Whenever you are given a series of data points, you make a linear regression by estimating a line that comes as close to running through the points as possible. To maximize the accuracy of this line, it is constructed as a Least Square Regression Line (LSRL for short). The regression is the difference between the actual y value of a data point and the y value predicted by your line, and the LSRL minimizes the sum of all the squares of your regression on the line.
A Correlation is a number between -1 and 1 that indicates how well a straight line represents a series of points. A value greater than one means it shows a positive slope; a value less than one, a negative slope. The farther away the correlation is from 0, the less accurately a straight line describes the data.
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Linear regression can be used in statistics in order to create a model out a dependable scalar value and an explanatory variable. Linear regression has applications in finance, economics and environmental science.
The acronym OLS as pertaining to the field of statistics stands for Ordinary Least Squares, the standard linear regression procedure. This is the standard approach to overdetermined systems.
Linear Regression is a method to generate a "Line of Best fit" yes you can use it, but it depends on the data as to accuracy, standard deviation, etc. there are other types of regression like polynomial regression.
I want to develop a regression model for predicting YardsAllowed as a function of Takeaways, and I need to explain the statistical signifance of the model.
= CORREL(x values,y values) ***clarification**** CORREL gives you the correlation coefficient (r), which is different than the coefficient of determination (R2) outside of simple linear regression situations.