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chpt. 6(scatter, association, correlation) & 7(linear regression)

Scatter plots show the relationship between 2 quantitative variables measured on the same cases.
Direction (describing association) positive (increase) or negative (decrease)
Form (describing association) patterns: scatter, mostly linear
Strength (describing association) amount of scatter: weak (lot) or strong (close, clump, or linear)
Line of best fit is equal to least squares line
r^2 or R^2 (squared correlation) gives the fraction of the data's variation accounted for by the model and 1-r^2 is the fraction of the original variation left in the residuals.
r^2 is always between 0% and 100%
Correlation coefficient (-1<r>1) and conditions you must check Quantitative variables conditions: correlation only for quantitative straight enough condition: judgement call No outliers condition: check for them, they effect everything
Best fit means least squares
residuals(e)= observed value (y) - predicted value (y-hat)
X and Y variables in scatterplots x= explanatory y= response
Lurking variables In scatter plots and correlation coefficients never prove causation. A hidden variable that stands behind, a relationship and determines it.
Created by: jcore