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Regression (Linear regression (Once an equation for some dependent…
Regression
Linear regression
Once an equation for some dependent variable y has been determined in terms of x, we can use the equation to predict values for y for known values of x.
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Significance
People who had done 800 and 1500 m, use regression equation to estimate future 800 m, score of how strong the equation is.
Data elite runners vs general population not the same
Nice spread of data to make it effective
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Presentation of results
Scatter plot/ graph
Regression equation
y= a + b x x (known data); example: time it 2.004 x
Significance of regression equation + standard error (table) optional
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The purpose of regression is to determine an equation for some dependent variable in terms of some independent variable given cases where the values of the dependent and independent variables are known.
Known data = x (a) unknown = y (b), linear relationship (x to model y)
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- Generate and visual data (scatter plot)
- Determine Regression line (regression equation y=)
- Put mean CI
- You have the graph and got all the info
Linear regression is used to determine an equation for the line of best fit between 2 variables (x and y) given a set of previous cases.
Once the model is produced, it can be used to predict future values of y if where x is known.