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Regression
Instead of deciding whether the independent variable has "some effect" on your dependent variable, we wish to see how well we can predict the dependent variable (response variable) from the independent variable (explanatory variable).
Of course, if the independent variable has no effect on the dependent variable, then the independent variable will NOT help us predict the dependent variable.
If the independent variable has a strong effect on the dependent variable, then we will be very GOOD at predicting the dependent variable from the independent variable.
Linear(strong correlation) Linear
(weak correlation)
Linear(strong correlation)
Non-linear(strong correlation) Non-linear
(strong correlation)
Non-linear(no correlation) No Relationship
(no correlation)
The ‘Coefficient of Correlation’, r
How close are the datapoints in the scatterplot to the best-fitting
regression line?
The Coefficient of Correlation Statistic (r)
r is a value between -1 and 1.
r = 1: Perfect Positive Linear Correlation
r = -1: Perfect Negative Linear Correlation
r = 0: No Linear Correlation
What Do Correlations Tell Us?
Correlations allow us to predict one score from another (using the "regression equation").
Good prediction doesn't always require understanding why there is a relationship.
Correlation does not imply causation!
Correlations are often due to coincidences or common cause factors.
Regression HypothesesRegression Hypotheses
• Null Hypothesis: Null Hypothesis:
HH00: : ββ11 = 0 = 0
• Alternative Hypothesis:Alternative Hypothesis:
HH11: : ββ11 ≠ 0 ≠ 0
Estimated Parameters
• From your data, we will get an estimate of β1.
• We will call this estimate B1.
• From your data, we will get an estimate of β0.
• We will call this estimate B0.
Estimated Parameters
• Slope
• Intercept
XBYB 10
formulanasty 1 B
(Do (Do notnot memorize this formula) memorize this formula)