolation and extrapolation?
2. What is Correlation Coefficient?
3. Explain the values of r as 1, -1 and 0.
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