45712
Description
Mind Map by Keith Walker, updated more than 1 year ago
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Created by Keith Walker
about 11 years ago
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Linear Regression
- Assumptions
- Linear relationship between x and y
- Observations are independant
- Residuals are Normally Distributed
- residuals have same variability
- x variable measured without error.
- Linear relationship between x and y
- Examining b or the slope. ie Null that
the true slope is zero. Reject if p<.05
- ANOVA F-ratio (explained & unexplained mean squares)
- b/SE(b) which follows t-distribution on n-2 dof
- ANOVA F-ratio (explained & unexplained mean squares)
- Y = a + bx
- Y = dependant varible
- a = intercept ( value of Y when x = 0)
- b = slope ∑(x-x͞) (y-y͞) / ∑(x-x͞)²
- explanatory variable
- Y = dependant varible
- When we are
interested in
predicting y from a
change in x; or
predicting an
explanatory variabel
from a dependant
one.
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