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interpreting residuals (positive)
The actual (response variable) is greater than predicted by (residual units)
interpreting residuals (negative)
The actual (response variable) is less than predicted by (residual units)
interpreting s
When using the LSRL with (explanatory variable) to predict (response variable), we will typically be off by (value of s with units of response variable y)
interpreting r²
r²% of the variation in (response variable) can be explained by the linear relationship with (explanatory variable)
CDOFS
context, direction, outliers/clusters, form, strength
interpreting slope
for every 1 (unit) increase in (explanatory variable), our model predicts an average increase/decrease of (slope) in (response variable)
interpreting y-intercept
When the (explanatory variable) is zero (units), our model predicts that the (response variable) would be (y-intercept)
high leverage/not an outlier
changed slope/y-intercept
low leverage/outlier
lowered r
high leverage/outlier
changed slope/y-intercept/lowered r