Simple Line Regression

Simple Line Regression

1. There was a positive [or negative] correlation between {predictor} and {criterion}, Beta=__ and __% of the variation in {criterion} could be accounted for by variation in{predictor} (adjusted Rsquared). [Predictor] is a {non}significant predictor of [criterion], t=__, p=__.. As [predictor] increases, [criterion] also tend to increases{decreases}. The relationship can be described by the linear regression equation Y=a+b(X), where X is an individual participants [predictor] and Y is the best predicition of their [criterion]. The standard deviation od the estimate was __.

   • Adjusted Rsquared (percentage of variance)::- takes account of the number of particpants in the study and number of predictor variables entered into the equation (X)
   • Standard Error of Estimate:- is an average amount by which a given estimate is likely to be wrong

2. Models the relationship between 2 variables

3. Association:- looking at the relationship between the 2 variables

4. Correlation vs Regression-> measures the strength of the linear relationship between 2 varibales

5. X=predictor Horizontal

   • Y=criterion Vertical

6. Regression line (line of best fit) allow us to make predictions. Equation/model- Y= a+b X

   • Substitute the value of X(_) into the regression equation Y=a+bX.. We would therefore have Y=[B constant] + [the next coefficient] (_)

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