Discriminant Analysis - Statpower

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We can use the regression formula (5) to classify scores, i.e., attempt to categorize them into groups. A score y ˆi classified as being in the group whose predicted score mean is closest to it. Since the group means are βˆ′ ( x − x ) and ˆ′ ( − ) the midpoint, or cutpoint, is 1 ( ) ˆ ( ) β x x (6) βˆ′ x1− x + β′ x2 − x ˆ ⎛ x1+ x ′ 2 = β − ⎞ ⎜ x ⎟ (7) 2 ⎝ 2 ⎠ 2
Recall that group 1 is associated with positive scores and group 2 negative scores. Consequently, if a predicted score y ˆi is above the cutpoint in Equation (7), it is classified in group 1, otherwise in group 2. That is, a score is classified in group 1 if or ˆ 1 2 ( ) ˆ ⎛ x + x β′ x ⎞ i − x > β′ ⎜ −x ⎟ (8) ⎝ 2 ⎠
Page 1 and 2: Discriminant Analysis In discrimina
Page 3 and 4: Linear Discriminant Function - Two
Page 5 and 6: The linear discriminant function wa
Page 7: ( ) βˆ = cA −1 x −x (3) 1 2 w
Page 11 and 12: so ˆβ in Equation (9) can be repl
Page 13 and 14: Of course, we could generate a diff
Page 15 and 16: above function values will lead to
Page 17 and 18: Example. Morrison (1990, page 143)
Page 19 and 20: In this case, −1 S is given by
Page 21 and 22: Note that variables x 1 and x 3 do
Page 23 and 24: But first, let’s perform the anal
Page 25 and 26: Now, if we drop the two non-contrib

Discriminant Analysis - Statpower

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