Regression

Best line has overall smallest residuals

minimizes the sum of squares of the residuals

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r2 represents the percentage of the variance in y
that can be explained by changes in x - tells us how good line is (0-1)

to test how good b0 and b1

Conf Int

Hypothesis Test

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Residual normally distributed
mean = 0

SD =
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Also called "s" or standard error of line

In neumerator = SS or Residuals
Den = dof of residuals

Confidence Interval

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t (n-2) dof

Hypothesis Test

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b-0 cause of null hypothesis
dof (n-2)

Defs

Suppose we want to measure the effect of household size
(predictor) on the number of cars owned by the household
(dependent variable)

X t by 2 - two sided test

In a regression, b1 or b0 are significant (i.e. null hypothesis
rejected), if and only if, the C=1-alpha interval does not
contain 0.

ANOVA

Mean Square - It gives us a sense of the average degree of variation along the regression line, and the residual.
SS / dof

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F stat
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P val= sig of F - for b1