Bidirectional Mean Averaging and The Wave MatrixPrev Topic Next Topic

JADELottery

Native American Eagle Sun

MN
United States
Member #21
December 7, 2001
4,811 Posts
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Dec 10, 2006, 7:45 am

Bidirectional Mean Averaging and The Wave Matrix
1 - Oscillation Data Set
      A = {X₁, X₂, X₃, ... , X_n-2, X_n-1, X_n}
      A - oscillation data set is any set of X values oscillating on the x-axis.
         oscillating X values can be obtained by removing any regression components.
         possible regression components are:
            constant, linear, exponential, logarithmic, power, geometric, polynomial, etc.
2 - Up Mean Averaging
      U₁ = X₁
      U₂ = (U₁ + X₂ e^d) / (1 + e^d)
      U₃ = (U₂ + X₃ e^d) / (1 + e^d)
      ...
      U_n-2 = (U_n-3 + X_n-2 e^d) / (1 + e^d)
      U_n-1 = (U_n-2 + X_n-1 e^d) / (1 + e^d)
      U_n   = (U_n-1 + X_n e^d) / (1 + e^d)
      Up Mean Averaging Set - U = {U₁, U₂, U₃, ... , U_n-2, U_n-1, U_n}
3 - Down Mean Averaging
      D_n   = X_n
      D_n-1 = (D_n + X_n-1 e^d) / (1 + e^d)
      D_n-2 = (D_n-1 + X_n-2 e^d) / (1 + e^d)
      ...
      D₃ = (D₄ + X₃ e^d) / (1 + e^d)
      D₂ = (D₃ + X₂ e^d) / (1 + e^d)
      D₁ = (D₂ + X₁ e^d) / (1 + e^d)
      Down Mean Averaging Set - D = {D₁, D₂, D₃, ... , D_n-2, D_n-1, D_n}
4 - Bidirectional Mean Averaging
       B = (U + D) / 2

                  ¯

      Y₁ = (U₁ + D₁) / 2
      Y₂ = (U₂ + D₂) / 2
      Y₃ = (U₃ + D₃) / 2
      ...
      Y_n-2 = (U_n-2 + D_n-2) / 2
      Y_n-1 = (U_n-1 + D_n-1) / 2
      Y_n   = (U_n + D_n) / 2
      Bidirectional Mean Averaging Set - B_ma(A,d) = B = {Y₁, Y₂, Y₃, ... , Y_n-2, Y_n-1, Y_n}
          A - oscillation data set.
          d - degree of weighting data.
                -¥ £ d £ +¥, d is any real number.
                lim d ® +¥, B_ma(A,d) = B = A
                lim d ® -¥, B_ma(A,d) = B = (X₁ + X_n ) / 2
   5 - Iteration of Bidirectional Mean Averaging
      B₁ = B_ma(A,d)
   B₂ = B_ma(B₁,d)
   B₃ = B_ma(B₂,d)
   ...
   B_i-2 = B_ma(B_i-3,d)
   B_i-1 = B_ma(B_i-2,d)
   B_i   = B_ma(B_i-1,d)
      Iteration of Bidirectional Mean Averaging - I_bma(A,d,i) = B_i
          A - oscillation data set.
          d - degree of weighting.
         i - iteration of averaging.
               i ³ 1, i is any positive integer.
6 - I_bma as Wave Data Set and Remainder Set of Wave Data Set
       Wave Data Set -   W = I_bma(A,d,i)
      R = A - W

            ¯

      R₁ = X₁ - Y₁
      R₂ = X₂ - Y₂
      R₃ = X₃ - Y₃
      ...
      R_n-2 = X_n-2 - Y_n-2
      R_n-1 = X_n-1 - Y_n-1
      R_n = X_n - Y_n
      R = {R₁, R₂, R₃, ... , R_n-2, R_n-1, R_n}
      Remainder Set of Wave Data Set -   R
7 - Iteration of Remainder Set, the Wave Matrix and Remainder Set of the Wave Matrix
      W₁ = I_bma(A,d,i) ,    R₁ = A - W₁       W₂ = I_bma(R₁,d,i) , R₂ = R₁ - W₂
      W₃ = I_bma(R₂,d,i) , R₃ = R₂ - W₃
      ...
      W_j-2 = I_bma(R_j-3,d,i) , R_j-2 = R_j-3 - W_j-2
      W_j-1 = I_bma(R_j-2,d,i) , R_j-1 = R_j-2 - W_j-1
      W_j   = I_bma(R_j-1,d,i) , R_j = R_j-1 - W_j
      The Wave Matrix -   W_m(A,d,i,j) = {W₁, W₂, W₃, ... , W_j-2, W_j-1, W_j}
      Remainder Set of the Wave Matrix -   R_m(A,d,i,j) = R_j
         A - oscillation data set.
               A = W₁ + W₂ + W₃ + ... + W_j-2 + W_j-1 + W_j + R_j
         d - degree of weighting.
          i - iteration of averaging.
          j - iteration of the wave data set.
            j ³ 1, j is any positive integer.
            lim j ® +¥, R_j = 0
8 - Determining d Values in the Wave Data Set through Root Mean Square (RMS) Equivalence and Degree of Weighting Set
      RMS of Wave Data Set -   _rmsW = Ö(S (W)²) / n
                                             = Ö(S I_bma(A,d,i)²) / n
                                             = [a = 1 to a = n] Ö(S (Y_a)²) / n
      RMS of Remainder Data Set -   _rmsR = Ö(S (R)²) / n
                                                   = Ö(S (A - W)²) / n
                                                   = Ö(S (A - I_bma(A,d,i))²) / n
                                                   =   [a = 1 to a = n] Ö(S (R_a)²) / n
      Determined d Value -   d à _rmsW = _rmsR
                                    Read as: d is determined when _rmsW is equal to _rmsR.
                                    the value of d is found through a feedback root find algorithm.
      W₁ = I_bma(A,d₁,i) ,    R₁ = A - W₁ ,   d₁ à _rmsW₁ = _rmsR₁       W₂ = I_bma(R₁,d₂,i) , R₂ = R₁ - W₂ ,   d₂ à _rmsW₂ = _rmsR₂
      W₃ = I_bma(R₂,d₃,i) , R₃ = R₂ - W₃ ,   d₃ à _rmsW₃ = _rmsR₃
      ...
      W_j-2 = I_bma(R_j-3,d_j-2,i) , R_j-2 = R_j-3 - W_j-2 ,   d_j-2 à _rmsW_j-2 = _rmsR_j-2
      W_j-1 = I_bma(R_j-2,d_j-1,i) , R_j-1 = R_j-2 - W_j-1 ,   d_j-1 à _rmsW_j-1 = _rmsR_j-1
      W_j = I_bma(R_j-1,d_j,i) ,       R_j = R_j-1 - W_j ,    d_j à _rmsW_j = _rmsR_j
      Degree of Weighting Data Set -   d = {d₁, d₂, d₃, ... , d_j-2, d_j-1, d_j}
      RMS Adjusted Wave Matrix -   W_m(A,d,i,j) = {W₁, W₂, W₃, ... , W_j-2, W_j-1, W_j}
      RMS Adjusted Remainder Set of the Wave Matrix -   R_m(A,d,i,j) = R_j

The One Over None
I Know...

Native American Eagle Sun

MN
United States
Member #21
December 7, 2001
4,811 Posts
Offline

Dec 10, 2006, 7:46 am

Here's a table of Draw Occurrence, Linear Regression of Occurrence, Oscillation Data, Wave Matrix, and Remainder Data for WI Lottery of Ball #1 for 1500 Draws:

Index n	Ball #01 D_n - Draw Occurrence	Linear Regression L_n = m * n + b m = 8.29, b =40.70	Oscillation Data - A {D_n - L_n}	Wave Data - W₁ d₁ = -0.87 i = 32	Wave Data - W₂ d₂ = 0.73 i = 32	Wave Data - W₃ d₃ = 1.89 i = 32	Wave Data - W₄ d₄ = 3.06 i = 32	Wave Data - W₅ d₅ = 3.83 i = 32	Remainder - R₅
1	16	48.98	-32.98	-23.92	-0.97	1.81	-3.29	-3.74	-2.87
2	41	57.27	-16.27	-23.85	-1.11	2.41	1.27	2.09	2.92
3	51	65.56	-14.56	-23.76	-1.33	2.92	3.6	2.65	1.36
4	53	73.84	-20.84	-23.65	-1.62	2.81	2.83	-0.02	-1.19
5	59	82.13	-23.13	-23.52	-1.96	1.94	1.95	-0.52	-1.02
6	69	90.42	-21.42	-23.36	-2.31	0.44	1.6	1.01	1.2
7	72	98.7	-26.7	-23.19	-2.63	-1.42	0.15	0.56	-0.18
8	76	106.99	-30.99	-22.99	-2.91	-3.19	-2.01	0.07	0.04
9	81	115.28	-34.28	-22.77	-3.1	-4.32	-4.24	-0.71	0.86
10	83	123.56	-40.56	-22.52	-3.22	-4.32	-5.53	-2.43	-2.53
11	102	131.85	-29.85	-22.25	-3.26	-3.02	-4.1	-0.3	3.07
12	106	140.14	-34.14	-21.95	-3.28	-0.67	-1.98	-2.19	-4.07
13	133	148.42	-15.42	-21.63	-3.3	2.04	2.4	2.14	2.93
14	143	156.71	-13.71	-21.28	-3.39	4.31	4.45	1.75	0.45
15	149	165	-16	-20.91	-3.6	5.54	4.43	0.04	-1.5
16	160	173.28	-13.28	-20.51	-3.94	5.51	4.19	0.92	0.54
17	166	181.57	-15.57	-20.08	-4.44	4.35	2.9	0.98	0.72
18	167	189.86	-22.86	-19.62	-5.08	2.44	0.48	-0.32	-0.76
19	172	198.15	-26.15	-19.14	-5.83	0.34	-1.62	-0.52	0.62
20	174	206.43	-32.43	-18.63	-6.67	-1.39	-3.01	-1.25	-1.47
21	185	214.72	-29.72	-18.09	-7.57	-2.36	-3.03	0.46	0.86
22	193	223.01	-30.01	-17.52	-8.51	-2.33	-3.53	0.46	1.42
23	196	231.29	-35.29	-16.92	-9.48	-1.22	-4.44	-2.28	-0.94
24	206	239.58	-33.58	-16.3	-10.51	0.83	-2.65	-2.95	-2
25	227	247.87	-20.87	-15.65	-11.6	3.19	2.53	0.62	0.04
26	247	256.15	-9.15	-14.97	-12.76	4.88	6.97	3.85	2.88
27	249	264.44	-15.44	-14.27	-13.99	5.05	7	1.59	-0.82
28	252	272.73	-20.73	-13.55	-15.24	3.46	4.98	0.19	-0.57
29	255	281.01	-26.01	-12.8	-16.44	0.42	2.66	0.14	0.01
30	257	289.3	-32.3	-12.03	-17.5	-3.43	0.18	0.15	0.33
31	258	297.59	-39.59	-11.24	-18.28	-7.35	-2.45	-0.55	0.29
32	259	305.87	-46.87	-10.44	-18.67	-10.64	-4.38	-1.58	-1.17
33	269	314.16	-45.16	-9.61	-18.54	-12.85	-4.5	-0.67	1.01
34	276	322.45	-46.45	-8.77	-17.79	-13.87	-3.7	-0.98	-1.33
35	293	330.73	-37.73	-7.92	-16.37	-13.89	-1.82	0.58	1.68
36	301	339.02	-38.02	-7.06	-14.25	-13.25	-0.48	0.14	-3.13
37	326	347.31	-21.31	-6.19	-11.47	-12.18	0.36	4.03	4.14
38	329	355.59	-26.59	-5.31	-8.1	-10.57	-3.33	0.58	0.14
39	335	363.88	-28.88	-4.43	-4.29	-7.85	-7.69	-4.19	-0.43
40	345	372.17	-27.17	-3.55	-0.18	-3.59	-6.98	-6.12	-6.74
41	395	380.46	14.54	-2.67	4.02	1.87	0.55	3.19	7.59
42	404	388.74	15.26	-1.79	8.11	7.38	4.96	0.91	-4.3
43	435	397.03	37.97	-0.92	11.9	11.78	7.82	3.64	3.76
44	440	405.32	34.68	-0.06	15.21	14.39	6.58	0.78	-2.22
45	454	413.6	40.4	0.79	17.9	15.24	4.18	0.67	1.61
46	457	421.89	35.11	1.63	19.89	14.81	1.12	-1.74	-0.61
47	462	430.18	31.82	2.46	21.14	13.67	0.13	-2.44	-3.13
48	483	438.46	44.54	3.27	21.65	12.12	1.8	2.11	3.59
49	484	446.75	37.25	4.06	21.47	10.23	1.27	0.68	-0.45
50	487	455.04	31.96	4.83	20.68	8.1	-0.14	-0.74	-0.76
51	493	463.32	29.68	5.58	19.37	5.92	-0.74	-0.58	0.13
52	498	471.61	26.39	6.31	17.65	3.84	-0.61	-0.26	-0.54
53	506	479.9	26.1	7.02	15.64	1.93	-0.22	0.83	0.91
54	509	488.18	20.82	7.7	13.44	0.22	-0.76	0.26	-0.05
55	513	496.47	16.53	8.36	11.17	-1.2	-1.61	-0.59	0.41
56	515	504.76	10.24	8.99	8.92	-2.27	-1.7	-1.62	-2.07
57	529	513.04	15.96	9.6	6.77	-3.04	-0.01	0.74	1.9
58	533	521.33	11.67	10.18	4.82	-3.77	0.96	0.23	-0.75
59	541	529.62	11.38	10.73	3.13	-4.71	1.58	0.62	0.02
60	548	537.9	10.1	11.26	1.77	-5.98	1.43	1	0.61
61	551	546.19	4.81	11.77	0.79	-7.49	0.12	0.4	-0.78
62	558	554.48	3.52	12.25	0.22	-8.87	-1.76	0.87	0.82
63	562	562.76	-0.76	12.7	0.08	-9.55	-4.63	-0.37	1
64	564	571.05	-7.05	13.14	0.35	-8.98	-6.93	-3.42	-1.21
65	576	579.34	-3.34	13.55	1	-6.95	-5.16	-3.65	-2.13
66	601	587.63	13.37	13.94	1.96	-3.96	0.85	0.8	-0.21
67	627	595.91	31.09	14.31	3.13	-1.03	5.86	5.2	3.63
68	631	604.2	26.8	14.66	4.41	1.01	4.88	2.65	-0.81
69	637	612.49	24.51	14.99	5.7	2.21	0.54	0.25	0.82
70	638	620.77	17.23	15.3	6.87	3.24	-4.01	-3.25	-0.93
71	646	629.06	16.94	15.61	7.83	4.83	-4.66	-4.34	-2.32
72	668	637.35	30.65	15.89	8.47	7.03	-0.23	-0.5	0
73	690	645.63	44.37	16.17	8.72	9.17	4.81	3.4	2.09
74	699	653.92	45.08	16.43	8.52	10.42	5.9	2.83	0.98
75	700	662.21	37.79	16.68	7.85	10.4	3.63	-0.21	-0.56
76	701	670.49	30.51	16.93	6.71	9.26	1.54	-1.82	-2.12
77	712	678.78	33.22	17.16	5.16	7.37	1.46	0.47	1.59
78	714	687.07	26.93	17.39	3.26	5	0.98	0.29	0.01
79	716	695.35	20.65	17.61	1.12	2.36	0.14	-0.3	-0.29
80	719	703.64	15.36	17.83	-1.15	-0.37	-0.36	-0.24	-0.35
81	724	711.93	12.07	18.04	-3.4	-3.01	-0.6	0.41	0.64
82	726	720.21	5.79	18.25	-5.53	-5.41	-1.28	-0.02	-0.23
83	730	728.5	1.5	18.46	-7.41	-7.39	-1.96	-0.17	-0.03
84	735	736.79	-1.79	18.66	-8.96	-8.79	-2.43	-0.19	-0.09
85	742	745.07	-3.07	18.86	-10.1	-9.46	-2.67	-0.16	0.45
86	748	753.36	-5.36	19.06	-10.8	-9.35	-2.68	-0.69	-0.9
87	760	761.65	-1.65	19.25	-11.07	-8.47	-1.94	0.31	0.27
88	772	769.93	2.07	19.44	-10.92	-6.95	-1.56	0.62	1.43
89	778	778.22	-0.22	19.63	-10.42	-4.9	-1.77	-1.59	-1.17
90	791	786.51	4.49	19.81	-9.62	-2.58	-0.17	-1.44	-1.51
91	812	794.8	17.2	19.99	-8.61	-0.43	3.03	1.56	1.65
92	823	803.08	19.92	20.16	-7.44	1.03	4.5	1.8	-0.13
93	833	811.37	21.63	20.32	-6.18	1.48	3.73	1.52	0.75
94	837	819.66	17.34	20.47	-4.86	1.08	1.18	-0.16	-0.37
95	843	827.94	15.06	20.62	-3.51	0.28	-1.21	-1.37	0.25
96	848	836.23	11.77	20.75	-2.12	-0.47	-1.91	-2.26	-2.22
97	866	844.52	21.48	20.86	-0.69	-1	-0.06	0.68	1.7
98	875	852.8	22.2	20.96	0.81	-1.48	1.13	1.01	-0.24
99	885	861.09	23.91	21.05	2.38	-2.07	1.07	1.17	0.31
100	893	869.38	23.62	21.11	4.06	-2.75	-0.4	0.41	1.18
101	894	877.66	16.34	21.16	5.82	-3.24	-2.25	-1.73	-3.42
102	914	885.95	28.05	21.18	7.66	-3.18	-2.34	1.53	3.2
103	918	894.24	23.76	21.18	9.52	-2.25	-3.97	-0.43	-0.28
104	927	902.52	24.48	21.15	11.35	-0.23	-5.08	-2.74	0.03
105	937	910.81	26.19	21.09	13.07	2.79	-2.84	-3.73	-4.2
106	970	919.1	50.9	21.01	14.58	6.15	3.53	2.7	2.94
107	986	927.38	58.62	20.89	15.82	8.8	6.9	3.9	2.3
108	987	935.67	51.33	20.74	16.73	10	5.74	0.3	-2.17
109	996	943.96	52.04	20.55	17.26	9.63	3.97	0.03	0.6
110	999	952.24	46.76	20.33	17.42	8.02	2.23	-0.54	-0.7
111	1005	960.53	44.47	20.08	17.26	5.59	1.1	0	0.44
112	1008	968.82	39.18	19.78	16.85	2.78	0	-0.05	-0.18
113	1012	977.11	34.89	19.45	16.26	-0.04	-1.05	0.08	0.19
114	1015	985.39	29.61	19.07	15.61	-2.5	-2.18	-0.04	-0.36
115	1021	993.68	27.32	18.66	14.97	-4.27	-3.31	0.12	1.15
116	1023	1001.97	21.03	18.21	14.41	-5.07	-4.45	-1.74	-0.32
117	1028	1010.25	17.75	17.72	13.98	-4.82	-3.5	-2.31	-3.32
118	1052	1018.54	33.46	17.19	13.69	-3.78	-0.07	2.59	3.84
119	1056	1026.83	29.17	16.62	13.53	-2.37	0.58	1.02	-0.2
120	1061	1035.11	25.89	16.01	13.46	-0.91	-0.03	-0.77	-1.86
121	1075	1043.4	31.6	15.36	13.42	0.55	0.09	0.64	1.54
122	1081	1051.69	29.31	14.67	13.35	2.01	-0.4	-0.42	0.1
123	1087	1059.97	27.03	13.95	13.17	3.48	-0.36	-1.62	-1.59
124	1101	1068.26	32.74	13.2	12.83	4.8	1.49	0.2	0.22
125	1113	1076.55	36.45	12.41	12.28	5.69	3.11	1.7	1.26
126	1117	1084.83	32.17	11.58	11.48	5.9	2.79	0.72	-0.3
127	1121	1093.12	27.88	10.73	10.42	5.42	1.54	-0.03	-0.21
128	1125	1101.41	23.59	9.85	9.12	4.47	0.31	-0.35	0.2
129	1128	1109.69	18.31	8.95	7.61	3.31	-0.47	-0.93	-0.15
130	1132	1117.98	14.02	8.02	5.95	2.08	-0.12	-0.95	-0.96
131	1141	1126.27	14.73	7.06	4.21	0.74	1.3	0.77	0.65
132	1146	1134.55	11.45	6.09	2.46	-0.88	1.84	1.32	0.61
133	1147	1142.84	4.16	5.1	0.8	-2.82	0.82	0.51	-0.26
134	1149	1151.13	-2.13	4.09	-0.69	-4.87	-0.85	-0.07	0.26
135	1150	1159.41	-9.41	3.07	-1.94	-6.67	-2.42	-0.89	-0.56
136	1155	1167.7	-12.7	2.05	-2.9	-7.87	-2.96	-0.51	-0.51
137	1164	1175.99	-11.99	1.01	-3.54	-8.2	-2.94	0.74	0.94
138	1170	1184.28	-14.28	-0.04	-3.86	-7.52	-3.73	0.01	0.86
139	1174	1192.56	-18.56	-1.08	-3.89	-5.74	-4.43	-2.34	-1.07
140	1185	1200.85	-15.85	-2.13	-3.71	-3	-2.39	-2.09	-2.53
141	1211	1209.14	1.86	-3.18	-3.39	0.16	1.96	2.31	4
142	1215	1217.42	-2.42	-4.22	-3.01	2.96	3.9	0.32	-2.38
143	1230	1225.71	4.29	-5.26	-2.67	4.77	5.2	1.56	0.69
144	1238	1234	4	-6.28	-2.43	5.28	4.66	1.8	0.97
145	1239	1242.28	-3.28	-7.3	-2.34	4.68	2.03	0.05	-0.4
146	1241	1250.57	-9.57	-8.31	-2.43	3.47	-0.7	-1.03	-0.57
147	1246	1258.86	-12.86	-9.3	-2.7	2.28	-2.24	-0.89	-0.01
148	1253	1267.14	-14.14	-10.28	-3.15	1.56	-2.67	-0.86	1.26
149	1254	1275.43	-21.43	-11.23	-3.76	1.4	-1.92	-2.15	-3.77
150	1276	1283.72	-7.72	-12.17	-4.5	1.6	1.07	2.4	3.88
151	1277	1292	-15	-13.09	-5.34	1.75	1.52	0.58	-0.42
152	1280	1300.29	-20.29	-13.98	-6.26	1.57	1.22	-1.02	-1.82
153	1291	1308.58	-17.58	-14.85	-7.21	0.93	1.98	0.82	0.75
154	1297	1316.86	-19.86	-15.69	-8.16	-0.18	1.8	1.47	0.9
155	1298	1325.15	-27.15	-16.51	-9.08	-1.61	-0.07	0.14	-0.03
156	1299	1333.44	-34.44	-17.3	-9.92	-2.96	-2.13	-1.11	-1.02
157	1306	1341.72	-35.72	-18.06	-10.67	-3.81	-2.94	-0.4	0.15
158	1314	1350.01	-36.01	-18.79	-11.31	-3.85	-3.25	-0.05	1.23
159	1318	1358.3	-40.3	-19.49	-11.84	-2.99	-3.36	-1.89	-0.73
160	1327	1366.58	-39.58	-20.16	-12.27	-1.46	-1.24	-1.88	-2.58
161	1350	1374.87	-24.87	-20.8	-12.62	0.23	2.91	2.59	2.81
162	1357	1383.16	-26.16	-21.4	-12.91	1.41	4.19	2.15	0.4
163	1361	1391.45	-30.45	-21.98	-13.13	1.7	2.87	0.05	0.03
164	1363	1399.73	-36.73	-22.52	-13.28	1.12	1.49	-1.7	-1.85
165	1374	1408.02	-34.02	-23.03	-13.34	-0.18	2.01	0.08	0.45
166	1382	1416.31	-34.31	-23.51	-13.24	-2.15	2.49	1.33	0.78
167	1385	1424.59	-39.59	-23.95	-12.94	-4.64	1.42	1.07	-0.55
168	1390	1432.88	-42.88	-24.37	-12.39	-7.27	-1.02	1.36	0.8
169	1391	1441.17	-50.17	-24.75	-11.54	-9.28	-5.06	0.04	0.42
170	1393	1449.45	-56.45	-25.1	-10.39	-9.76	-9.09	-2.46	0.35
171	1398	1457.74	-59.74	-25.42	-8.97	-7.99	-10.07	-5.02	-2.27
172	1420	1466.03	-46.03	-25.7	-7.34	-4.07	-5.13	-2.81	-0.98
173	1449	1474.31	-25.31	-25.96	-5.59	0.89	3.15	2	0.2
174	1476	1482.6	-6.6	-26.18	-3.86	5.3	9.01	5.62	3.5
175	1480	1490.89	-10.89	-26.37	-2.25	7.95	8.34	2.52	-1.07
176	1485	1499.17	-14.17	-26.54	-0.89	8.68	4.46	0.48	-0.37
177	1489	1507.46	-18.46	-26.67	0.15	8.19	-0.04	-0.75	0.66
178	1490	1515.75	-25.75	-26.77	0.79	7.51	-3.55	-2.79	-0.94

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Native American Eagle Sun

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Dec 10, 2006, 7:47 am

Here are a few graphs of the table:

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Native American Eagle Sun

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Dec 10, 2006, 8:38 am

I'm tired, I'll work on Wave Matrix projection and probability projection at another time.
I'll pop in now and then.
For now, .

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Native American Eagle Sun

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Dec 30, 2006, 4:20 am

The Wave Matrix has been on hold for a bit while I format H-Trac for Pick 3 and Pick 4.
I'll update this thread when I have time.
Thank you for you patients..

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Native American Eagle Sun

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Jan 24, 2007, 5:11 pm

A few side Tracs... but still working on it.

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pumpi76

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Feb 13, 2007, 2:18 am

JadeLottery, God knows i'll like to follow you but is all too complex for me...
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Native American Eagle Sun

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Feb 15, 2007, 4:06 pm

Quote: Originally posted by pumpi76 on Feb 13, 2007
JadeLottery, God knows i'll like to follow you but is all too complex for me...

it does seem that way, but in the end it will be simple like a computer that can fit inside a single room.
if learning the knowable were easy, everyone would be a genius.

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Native American Eagle Sun

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Feb 27, 2007, 4:57 pm

Whoo, I'm glad I used the Wave Matrix to analyze my 401k and put all my money into Cash back in Mid Dec.
Now the fun part, make even more money after the Market gose Lower.... YEAH!!!
I love those Waves and the Matrix it's in.

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Native American Eagle Sun

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Mar 13, 2007, 5:51 pm

Whoo, another one.... the storm ain't over yet.
Hang on boys and girls, we're going for a ride!!!
Yee-Haw....

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Native American Eagle Sun

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Mar 27, 2007, 5:10 am

I've been setting this aside too long, other work and projects gobbling up my time.... this week, this week... get a move on.

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Native American Eagle Sun

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Apr 1, 2007, 4:32 pm

Alright, let's take this in a few steps. First, the components of the projection are the Regression, Wave Matrix and Remainder values. Each of these, when summed together, equal the Observed data. The Observed data in this case is the Draw Occurrence. However, the Observed data is not restricted to just Draw Occurrence. The Observed data could be the Number drawn in a specific column, the Sum of the Numbers in each column or any data where a Regression curve/line can be derived to create an Oscillating data set about the X-axis.
Observed data = Regression + Wave Matrix + Remainder
more to continue...

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Native American Eagle Sun

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Apr 1, 2007, 5:03 pm

As an example, we can look at the first few rows of the table I posted a while ago.

Index n	Ball #01 D_n - Draw Occurrence	Linear Regression L_n = m * n + b m = 8.29, b =40.70	Oscillation Data - A {D_n - L_n}	Wave Data - W₁ d₁ = -0.87 i = 32	Wave Data - W₂ d₂ = 0.73 i = 32	Wave Data - W₃ d₃ = 1.89 i = 32	Wave Data - W₄ d₄ = 3.06 i = 32	Wave Data - W₅ d₅ = 3.83 i = 32	Remainder - R₅
1	16	48.98	-32.98	-23.92	-0.97	1.81	-3.29	-3.74	-2.87
2	41	57.27	-16.27	-23.85	-1.11	2.41	1.27	2.09	2.92
3	51	65.56	-14.56	-23.76	-1.33	2.92	3.6	2.65	1.36

The sum of each component, Linear Regression, Wave Data and Remainder equals the Draw Occurrence.

Index 1: 16 = 48.98 + (-23.92) + (-0.97) + 1.81 + (-3.29) + (-3.74) + (-2.87)

Index 2: 41 = 57.27 + (-23.85) + (-1.11) + 2.41 + 1.27 + 2.09 + 2.92

Index 3: 51 = 65.56 + (-23.76) + (-1.33) + 2.92 + 3.60 + 2.65 + 1.36

Continues...

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Apr 1, 2007, 6:04 pm

In making a projection, we need to understand each component. The Regression is the easiest, just extend the Index to the next number in its sequence. For the example I've provided, the next Index value would be 179 and the Linear Regression value would be 1,524.61. Next the Wave Matrix and Remainder Data need to be projected to the next Index value. The Wave Matrix and Regression Data are derived from the Oscillation Data and approximate the individual Wave Components of the Oscillation Data.
Previously, I posted a graph of the Oscillation, Wave and Remainder data. Each of these components move about the X-axis at different frequencies, shown here:
Continues...

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Native American Eagle Sun

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Apr 1, 2007, 7:14 pm

We use the Wave Matrix and Remainder data to project the deviation from the Regression component. How we handle each Wave Data and Remainder Data is dependent on the frequency of each component. Wave Data (W1) is a relatively low frequency and can be projected to a value that is close to the last Indexed value. Wave Data (W5) and the Remainder (R5) are relatively high frequencies and can be approximated using a Normal Distribution calculation that has the average centered on the X-axis and the Standard Deviation trailing out above and below the X-axis. To project higher frequency waves and the remainder data, it becomes more of a range probability or educated guess where the next data point might be. With this also, we could induce a kind of Quantum Fog at this level using the Random Number Transforms - Normal Distribution to guess the next value.
Continues...

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