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Math Forum » Discussions » sci.math.* » sci.stat.math.independent

Topic: Interpretation of coefficients in multiple regressions which model
linear dependence on an IV
Replies: 146   Last Post: Dec 15, 2012 6:44 PM

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Ray Koopman

Posts: 3,345
Registered: 12/7/04
Re: Since 3rd regression computation needs df = 5, am requiring 15
observations for any given length singleton in any cell
Posted: Dec 3, 2012 11:31 PM
  Click to see the message monospaced in plain text Plain Text   Click to reply to this topic Reply

On Dec 3, 4:22 am, djh <halitsk...@att.net> wrote:
> Since the 3rd regression computation needs a df of 5, I am requiring
> at least 15 observations for any given length singleton in any cell,
> so N-5 >= 10.
>
> If you want a greater cutoff value, please let me know at your
> earliest convenience.

Here's what I get for the cell n's. You should probably check them
against what you get. It looks like B1 is going to give problems.
__________________________________________________________________

a1
N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S

24 36 47 75 93 31 36 34 41 87 94 28 40
25 48 66 83 91 35 36 50 48 84 86 26 31
26 30 44 68 83 30 29 47 45 95 100 26 31
27 52 58 80 95 27 35 39 46 97 106 37 34
28 58 64 79 76 26 37 31 34 104 98 33 35
29 65 74 112 100 45 54 42 44 91 95 29 29
30 55 64 80 82 34 38 34 33 100 93 37 36
31 50 65 84 71 21 28 56 49 88 95 34 37
32 37 47 98 83 33 31 35 40 85 90 33 33
33 48 54 101 100 43 42 43 45 76 90 44 43
34 56 73 83 82 27 27 35 32 90 92 34 30
35 54 59 86 84 32 31 38 35 70 68 41 33
36 43 57 68 67 23 21 33 32 85 86 31 28
37 57 68 95 95 24 27 34 39 75 78 16 25
38 33 38 70 74 43 44 30 26 74 76 23 31
39 28 30 66 64 24 24 26 22 72 74 43 42
40 40 46 75 78 51 48 39 39 65 63 29 25
41 29 32 75 75 27 30 24 28 65 67 29 24
42 31 31 64 65 19 19 44 38 66 68 34 38
43 27 33 41 44 26 25 38 38 57 58 29 31
44 32 35 66 65 35 35 35 34 51 53 21 22
45 29 29 51 57 25 25 43 46 74 83 24 27
46 33 30 56 55 25 20 27 22 56 56 36 36
47 40 45 75 74 22 23 25 23 59 63 - -
48 32 38 75 74 28 30 30 29 64 62 30 28
49 39 39 59 59 20 22 27 29 49 50 28 30
50 38 40 71 72 23 33 21 22 58 55 20 18
51 42 45 65 68 16 21 28 26 63 66 26 20
52 38 38 67 69 33 34 32 30 54 56 18 19
53 27 33 54 55 26 27 21 25 63 63 23 25
54 30 33 54 56 21 23 29 32 51 52 29 27
55 29 31 52 55 22 23 22 25 53 55 22 20
56 28 31 59 61 19 16 27 29 48 49 28 26
57 34 35 53 54 22 23 - - 43 45 21 20
58 32 32 59 57 22 25 32 36 47 45 17 17
59 24 24 42 42 - - 27 21 38 39 16 15
60 32 34 43 43 19 19 21 22 45 47 18 21
61 35 39 59 61 28 29 25 27 38 42 19 20
62 24 24 46 49 20 21 21 21 59 64 30 32
63 27 28 60 63 19 17 28 27 41 42 16 17
64 23 24 43 45 16 - - - 44 44 25 25
65 22 21 39 42 18 19 21 22 63 63 - -
66 34 35 58 58 16 17 36 37 54 58 19 20
67 39 40 48 49 - 19 28 31 50 51 24 24
68 16 19 37 39 17 17 15 17 40 39 - -
69 30 34 43 42 19 21 22 20 41 46 15 -
70 38 39 66 66 21 24 16 - 34 31 - -
71 24 24 62 66 - - 20 24 30 28 20 19
72 30 31 42 43 - - - - 29 32 15 15
73 29 31 39 41 15 15 20 21 34 35 - -
74 23 25 35 37 16 16 17 19 40 42 17 16
75 17 20 43 44 17 21 15 15 27 30 19 -
76 22 22 34 34 - - 17 15 29 29 - -
77 24 28 32 34 - - 15 15 29 28 15 -
78 29 30 45 46 - - - - 29 32 - -
79 19 19 39 38 16 19 - - 22 23 19 20
80 30 31 51 52 19 19 30 33 36 38 - -
81 25 26 31 32 16 22 - - 17 17 - -
82 22 24 32 33 - - 15 - 32 31 22 22
83 18 19 29 30 - - 21 25 34 36 - -
84 27 27 36 38 - - 15 17 36 39 - -
85 16 18 24 24 19 21 - - 27 30 19 19
86 20 21 25 25 - - - - 19 20 - -
87 16 17 27 27 - - 17 18 27 30 - -
88 20 21 20 21 - - - - 23 23 - -
89 11 11 28 28 - - 16 16 23 25 16 16
90 13 14 34 35 16 17 17 16 20 22 20 19
91 12 13 20 20 - - - 15 25 28 16 16
92 11 13 18 18 - - - - 31 33 15 -
93 26 26 34 34 17 18 - - 33 38 19 19
94 21 22 15 15 - - 19 19 33 35 - -
95 10 12 19 20 - - 18 20 31 34 - -
96 23 23 35 35 15 18 26 27 28 30 18 19
97 10 12 23 22 - - - - 30 35 18 17
98 12 13 15 16 17 16 - - 24 25 - -
99 17 17 22 23 - - - - 29 31 - -
100 14 14 27 27 - - - - 18 21 - 15
101 18 19 30 31 - - 15 16 21 25 16 17
102 17 18 23 23 - - - - 21 26 - -
103 11 13 15 15 - - 15 - 27 31 18 18
104 8 7 22 23 - - - - 16 20 - -
105 15 15 - - - - - - 20 26 - -
106 15 16 15 15 - - - - 20 25 - -
107 11 11 16 16 - - - - 20 21 - -
108 14 14 - - - - 15 17 25 31 16 16
109 23 25 18 18 - - 16 17 34 39 - -
110 15 16 - - - - 18 17 21 26 - -
111 18 18 - - - - 19 19 22 29 - -
112 14 15 - - - - - - 15 22 - -
113 11 11 - - 15 16 - - - 20 - -
114 19 20 - - - - - - - 21 - -
115 24 23 15 15 - 15 - - - - - -
116 11 10 - - 15 15 - - 17 24 - -
117 14 14 - - - - - - - 24 16 16
118 9 10 - - 16 17 - - - 21 - -
119 12 12 - - - - - - 18 32 - -
120 15 16 - - - - 18 18 17 22 - -
121 16 15 - - - - - - - 25 - -
122 16 16 - - 18 19 18 17 - - - -
123 16 16 - - - - - - - 18 - -
124 22 22 - - - - - - 16 27 - -

N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S
______________________________________________________

a3
N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S

24 22 47 62 92 38 34 18 30 52 52 19 -
25 25 73 63 108 18 26 17 16 56 51 26 15
26 22 35 58 104 26 29 - 17 47 56 19 16
27 16 37 52 90 24 30 22 21 59 61 19 17
28 23 50 72 109 26 40 18 23 60 51 27 25
29 19 39 84 110 33 39 29 26 76 74 - 19
30 36 53 64 86 22 38 19 27 55 51 27 22
31 30 54 64 98 23 26 19 29 51 61 25 18
32 27 47 49 68 30 30 19 17 51 49 24 20
33 21 33 69 98 27 32 20 21 59 70 22 19
34 19 37 54 84 22 26 - 18 59 60 27 24
35 18 37 72 92 26 29 18 - 39 49 17 16
36 18 28 50 69 20 24 18 17 55 53 18 -
37 18 39 53 77 30 31 19 22 42 49 20 18
38 20 33 60 88 28 32 20 18 45 40 20 23
39 17 29 69 83 21 23 21 22 43 47 24 19
40 20 38 48 64 20 22 17 24 42 51 22 18
41 24 32 39 61 25 29 26 28 48 55 23 23
42 23 33 58 67 22 23 22 23 49 48 18 18
43 29 37 44 63 19 24 - 18 50 47 24 22
44 27 43 62 85 28 26 17 18 45 52 18 17
45 23 38 49 66 - 15 15 19 54 48 23 22
46 17 36 62 75 19 22 16 - 36 44 23 20
47 15 20 45 63 - - - - 38 36 - -
48 15 34 42 47 - 15 19 20 40 40 17 17
49 20 32 42 52 17 19 - - 45 51 - -
50 19 30 50 59 24 33 23 26 40 42 16 -
51 16 27 38 45 - 20 16 16 49 43 15 -
52 20 30 43 49 - 15 - - 32 33 15 -
53 - 26 47 59 23 27 21 18 41 41 17 19
54 19 24 46 58 30 30 - - 30 30 - -
55 16 18 44 53 20 18 17 21 34 37 20 18
56 22 30 38 48 25 21 - - 48 46 16 15
57 23 31 37 47 17 19 15 - 31 37 17 19
58 - 21 46 54 28 28 18 21 27 30 19 15
59 - 21 31 38 - 16 18 22 34 34 20 16
60 - 20 40 47 21 21 - 15 37 37 15 -
61 16 24 48 58 22 22 15 - 27 33 - 19
62 15 26 46 51 18 18 21 22 32 35 - -
63 - 20 32 35 - 17 15 20 36 33 - -
64 16 23 42 49 - - - 16 37 37 - -
65 19 23 34 38 - - - - 45 43 - -
66 - 23 42 47 23 26 - - 21 24 - 15
67 - 19 33 36 - - 15 16 29 37 - -
68 - 22 38 42 - 17 - 16 31 31 - -
69 15 20 28 30 - - - - 34 31 - -
70 19 22 29 32 17 18 17 16 39 40 18 -
71 - 18 23 28 16 15 18 17 29 30 - -
72 - 20 35 39 - - - 15 19 17 - -
73 - 16 33 37 24 24 22 21 34 32 - -
74 25 28 41 44 26 23 17 16 31 30 - -
75 - 16 30 33 - - - 17 30 31 - -
76 - 17 25 28 - - - - 25 25 - -
77 17 23 32 33 - - - - 32 32 - -
78 17 20 40 43 - 15 - 17 26 27 17 -
79 - 16 22 26 - - - - 22 23 - -
80 - - 21 23 - - - - 31 33 - -
81 16 21 31 32 - - - - 23 23 - -
82 - - 25 28 - 16 - 15 29 28 - -
83 15 19 27 32 - - - - 18 21 - -
84 17 22 22 25 - - - - 25 27 - -
85 15 17 33 34 - - - - 21 21 - -
86 - - 31 33 15 16 - - 24 27 - -
87 - 18 30 32 - - - - - - - -
88 - - 24 24 - - - - 27 26 - -
89 - 17 28 28 - - - - 20 19 - -
90 - - 28 30 - - - - 15 15 - -
91 - 15 29 30 17 20 - - - 15 - -
92 - - - 15 - - 15 18 - - - -
93 - - 24 24 15 16 - - 25 27 - -
94 - 15 24 24 16 17 - - 16 16 - -
95 - - 27 27 - - - - 17 20 - -
96 16 18 26 26 - - - - 16 15 - -
97 - 16 24 25 - - - - 15 15 - -
98 - 17 25 25 - - - - 15 15 - -
99 16 20 28 28 - - - 16 21 23 - -
100 - - 24 25 - - - - 16 16 - -
101 - - 21 22 15 17 - - 22 22 - -
102 - - - - - - - - 17 16 - -
103 - - - - - - - - 22 22 - -
104 - 15 30 30 18 18 - - 19 20 - -
105 - - 27 27 - - - 15 - - - -
106 - - 22 23 - - - - 21 21 - -
107 - 15 21 21 - 15 - - 18 17 - -
108 - - 18 19 - - - - 18 18 - -
109 - - 19 20 - - - - 17 17 - -
110 - - 18 20 16 16 - - 21 20 - -
111 - - 17 19 - - - - - - - -
112 - - - - - - - - - - - -
113 - - - 15 - - - - - 15 - -
114 - - 22 23 - - - - 15 15 - -
115 - - 17 17 - - - - 16 16 - -
116 - - - 16 - - - - - - - -
117 - - 15 15 - - - - - - - -
118 - - 22 23 - - - - - - - -
119 - - - - - - - - - - - -
120 - - 17 17 - - - - - - - -
121 - - - - - - - - - - - -
122 - - - - - - - - - - - -
123 - - - - - - - - - - - -
124 - - - - - - - - - - - -

N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S
______________________________________________________

b1
N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S

24 - - - - - - 15 15 - 17 18 18
25 - - 22 23 - - - - 18 27 20 21
26 - - 23 23 - 15 - - - 17 22 22
27 - - 18 18 - 21 16 15 27 32 15 22
28 24 17 15 17 - - - 17 18 19 - -
29 25 - - 15 - - - 15 16 16 - -
30 29 17 18 17 - - - - 15 17 - -
31 19 - 16 - - - - - - - - -
32 20 15 - - - - - 17 18 23 - -
33 17 19 15 22 - - - - 38 27 19 22
34 22 18 - - - - - - 23 17 17 26
35 21 17 19 20 - - - - 30 31 22 27
36 23 26 - - - - - - 27 29 24 29
37 16 20 - - - - - - 34 30 27 28
38 17 18 18 17 15 - - - - - 17 15
39 15 15 - 15 - - - - 16 - 22 31
40 - 17 17 16 - - - - 20 - 19 24
41 29 26 - - 17 - - - - - 17 26
42 - - 21 22 - - - 16 17 15 18 26
43 - 15 17 18 18 18 17 - 15 - 25 27
44 - - 15 - - - - - - - - -
45 - - 17 - - - - - - - - -
46 - - - - - - - - - - - -
47 - 17 19 19 - - - - - - - -
48 - - - - - - - - 22 21 - -
49 - - - - - 15 - - 16 16 - -
50 - - 17 16 - - - - 15 15 - -
51 - - - - 16 16 - - 23 26 15 -
52 - - - - - - - - 19 21 - -
53 - - 15 - - - 25 21 17 18 16 15
54 - - 16 15 - - - - 27 27 19 24
55 - - - - - - 16 - 24 24 - -
56 - - - - - - - - 24 25 17 16
57 - - - 15 - - 16 16 38 38 24 23
58 16 15 - - 16 - - - - - - -
59 - - - - 16 - - 16 - 16 - -
60 19 17 25 26 15 - - - - - - -
61 19 16 22 22 - - - 17 - - - 15
62 19 20 - - 20 - - - 24 24 - 15
63 17 17 - - 15 - 15 15 19 21 - -
64 - 16 19 18 21 16 - - 20 19 - -
65 - - 19 19 - - - - 23 24 - -
66 - - 17 16 - - - - 18 18 - -
67 19 16 - 15 - - - 17 19 19 - -
68 - - - - - - - - 23 24 - -
69 - - - - - - - - 15 16 - 16
70 - - - - - - 15 - 18 18 - -
71 - - - - - - 16 15 26 26 - -
72 18 21 - - - - - - 29 30 - -
73 - - - - - - - - 21 21 - -
74 19 21 - - - - - - 22 22 15 15
75 - - - - - - - - - - 16 16
76 24 22 - - - - - - 19 19 - -
77 - - - - - - - - - - - -
78 20 19 - - - - - - 17 17 - -
79 20 19 - - - - - - 22 22 16 18
80 - - - - - - - - 19 18 - -
81 - 17 - - - - - - 19 19 - -
82 - 16 - - 15 - - - 15 15 - -
83 - - - - - - - - 16 17 18 16
84 17 18 - - - 17 18 19 22 22 18 20
85 - - - - - - 16 17 18 17 16 17
86 - - - - - - - - 19 19 - -
87 - - - - 21 21 15 16 19 19 - -
88 - - - - - - - 17 20 19 - 15
89 - - 17 17 - - - 17 26 27 16 -
90 - - 15 - - - - - 19 19 - -
91 - - 15 15 - - - 16 24 25 - -
92 - 15 21 22 - - 17 18 21 21 - -
93 15 - - - - - 17 21 18 19 - -
94 - - - - - 15 15 15 15 16 - 15
95 - - 19 19 15 16 - - 25 25 - -
96 - - 21 21 - - 17 16 28 28 17 19
97 - - 15 15 - - - - 22 25 - -
98 16 16 20 20 - - - - 28 28 - -
99 - - 20 20 - - - - 24 27 - -
100 - - 18 19 - - - - 16 17 - -
101 - - - - - - - - 25 26 - -
102 15 - - - - - - - 22 24 - -
103 - - 17 17 - - - - 20 20 - -
104 - - - - - - - - 20 21 - -
105 - - 15 15 - - - - 16 17 - -
106 - - 16 17 - - - - 26 27 - -
107 - - - - - - - - 24 24 - -
108 - - 17 18 - - - - 18 19 - -
109 - - 15 17 - - - - 19 19 - -
110 - - - - - - - - - - - -
111 - - - - - - - - 15 15 - -
112 - - - - - - - - 17 18 - -
113 - - - - - - - - - - - -
114 - - - - - - - - - - - -
115 - - - - - - - - - - - -
116 - - - - - - - - - - - -
117 - - - - - - - - - - - -
118 - - - - - - - - - - - -
119 - - - - - - - - - - - -
120 - - - - - - - - - - - -
121 - - - - - - - - - - - -
122 - - - - - - - - 16 16 - -
123 - - - - - - - - 16 16 - -
124 - - - - - - - - - - - -

N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S
______________________________________________________

b47
N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S

24 60 83 129 158 36 41 53 61 141 131 43 41
25 55 73 107 137 41 44 69 75 157 148 48 51
26 53 74 105 128 36 43 45 50 142 148 50 53
27 58 77 136 150 35 44 44 44 143 148 38 39
28 50 79 119 137 37 48 43 56 111 115 42 52
29 57 76 102 127 36 35 55 59 127 120 30 34
30 59 79 110 123 26 26 59 60 137 125 39 41
31 57 67 119 142 39 44 55 55 116 119 39 46
32 58 83 109 119 40 44 51 56 109 115 35 37
33 58 69 121 133 34 38 60 61 131 137 42 41
34 42 58 93 100 36 40 50 56 105 106 35 43
35 43 61 97 106 33 28 47 52 101 106 32 33
36 59 70 111 125 27 29 48 50 104 106 31 32
37 49 61 105 111 40 44 48 51 125 117 31 42
38 56 69 84 98 37 35 57 63 81 82 46 45
39 51 59 102 111 31 37 28 43 118 123 39 41
40 41 46 103 110 17 26 60 59 106 102 38 39
41 41 48 89 103 38 37 46 44 93 91 30 32
42 42 46 90 107 28 33 39 45 68 70 22 25
43 44 55 82 91 24 31 39 45 95 90 36 38
44 58 65 88 98 - 16 43 47 83 83 27 26
45 41 52 67 81 32 35 45 46 83 79 26 22
46 43 53 100 105 36 36 36 35 92 90 30 36
47 38 38 65 70 32 35 38 34 81 86 31 32
48 35 45 66 68 29 27 36 39 91 91 29 35
49 43 55 86 92 25 21 36 39 78 79 34 37
50 49 58 74 86 26 22 33 43 87 89 24 24
51 43 50 82 87 30 33 41 50 89 86 46 47
52 39 47 86 94 29 32 40 42 94 93 30 35
53 38 51 73 76 21 25 41 42 72 68 21 27
54 49 53 87 85 32 33 29 29 56 55 25 30
55 46 52 79 85 35 32 36 42 86 87 25 27
56 49 61 90 95 25 33 49 54 73 71 30 28
57 36 46 75 83 26 29 36 38 80 79 28 26
58 26 33 81 87 22 26 35 33 83 79 29 33
59 31 39 61 65 26 25 30 31 79 76 29 32
60 33 37 61 67 24 23 27 31 56 56 28 29
61 40 45 67 74 29 31 33 35 69 69 25 27
62 32 39 88 93 23 23 28 26 73 73 27 33
63 23 24 55 62 26 21 28 25 67 67 22 19
64 29 38 43 48 26 24 26 24 71 70 34 36
65 39 42 52 54 21 23 30 29 58 60 15 20
66 19 26 52 57 18 18 22 22 66 62 31 39
67 39 42 66 69 19 20 30 31 57 56 27 33
68 29 32 58 60 17 18 31 28 60 63 22 31
69 26 31 55 57 18 18 20 22 70 68 26 30
70 32 38 55 56 23 24 25 22 52 50 29 29
71 33 35 40 41 - 16 32 35 53 51 21 22
72 33 35 55 56 18 21 22 21 58 59 22 20
73 34 35 43 47 25 24 32 28 45 47 24 28
74 30 34 45 48 21 27 33 30 56 59 16 19
75 33 36 52 54 21 19 24 25 46 46 22 20
76 27 29 43 44 - 17 35 34 42 42 18 17
77 31 32 47 50 17 19 21 19 55 51 22 25
78 33 36 41 44 19 17 37 34 53 51 28 33
79 24 26 43 45 20 20 27 23 55 53 29 30
80 36 38 54 53 17 19 15 16 44 41 25 25
81 38 40 53 54 24 25 18 18 37 37 18 19
82 20 21 40 41 16 15 22 26 54 53 19 21
83 25 26 29 30 15 15 24 25 39 37 26 23
84 26 30 36 37 - 15 32 30 47 45 22 22
85 22 24 40 41 28 27 20 21 45 45 21 26
86 27 27 46 48 18 19 24 27 43 43 20 22
87 26 27 53 55 15 15 28 31 42 41 16 20
88 27 25 40 41 - - 19 17 50 48 20 21
89 27 27 41 42 16 - 24 25 34 34 15 15
90 33 33 55 58 15 - 16 15 41 40 - 16
91 25 29 50 50 15 15 17 18 29 29 - 16
92 26 26 36 37 21 21 18 20 47 48 - 15
93 27 28 31 32 - - 25 22 55 54 21 25
94 32 31 52 53 21 24 26 26 31 31 16 19
95 24 25 41 42 - 15 22 25 37 37 15 17
96 32 35 42 43 16 19 23 26 43 43 - 15
97 35 38 48 49 18 16 26 27 45 45 23 23
98 26 26 51 51 28 29 22 21 35 36 20 21
99 29 29 44 44 16 15 18 18 36 37 17 16
100 26 29 53 54 19 18 25 25 42 43 16 17
101 26 27 37 38 17 19 23 26 36 36 17 17
102 24 25 40 41 21 22 22 22 37 38 19 18
103 26 28 43 44 - - 19 18 41 40 19 18
104 30 32 40 40 15 - 21 19 34 34 18 20
105 24 27 38 38 - - 31 29 45 44 24 25
106 26 27 41 41 17 17 19 21 46 47 18 20
107 15 17 31 31 18 16 23 24 36 35 - -
108 22 25 41 41 - - 18 18 32 31 21 21
109 18 18 39 40 - - - - 32 32 20 21
110 21 21 38 38 - - 25 26 42 42 15 15
111 26 27 38 39 - - 21 18 36 36 16 17
112 - - 35 35 15 17 21 21 37 36 19 19
113 18 20 37 38 - - 22 22 31 30 - 15
114 22 22 27 27 - - 24 24 37 37 - -
115 23 23 29 30 - - 20 21 26 26 - -
116 20 20 27 28 17 17 26 27 29 30 - -
117 16 16 33 34 - - 25 25 36 36 15 16
118 17 19 31 31 - - 20 20 31 30 - -
119 17 17 26 26 - - 17 17 39 39 17 19
120 - - 25 25 - - - - 33 32 15 -
121 17 19 38 38 - - 19 19 43 44 15 18
122 20 21 34 35 - - - - 32 32 - -
123 18 17 28 29 - - 16 18 30 29 17 16
124 15 16 25 25 - - 22 22 34 34 - -

N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S
______________________________________________________

c1
N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S

24 69 85 211 248 121 129 120 122 240 247 63 78
25 69 83 244 262 106 117 88 102 192 211 75 94
26 80 91 243 281 123 133 81 86 227 232 70 76
27 84 101 216 243 90 104 102 114 205 231 77 80
28 137 165 220 251 91 108 100 94 158 154 111 125
29 121 153 205 240 97 113 97 102 139 153 114 123
30 109 141 203 230 89 84 103 122 159 167 114 121
31 125 139 187 200 89 87 91 99 154 168 114 123
32 117 140 190 216 82 84 103 102 154 167 117 120
33 97 117 200 230 87 88 107 103 177 178 98 99
34 103 122 211 235 91 100 99 112 188 205 88 97
35 89 111 202 231 85 95 98 113 166 170 77 87
36 102 118 190 206 85 99 112 119 204 211 86 89
37 94 110 204 214 84 84 92 103 203 221 86 88
38 149 160 148 153 111 111 107 117 262 269 98 98
39 125 140 166 186 93 105 87 91 221 222 104 115
40 128 157 156 181 109 114 108 118 239 248 108 120
41 122 146 157 180 97 96 86 108 257 267 99 107
42 110 138 182 190 80 85 88 93 224 238 110 116
43 136 150 158 161 70 83 84 86 216 233 118 118
44 95 108 182 206 86 90 62 66 147 151 90 97
45 92 104 187 199 85 92 72 73 158 162 81 92
46 92 100 183 195 96 97 65 83 139 143 89 89
47 89 106 184 204 91 95 78 85 173 177 86 105
48 109 118 202 211 74 84 71 78 138 149 83 92
49 91 103 176 194 74 82 62 76 139 152 78 85
50 96 118 178 193 87 96 76 69 138 143 82 77
51 88 96 161 167 65 70 89 96 164 172 77 84
52 69 77 141 156 70 67 79 96 161 168 62 72
53 92 100 117 127 64 64 109 104 135 148 79 78
54 79 89 143 147 56 61 90 100 147 152 92 83
55 92 96 144 149 53 63 103 108 171 167 86 77
56 82 94 152 159 71 73 86 97 134 129 82 90
57 81 89 137 146 61 66 84 91 128 135 76 80
58 76 84 132 142 66 67 86 89 151 155 47 46
59 64 73 116 128 83 84 64 71 172 169 56 61
60 69 76 136 141 89 84 73 69 159 165 61 64
61 77 92 136 143 73 80 76 81 169 170 67 72
62 58 61 142 147 77 74 70 69 149 154 50 51
63 64 65 116 126 74 72 69 80 153 160 56 54
64 48 63 124 130 76 76 66 72 140 145 43 39
65 58 61 152 156 77 82 69 69 159 158 48 48
66 86 95 171 181 61 69 75 84 133 133 85 86
67 77 83 166 167 55 50 79 81 172 170 81 87
68 76 85 163 172 65 74 98 103 131 130 74 81
69 90 84 159 161 67 76 78 88 130 138 70 66
70 89 100 156 164 73 68 79 91 121 124 55 64
71 82 88 160 160 60 62 97 94 120 125 75 80
72 71 78 153 155 63 67 84 86 124 132 79 86
73 70 75 146 148 51 49 84 94 137 139 57 65
74 71 73 123 127 59 54 70 85 134 138 83 84
75 57 65 137 140 48 51 55 61 116 121 69 75
76 67 72 133 135 51 56 72 68 126 124 72 76
77 53 57 122 132 48 57 69 76 102 102 58 59
78 50 55 121 127 60 64 71 71 104 109 53 60
79 62 65 111 114 52 58 83 84 92 93 58 61
80 58 65 131 132 53 57 63 68 114 115 80 76
81 60 65 130 133 59 63 68 73 120 120 62 62
82 65 63 129 131 58 62 74 71 91 94 78 79
83 68 72 131 135 62 59 69 76 117 114 58 57
84 59 62 117 123 65 67 58 58 113 115 59 63
85 76 79 117 114 73 74 61 66 126 128 65 69
86 84 90 122 126 64 66 72 75 94 93 60 63
87 98 102 136 138 65 67 42 42 105 106 58 63
88 80 80 131 134 61 63 38 40 117 119 61 60
89 72 72 116 119 67 62 54 55 118 118 48 49
90 78 78 132 137 52 56 47 49 99 100 55 61
91 70 73 119 122 54 56 64 66 117 117 60 65
92 73 78 147 150 57 61 67 71 118 121 53 57
93 56 61 120 123 57 55 60 61 112 111 57 58
94 74 75 124 127 57 63 60 57 109 109 61 66
95 68 72 146 146 70 73 47 48 112 111 42 42
96 70 71 129 131 60 61 37 38 101 104 49 54
97 62 68 89 92 60 62 48 47 92 92 59 58
98 65 65 119 119 47 50 63 62 116 116 47 47
99 73 72 128 129 44 48 58 61 111 110 58 56
100 56 62 106 105 56 58 52 55 100 99 57 59
101 59 62 96 97 46 52 57 60 100 101 56 61
102 82 83 98 100 48 48 60 67 91 90 44 42
103 61 58 112 114 49 54 71 74 82 81 48 49
104 71 75 94 92 42 40 57 60 90 92 58 60
105 63 68 103 103 54 59 59 59 82 82 52 50
106 61 65 104 105 46 48 49 48 87 88 50 50
107 55 57 104 104 45 48 48 47 90 87 49 52
108 52 55 95 95 48 49 50 58 88 89 47 47
109 65 65 111 111 57 59 67 68 74 75 56 60
110 61 62 72 74 47 47 65 69 93 94 45 48
111 55 59 81 81 46 51 41 48 85 87 50 53
112 50 53 84 83 56 55 49 53 80 81 52 52
113 49 47 84 85 44 44 44 46 65 64 39 37
114 54 58 79 78 32 34 51 53 80 80 52 51
115 53 54 87 89 41 41 48 55 74 74 48 48
116 51 53 81 80 36 38 47 50 75 77 52 54
117 51 50 78 80 45 46 49 47 74 76 54 51
118 32 35 75 76 42 44 51 55 83 85 43 43
119 41 42 87 88 40 40 38 38 80 81 45 46
120 50 52 95 95 27 28 36 36 72 72 52 54
121 43 45 76 77 37 36 55 56 76 76 45 44
122 44 44 74 76 45 46 50 54 69 70 43 43
123 48 47 75 75 44 44 39 40 67 69 48 51
124 43 44 84 85 34 32 48 55 70 71 57 60

N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S
______________________________________________________

c2
N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S

24 69 80 137 148 43 47 52 57 128 132 44 46
25 58 82 120 134 36 37 52 52 110 125 45 55
26 68 76 137 152 41 39 59 61 125 129 49 46
27 59 79 115 135 43 42 64 69 132 136 34 39
28 51 65 106 123 39 43 52 56 114 128 38 40
29 52 64 86 102 34 37 57 51 95 103 46 48
30 59 67 114 136 38 38 39 40 115 115 43 50
31 58 71 107 119 44 37 51 53 88 96 31 33
32 58 57 108 115 34 28 37 43 106 108 30 38
33 56 67 116 125 31 31 46 48 105 111 43 43
34 53 58 92 104 38 42 67 58 98 97 37 36
35 43 56 88 107 29 29 49 48 87 90 30 35
36 37 52 103 112 31 30 50 46 95 102 38 40
37 36 45 96 105 34 35 44 43 98 97 38 44
38 50 62 96 100 15 19 40 36 80 83 38 42
39 56 62 86 96 41 36 49 49 86 90 47 44
40 47 50 86 94 35 35 41 45 89 90 35 41
41 40 47 87 94 31 29 42 39 86 90 37 36
42 44 49 81 91 29 28 46 48 78 82 30 33
43 46 51 75 81 24 23 37 36 88 85 32 27
44 51 50 72 73 33 35 41 43 66 68 31 33
45 41 44 76 77 31 28 43 32 71 78 27 30
46 33 40 83 92 28 31 38 38 66 72 32 35
47 37 43 78 89 37 36 34 34 72 72 36 35
48 39 44 73 80 33 31 42 37 72 74 43 38
49 32 42 78 80 27 24 29 28 47 45 29 26
50 34 38 55 59 33 25 31 28 65 64 29 33
51 33 37 74 83 23 22 40 38 79 81 29 29
52 49 52 65 73 24 23 39 36 76 83 25 29
53 36 41 74 81 28 27 30 30 59 61 24 18
54 38 35 68 73 17 20 29 26 60 61 17 22
55 25 29 75 79 22 23 31 36 68 66 29 32
56 32 36 63 66 18 17 27 27 51 53 30 36
57 30 41 59 63 23 17 24 28 52 53 27 25
58 36 41 73 73 21 26 27 28 56 58 19 22
59 39 42 67 74 20 25 30 25 68 69 16 21
60 30 36 72 75 22 21 28 28 56 54 22 26
61 35 38 53 54 30 30 31 32 62 64 31 31
62 28 36 56 57 30 27 32 33 52 54 23 27
63 26 27 51 53 22 22 31 30 61 63 23 24
64 37 38 48 52 22 22 38 40 60 63 22 22
65 28 33 50 48 - - 39 40 54 55 16 19
66 38 42 52 55 21 18 27 25 47 48 29 31
67 33 40 57 60 22 22 34 33 60 64 27 28
68 30 33 51 52 27 27 37 33 56 54 25 25
69 23 25 54 53 19 20 38 37 44 47 26 27
70 27 28 47 47 20 22 34 31 55 54 15 -
71 - 16 47 50 18 24 32 35 66 69 18 15
72 35 38 40 44 22 20 26 27 45 45 21 23
73 27 30 46 48 24 26 27 28 47 45 26 27
74 32 35 48 49 19 20 27 28 49 47 27 29
75 29 32 54 58 15 16 31 31 55 54 24 21
76 35 36 44 44 17 19 26 26 44 43 19 17
77 24 27 47 51 20 19 20 19 48 46 20 21
78 19 24 57 60 - 15 25 25 47 49 23 22
79 22 24 39 40 16 16 26 25 44 43 17 15
80 31 34 30 33 21 26 27 29 45 46 28 27
81 21 23 50 54 16 16 25 24 37 38 17 18
82 30 33 41 42 - 16 26 24 37 37 15 15
83 23 25 36 37 - - 24 26 41 39 15 -
84 19 21 49 51 17 19 23 21 42 42 18 19
85 19 21 39 42 18 19 18 15 32 32 20 20
86 15 16 39 40 23 20 19 19 47 48 16 15
87 25 24 41 44 19 20 16 15 44 44 18 21
88 28 30 49 51 17 17 17 17 36 36 21 21
89 26 29 42 42 16 16 26 26 38 39 16 21
90 27 29 44 43 26 24 20 19 22 24 - -
91 28 28 42 43 - - 24 22 33 32 19 21
92 21 22 35 36 - - - - 44 43 19 21
93 16 17 32 32 16 - 20 17 30 30 - -
94 31 34 38 39 - - 23 24 43 43 18 21
95 20 21 43 43 19 18 20 20 39 40 19 16
96 21 23 33 33 - - 23 22 25 24 - -
97 22 21 35 37 - - - - 36 34 - -
98 19 18 34 34 - - 26 25 27 26 - -
99 18 20 38 37 15 16 20 20 35 35 18 19
100 17 16 29 29 16 17 17 17 35 34 21 21
101 - - 34 35 16 17 15 - 37 35 15 15
102 17 18 29 30 - - - - 34 31 18 19
103 19 19 35 36 - - 20 21 35 36 18 19
104 21 20 29 31 - - 19 20 38 37 17 20
105 20 21 39 40 - - 16 - 33 33 16 16
106 17 17 29 29 - - 22 23 34 34 - -
107 22 22 43 44 - - 22 21 27 27 - -
108 18 18 30 31 - - 17 17 32 32 - -
109 21 20 28 29 22 20 15 - 33 32 - -
110 17 18 28 27 - - 18 18 35 34 - 15
111 - - 32 32 - - - - 24 22 - -
112 21 21 31 31 - - 16 16 33 34 20 20
113 17 17 26 26 - - 18 17 39 39 - -
114 - - 23 22 15 - 16 16 27 28 20 19
115 19 18 35 36 - - - - 32 33 16 16
116 19 22 32 32 - - - - 20 20 - -
117 15 16 21 21 - - 19 20 32 32 17 18
118 20 20 25 25 - - 18 17 31 31 17 17
119 - - 27 28 - - - - 28 28 16 16
120 22 26 28 29 - - 15 15 27 28 - -
121 18 20 33 34 - - 15 - 23 23 - -
122 15 18 25 25 - - 16 15 26 26 - -
123 17 20 29 29 - - - - 30 30 19 20
124 - - 24 24 - - 15 - 18 18 - -

N N N N N N R R R R R R
1 2 3 1 2 3 1 2 3 1 2 3
C S C S C S C S C S C S
______________________________________________________
Date Subject Author
11/21/12
Read Interpretation of coefficients in multiple regressions which model
linear dependence on an IV
Halitsky
11/21/12
Read The problematic regression is actually ln(c) on ( ln(u), ln(u^2) ),
not c on (u, u^2)
Halitsky
11/22/12
Read Re: The problematic regression is actually ln(c) on ( ln(u), ln(u^2)
), not c on (u, u^2)
Ray Koopman
11/22/12
Read Off-line Zip File with one Summ File and 12 Detl files for lnc on (lnu,(lnu)^2)
Halitsky
11/23/12
Read Re: Off-line Zip File with one Summ File and 12 Detl files for lnc on (lnu,(lnu)^2)
Ray Koopman
11/23/12
Read Re: Off-line Zip File with one Summ File and 12 Detl files for lnc on (lnu,(lnu)^2)
Halitsky
11/23/12
Read Complete "a1_N_1_S" zipfile with results from all 3 new regressions
Halitsky
11/24/12
Read Re: Complete "a1_N_1_S" zipfile with results from all 3 new regressions
Ray Koopman
11/24/12
Read Re: Complete "a1_N_1_S" zipfile with results from all 3 new regressions
Halitsky
11/24/12
Read You now have N_1_S, N_2_S, and N_3_S files for all folds
Halitsky
11/25/12
Read As per your suggestion in the other thread, scaled e on scaled u, c, L
Halitsky
11/26/12
Read Re: As per your suggestion in the other thread, scaled e on scaled u,
c, L
Ray Koopman
11/26/12
Read Re: Interpretation of coefficients in multiple regressions which
model linear dependence on an IV
Ray Koopman
11/26/12
Read Them there is some neat algebraic mechanics !
Halitsky
11/27/12
Read Re: Them there is some neat algebraic mechanics !
Ray Koopman
11/27/12
Read OK – I think I’m set, at least till we get to c
on (e, u, u*e).
Halitsky
11/27/12
Read Re: OK – I think I’m set, at least till we get t
o c on (e, u, u*e).
Ray Koopman
11/28/12
Read Re: OK – I think I’m set, at least till we get t
o c on (e, u, u*e).
Ray Koopman
11/28/12
Read Thanks for your review of Tables I/II from previous analysis
Halitsky
11/27/12
Read Holy Cow! Look at your "average a1" slope regressed on Len Int
Halitsky
11/27/12
Read Re: Holy Cow! Look at your "average a1" slope regressed on Len Int
Ray Koopman
11/27/12
Read Re: Holy Cow! Look at your "average a1" slope regressed on Len Int
Halitsky
11/27/12
Read Re: Holy Cow! Look at your "average a1" slope regressed on Len Int
Ray Koopman
11/27/12
Read Here's how I did logs ...
Halitsky
11/27/12
Read Please note that $u = u in last post (the $ prefix is from PERL - sorry).
Halitsky
11/27/12
Read Re: Here's how I did logs ...
Ray Koopman
11/28/12
Read Average slopes and means of u' for c on (u',u'^2) WITHOUT logs
Halitsky
11/28/12
Read Results (!!) on average slopes and means for a1_N_1_C (complement
instead of core subset)
Halitsky
11/28/12
Read Re: Results (!!) on average slopes and means for a1_N_1_C (complement
instead of core subset)
Ray Koopman
11/28/12
Read Finally! Pay-off for all that work I did with the "A" matrix returned
by Ivor Welch's module!
Halitsky
11/29/12
Read Average Slope SEs for a1_N_1_S and a1_N_1_C (and some questions
regarding them ...)
Halitsky
11/30/12
Read Re: Average Slope SEs for a1_N_1_S and a1_N_1_C (and some questions
regarding them ...)
Ray Koopman
12/2/12
Read Re: Average Slope SEs for a1_N_1_S and a1_N_1_C (and some questions
regarding them ...)
Ray Koopman
12/2/12
Read Re: Average Slope SEs for a1_N_1_S and a1_N_1_C (and some questions
regarding them ...)
Ray Koopman
12/2/12
Read Glad you brought up “singleton” length intervals
... been thinkin’ on ‘em also ...
Halitsky
12/2/12
Read Re: Glad you brought up “singleton” length inter
vals ... been thinkin’ on ‘em also ...
Ray Koopman
12/2/12
Read It's still 24...124 - don't know why I bothered to say "roughly
25...125" instead of "exactly "24...124"
Halitsky
12/2/12
Read You should probably clear your data deck and start fresh with the two
csv's I just mentioned in the last email
Halitsky
12/2/12
Read Re: Glad you brought up “singleton” length inter
vals ... been thinkin’ on ‘em also ...
Halitsky
12/2/12
Read One last thought: definitions for the third regression (will save a
complete re-run if I incorporate them now) ...
Halitsky
12/3/12
Read Number of Bonferroni entries for each singleton length is still 72 (duh!)
Halitsky
11/30/12
Read En passant question: What if a plot of slope CI’s
is lousy, but splits the “m’s” perfectly?
Halitsky
11/30/12
Read Re: En passant question: What if a plot of slope CI
’s is lousy, but splits the “m’s” perfectly?
Ray Koopman
12/1/12
Read I’m glad the perfect m split legitimately suggests
a subset effect; here’s why.
Halitsky
12/1/12
Read Re: I’m glad the perfect m split legitimately sugg
ests a subset effect; here’s why.
Ray Koopman
12/1/12
Read Re: I’m glad the perfect m split legitimately sugg
ests a subset effect; here’s why.
Halitsky
12/1/12
Read Slope and intercept for R'uq in the above example ...
Halitsky
12/1/12
Read Bonferroni tables for p’s from new 2-ways for Auq
per fold and length interval
Halitsky
12/1/12
Read Nope! 24-entry Bonferroni tables for (a1,a3) and (b1,b47) do NOT
improve results for a3 nor b47
Halitsky
12/5/12
Read I'm VERY glad you'll know how to answer this "perms and combs"
question !
Halitsky
12/5/12
Read “L-H Het” Table for Average Slopes Auq, Aubu, Au
bqu
Halitsky
12/5/12
Read In "L-H Het table", L-H Het for N1 Aubu should be 4, NOT 2
Halitsky
12/5/12
Read Holy Moly, were you right about covariances for Rub and Rubq !!!!
Halitsky
12/5/12
Read Re: Holy Moly, were you right about covariances for Rub and Rubq !!!!
Ray Koopman
12/6/12
Read So do we need to "Bonferroni-correct" in this case
Halitsky
12/7/12
Read Re: So do we need to "Bonferroni-correct" in this case
Ray Koopman
12/7/12
Read Response to your last of 12/7 at 12:17am
Halitsky
12/7/12
Read Re: Response to your last of 12/7 at 12:17am
Ray Koopman
12/7/12
Read Thanks for the guidance on how to evaluate the contribution of u^2 in
the second model.
Halitsky
12/7/12
Read Please ignore my first question about "estimated standard errpr" in
my last post !!!! Sorry !
Halitsky
12/7/12
Read The u^2 coefficient in c on (e,u,u*e,u^2) does NOT distinguish among
the four subset x MoSS roll-ups
Halitsky
12/7/12
Read Sorry! Those were the SE's in my last post, not the t's !
Halitsky
12/7/12
Read SE's and p's for four subset x MoSS roll-ups of u*e coefficient in c
= (u,e,u*e)
Halitsky
12/7/12
Read Re: SE's and p's for four subset x MoSS roll-ups of u*e coefficient
in c = (u,e,u*e)
Ray Koopman
12/7/12
Read I'm sorry Ray - excitement (probably unwarranted) has disconnected my
brain from my fingers ...
Halitsky
12/7/12
Read Must we say S,N instead of N,S if we've said "Subset x MoSS" (not
MoSS x Subset) ???
Halitsky
12/7/12
Read Re: Must we say S,N instead of N,S if we've said "Subset x MoSS" (not
MoSS x Subset) ???
Ray Koopman
12/7/12
Read Response to your last
Halitsky
12/8/12
Read Re: Response to your last
Ray Koopman
12/8/12
Read Re: Response to your last
Ray Koopman
12/8/12
Read I think I understand; if so, then here’s what I ex
pect you’ll agree I should do next
Halitsky
12/9/12
Read Thanks so much for the sample picture you sent off-line
Halitsky
12/8/12
Read One other thing - because we're using "c-average", not "c-simple",
"c" is no longer a pure count
Halitsky
12/8/12
Read One other possibly worthwhile observation regarding the term u*e in
the regression c on (e,u,u^e,u^2)
Halitsky
12/8/12
Read Typo's of u^e for u*e in previous post.
Halitsky
12/9/12
Read Could I impose on you for four more ordered p “ref
erence plots”?
Halitsky
12/9/12
Read Have sent off-line a PDF of the four plots themselves graphed all together.
gimpeltf@hotmail.com
12/9/12
Read I'm getting the hang of the plotting now - see PDF SNa1_1_for_Rubq
sent offline
Halitsky
12/9/12
Read Am resending the last PDF sent off-line, since I've now learned how
to highlight the line of interest against the random backdrop.
Halitsky
12/10/12
Read Re: Am resending the last PDF sent off-line, since I've now learned
how to highlight the line of interest against the random backdrop.
Ray Koopman
12/10/12
Read 1) Just u*e and u^2(!!); 2) IOTs vs “proper” tes
ts
Halitsky
12/10/12
Read Re: 1) Just u*e and u^2(!!); 2) IOTs vs “proper”
tests
Ray Koopman
12/10/12
Read Response to your last re Q and p
Halitsky
12/10/12
Read Sorry! I meant set=2, not set =1 in last post ...
Halitsky
12/11/12
Read Re: Response to your last re Q and p
Ray Koopman
12/11/12
Read 1) yes - I am using abs(t); 2) subtraction from 1
Halitsky
12/10/12
Read Results of p's obtained by referring Q’s to the ch
i-square distribution.
Halitsky
12/11/12
Read Correction to harmless "thought-typo" in last post
Halitsky
12/11/12
Read Another way to bring the other folds in might be via investigation of
your average slopes and covar vis a vis "hetness"
Halitsky
12/11/12
Read Re: Results of p's obtained by referring Q’s to th
e chi-square distribution.
Ray Koopman
12/11/12
Read OK then, how ‘bout “hetness”? Are you amenabl
e to its further investigation?
Halitsky
12/12/12
Read Re: OK then, how ‘bout “hetness”? Are you amen
able to its further investigation?
Ray Koopman
12/12/12
Read I need to correct an apparent miscommunication regar
ding derivation of het H’s and L’s
Halitsky
12/13/12
Read Re: I need to correct an apparent miscommunication r
egarding derivation of het H’s and L’s
Ray Koopman
12/13/12
Read The SE's are in the zipped files but here they are for your
convenience ....
Halitsky
12/13/12
Read Re: The SE's are in the zipped files but here they are for your
convenience ....
Ray Koopman
12/13/12
Read Re your question about "linearity of SE’s in lengt
h"
Halitsky
12/14/12
Read Re: Re your question about "linearity of SE’s in l
ength"
Ray Koopman
12/14/12
Read Your question re features of (L,Aubqe) plots
Halitsky
12/13/12
Read I think I may have found something relevant to Aubqe
“het-ness” and heteroscedasticity
Halitsky
12/13/12
Read Re: I think I may have found something relevant to A
ubqe “het-ness” and heteroscedasticity
Ray Koopman
12/14/12
Read Re your questions about the plots sent off-line (and the underlying
data posted here 12/13 at 10:33am)
Halitsky
12/14/12
Read Re: Re your questions about the plots sent off-line (and the
underlying data posted here 12/13 at 10:33am)
Ray Koopman
12/14/12
Read Thanks for the terminological/methodological corrections, and also
for the ref to gnuplot.
Halitsky
12/14/12
Read Re: Thanks for the terminological/methodological corrections, and
also for the ref to gnuplot.
Ray Koopman
12/14/12
Read Response to your last of 12/14 at 227pm re terminology and methodology.
Halitsky
12/14/12
Read Re linearity of the Axxxx SE plots – hold on to yo
ur hat
Halitsky
12/14/12
Read Re: Re linearity of the Axxxx SE plots – hold on t
o your hat
Ray Koopman
12/14/12
Read Thanks for doing those two plots - yes - we agree on what we're seeing
Halitsky
12/14/12
Read Re: Thanks for doing those two plots - yes - we agree on what we're seeing
Ray Koopman
12/15/12
Read Re: Thanks for doing those two plots - yes - we agree on what we're seeing
Ray Koopman
12/15/12
Read Re plot of SEP against L
Halitsky
12/15/12
Read Effect of multiplying SE by sqrt(N), as per your post of 12/14 at 10:34pm
Halitsky
12/15/12
Read Re: Effect of multiplying SE by sqrt(N), as per your post of 12/14 at 10:34pm
Ray Koopman
12/14/12
Read One other general question regarding scaling to [0,1].
Halitsky
12/14/12
Read Re: One other general question regarding scaling to [0,1].
Ray Koopman
12/14/12
Read Sorry - I will be typographically more careful re Aubqe in the future.
Halitsky
12/1/12
Read Re: Interpretation of coefficients in multiple regressions which
model linear dependence on an IV
Ray Koopman
12/1/12
Read Thanks for elucidation of 2nd new regression.
Halitsky
12/1/12
Read Re: Interpretation of coefficients in multiple regressions which
model linear dependence on an IV
Ray Koopman
12/1/12
Read Roger corrected defs; also, will add new cov, just in case it's
needed later
Halitsky
12/2/12
Read Re: Interpretation of coefficients in multiple regressions which
model linear dependence on an IV
Ray Koopman
12/2/12
Read 1) thanks for the 3rd regression defs; 2) Yes - I see why the terms
aren't "symmetrical" in this case.
Halitsky
12/3/12
Read New copies of a1_N_1_C and a1_N_1_S with data for all three
regressions at each singleton length.
Halitsky
12/3/12
Read Since 3rd regression computation needs df = 5, am requiring 15
observations for any given length singleton in any cell
Halitsky
12/3/12
Read Have sent off-line all N_1 regression coefficient files and master N
per length index file for N1
Halitsky
12/3/12
Read Same as above post for f_N_2_ss
Halitsky
12/3/12
Read Same as above post for f_N_3_ss
Halitsky
12/3/12
Read Same as above post for f_R_1_ss
Halitsky
12/3/12
Read Same as above post for f_R_2_ss
Halitsky
12/3/12
Read Same as above post for f_R_3_ss
Halitsky
12/3/12
Read Re: Since 3rd regression computation needs df = 5, am requiring 15
observations for any given length singleton in any cell
Ray Koopman
12/4/12
Read Sparseness of b1 data ...
Halitsky
12/4/12
Read I realized I should clarify my 4-way b1 match table: it's AFTER
subtracting df of 3
Halitsky
12/4/12
Read Re: I realized I should clarify my 4-way b1 match table: it's AFTER
subtracting df of 3
Ray Koopman
12/4/12
Read No - the counts in the files themselves are all OK.
Halitsky
12/4/12
Read Re: Sparseness of b1 data ...
Ray Koopman
12/5/12
Read We cross posted, so I just saw your revised "counts" table after I
made my last two posts ...
Halitsky
12/4/12
Read Let me know if you're ready for some interesting data, or if you're
too busy analyzing
Halitsky
12/4/12
Read Re: Let me know if you're ready for some interesting data, or if
you're too busy analyzing
Ray Koopman
12/4/12
Read Please evaluate this "yield" table of method/subset avg slope 2-ways
per fold and len with p < .05
Halitsky
12/5/12
Read One other question about using Auq avg slope as a constant when
computing the other two regressions
Halitsky
12/5/12
Read Re: One other question about using Auq avg slope as a constant when
computing the other two regressions
Ray Koopman
12/5/12
Read Re: One other question about using Auq avg slope as a constant when
computing the other two regressions
Halitsky
12/4/12
Read Some of your counts apparently ARE off.
Halitsky
12/4/12
Read Sorry! those counts in my last post were for len 63 in b1 (forgot to
tell you the length!!!!)
Halitsky
12/4/12
Read Re: Since 3rd regression computation needs df = 5, am requiring 15
observations for any given length singleton in any cell
Ray Koopman

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