Regression notes

Correlation ( r )

• the association between x-context and y-context is weak/moderate/strong(strength) and positive/negative(direction) and linear

• What you need to remember for correlation

  • X- context

  • y-context

  • weak/moderate/strong (Strength)

  • positive/negative (direction)

  • linear (form)

Residual

• The actual y-context was residual above/below the predicted value when x-context = #

• What you need to remember for residual

  • y-context

  • residual

  • above/below

  • predicted

  • x-context = #

y-intercept

• the predicted y-content when x=0 context is the y-intercept

• what to know for y-int

  • predicted

  • y-context

  • x=0 context

  • y-intercept

Slope

• the predicted y-context increases/decreases by slope for each additional x-context

• what to know for slope

  • predicted

  • y-context

  • increases/decreases

  • slope

coefficient of determination (r²)

• About r²% of the variation in y-context can be explained by the linear relationship with x-context

• what to know for the coefficient of determination

  • r²%

  • variation

  • y-context

  • linear relationship

  • x-context

Describe the relationship

• Be sure to address strength, association, form, and unusual features (in context)

• What to know for describing the relationship

  • strength (weak/moderate/strong)

  • association

  • form (positive/negative & linear)

  • Unusual features (outliers)