PSYCH 2300

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PSYCH 2300
2013-07-02 12:30:10
Chapter 15

Correlation and Regression
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  1. Correlation
    Statistical technique that is used to measure and describe a relationship between two variables
  2. Positive Correlation
    The two variables tend to change in the same direction.

    As the value of X increases, the Y also increases. Vice Versa
  3. Negative Correlation
    • Two variables go in opposite directions.
    • As X increases, Y decreases - an inverse relationship
  4. Perfect Correlation
    Identified by a correlation of 1.00 and indicates a perfectly consistent relationship
  5. Pearson Correlation
    Measures the degree to and direction of the linear relationship between two variables
  6. Sum of Products or SP
    Measure the amount of co-variability between TWO variables

  7. Restricted Range
    A correlation that is computed from scores that do not represent the full range of possible values, should be cautious when interpreting correlation.
  8. 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
  9. Linear Relationship
    • A linear relationship between two variables can be expressed by the equation:
    • Where a and b are fixed constants
  10. Linear Equation
  11. Slope
    Slope determines how much the Y variable changes when X is increased by 1 point
  12. Y-Intercept
    • Determines the value of Y when X=0
    • a value
  13. Regression
    Statistical technique for finding the best fitting straight line for a set of data
  14. Regression Line
    The resulting straight line
  15. Least-squared-error
    • The best fitting line has the smallest total squared error
  16. Regression Equation for Y
  17. Standard Error of Estimate
    Gives a measure of the standard distance between a regression line and the actual data points
  18. 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
  19. Unpredicted Variability (SSresidual)
    residual of an observed value is the difference between the observed value and theestimated function value