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Statistical technique that is used to measure and describe a relationship between two variables
The two variables tend to change in the same direction.
As the value of X increases, the Y also increases. Vice Versa
- Two variables go in opposite directions.
- As X increases, Y decreases - an inverse relationship
Identified by a correlation of 1.00 and indicates a perfectly consistent relationship
Measures the degree to and direction of the linear relationship between two variables
Sum of Products or SP
Measure the amount of co-variability between TWO variables
A correlation that is computed from scores that do not represent the full range of possible values, should be cautious when interpreting correlation.
Coefficient of determination =
- Measures the proportion of variability in one variable that can be determined from the relationship with the other variable.
- A correlation of r=0.80 (or -0.80), means that =0.64 (or 64%) of the variability in the Y scores can be predicted from the relationship with X
- A linear relationship between two variables can be expressed by the equation:
- Where a and b are fixed constants
Slope determines how much the Y variable changes when X is increased by 1 point
- Determines the value of Y when X=0
- a value
Statistical technique for finding the best fitting straight line for a set of data
The resulting straight line
- The best fitting line has the smallest total squared error
Regression Equation for Y
Standard Error of Estimate
Gives a measure of the standard distance between a regression line and the actual data points
Predicted Variability (SSregression)
A statistical technique used to measure the amount of variance in a data set that is not explained by the regression model
Unpredicted Variability (SSresidual)
residual of an observed value is the difference between the observed value and theestimated function value
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