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Words: 583
Articles: 25
Y
=
Xβ
+
ε
,
ε
∼
N
(
0
,
σ
2
I
)
, and full-rank
X
gives
β
=
(
X
T
X
)
−
1
X
T
Y
.
Table of contents
583
25
Cochran's theorem
Normal linear model
24
Linear regression
Normal linear model
140
4
Design matrix
Linear regression
23
Regression coefficient
Linear regression
20
Interaction term
Linear regression
26
Attenuation bias from classical measurement error
Linear regression
46
R linear-model formula
Normal linear model
53
1
Degrees of freedom of a factor predictor
R linear-model formula
24
Hat matrix
Normal linear model
25
Normal linear-model confidence ellipsoid
Normal linear model
48
1
Cook's distance
Normal linear-model confidence ellipsoid
34
Multicollinearity
Normal linear model
31
Ordinary least squares estimators
Normal linear model
69
1
Residual sum of squares in simple linear regression
Ordinary least squares estimators
52
Gauss-Markov theorem
Normal linear model
14
Weighted least squares
Normal linear model
47
2
Generalized least squares
Weighted least squares
32
1
Whitening transformation
Generalized least squares
20
Normal equation
Normal linear model
9
Consistency of least squares
Normal linear model
21
One-way normal linear model
Normal linear model
91
4
Cell-means parametrization
One-way normal linear model
41
1
Linear contrast of cell means
Cell-means parametrization
21
Full-dominance mean constraint
One-way normal linear model
12
Additive allele-count model
One-way normal linear model
18
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Statistical modelling
Probability and statistics
Area of mathematics
Mathematics
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