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Bidirectional Mean Averaging and The Wave Matrix

Topic closed. 28 replies. Last post 10 years ago by Hyperdimension.

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JADELottery's avatar - YingYangYong 01.PNG
The Quantum Master
West Concord, MN
United States
Member #21
December 7, 2001
3685 Posts
Offline
Posted: December 10, 2006, 7:45 am - IP Logged

Bidirectional Mean Averaging and The Wave Matrix

    1 - Oscillation Data Set

      A = {X1, X2, X3, ... , Xn-2, Xn-1, Xn}

       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
ed) / (1 + ed)
      U3 = (U2 + X3
ed) / (1 + ed)
      ...
      Un-2 = (Un-3 + Xn-2
ed) / (1 + ed)
      Un-1 = (Un-2 + Xn-1
ed) / (1 + ed)
      Un  = (Un-1 + Xn
ed) / (1 + ed)

      Up Mean Averaging Set -  U = {U1, U2, U3, ... , Un-2, Un-1, Un}

    3 - Down Mean Averaging

      Dn  = Xn
      Dn-1 = (Dn + Xn-1
ed) / (1 + ed)
      Dn-2 = (Dn-1 + Xn-2
ed) / (1 + ed)
      ...
      D3 = (D4 + X3
ed) / (1 + ed)
      D2 = (D3 + X2
ed) / (1 + ed)
      D1 = (D2 + X1
ed) / (1 + ed)

      Down Mean Averaging Set -  D = {D1, D2, D3, ... , Dn-2, Dn-1, Dn}

    4 - Bidirectional Mean Averaging

        B = (U + D) / 2

             
¯

      Y1 = (U1 + D1) / 2
      Y2 = (U2 + D2) / 2
      Y3 = (U3 + D3) / 2
      ...
      Yn-2 = (Un-2 + Dn-2) / 2
      Yn-1 = (Un-1 + Dn-1) / 2
      Yn  = (Un + Dn) / 2

      Bidirectional Mean Averaging Set -  Bma(A,d) = B = {Y1, Y2, Y3, ... , Yn-2, Yn-1, Yn}
          A -  oscillation data set.
          d -  degree of weighting data.
             
-¥ £ d £ +¥, d is any real number.
              lim d
®  +¥, Bma(A,d) = B = A
              lim d
®  -¥, Bma(A,d) = B = (X1 + Xn ) / 2

  5 - Iteration of Bidirectional Mean Averaging

      B1 = Bma(A,d)
      B2 = Bma(B1,d)
      B3 = Bma(B2,d)
      ...
      Bi-2 = Bma(Bi-3,d)
      Bi-1 = Bma(Bi-2,d)
      Bi  = Bma(Bi-1,d)

      Iteration of Bidirectional Mean Averaging -  Ibma(A,d,i) = Bi

          A - oscillation data set.   
          d - degree of weighting.
          i - iteration of averaging.
            i
³ 1, i is any positive integer.

    6 - Ibma as Wave Data Set and Remainder Set of Wave Data Set

        Wave Data Set -  W = Ibma(A,d,i)

       R = A - W

           
¯

      R1 = X1 - Y1
      R2 = X2 - Y2
      R3 = X3 - Y3
      ...
      Rn-2 = Xn-2 - Yn-2
      Rn-1 = Xn-1 - Yn-1
      Rn = Xn - Yn

       R = {R1, R2, R3, ... , Rn-2, Rn-1, Rn}

      Remainder Set of Wave Data Set -  R 

    7 - Iteration of Remainder Set, the Wave Matrix and Remainder Set of the Wave Matrix

      W1 = Ibma(A,d,i) ,    R1 = A - W1
      W2 = Ibma(R1,d,i) ,    R2 = R1 - W2
      W3 = Ibma(R2,d,i) ,    R3 = R2 - W3
      ...
      Wj-2 = Ibma(Rj-3,d,i) ,    Rj-2 = Rj-3 - Wj-2
      Wj-1 = Ibma(Rj-2,d,i) ,    Rj-1 = Rj-2 - Wj-1
      Wj  = Ibma(Rj-1,d,i) ,    Rj = Rj-1 - Wj

      The Wave Matrix -  Wm(A,d,i,j) = {W1, W2, W3, ... , Wj-2, Wj-1, Wj}
      Remainder Set of the Wave Matrix -  Rm(A,d,i,j) = Rj

         A - oscillation data set.
            A = W1 + W2 + W3 + ... + Wj-2 + Wj-1 + Wj + Rj
        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
® +¥, Rj = 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 Ibma(A,d,i)2) / n
                                  =  [a = 1 to a = n]
Ö(S (Ya)2) / n

        RMS of Remainder Data Set -  rmsR =  Ö(S (R)2) / n 
                                      = 
Ö(S (A - W)2) / n
                                      = 
Ö(S (A - Ibma(A,d,i))2) / n
                                      =  [a = 1 to a = n]
Ö(S (Ra)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 = Ibma(A,d1,i) ,    R1 = A - W1 ,  d1 à rmsW1 = rmsR1
      W2 = Ibma(R1,d2,i) ,    R2 = R1 - W2 ,  d2
à rmsW2 = rmsR2
      W3 = Ibma(R2,d3,i) ,    R3 = R2 - W3 ,  d3
à rmsW3 = rmsR3
      ...
      Wj-2 = Ibma(Rj-3,dj-2,i) ,    Rj-2 = Rj-3 - Wj-2 ,  dj-2
à rmsWj-2 = rmsRj-2
      Wj-1 = Ibma(Rj-2,dj-1,i) ,    Rj-1 = Rj-2 - Wj-1 ,  dj-1
à rmsWj-1 = rmsRj-1
      Wj = Ibma(Rj-1,dj,i)  ,      Rj = Rj-1 - Wj  ,      dj
à rmsWj = rmsRj

      Degree of Weighting Data Set -  d = {d1, d2, d3, ... , dj-2, dj-1, dj}

      RMS Adjusted Wave Matrix -  Wm(A,d,i,j) = {W1, W2, W3, ... , Wj-2, Wj-1, Wj}
      RMS Adjusted Remainder Set of the Wave Matrix -  Rm(A,d,i,j) = Rj

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Any gain or loss is your responsibility.
Use at your own risk.

Order is a Subset of Chaos
Knowledge is Beyond Belief
Wisdom is Not Censored
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Jehocifer

    JADELottery's avatar - YingYangYong 01.PNG
    The Quantum Master
    West Concord, MN
    United States
    Member #21
    December 7, 2001
    3685 Posts
    Offline
    Posted: December 10, 2006, 7:46 am - IP Logged

    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

    Dn - Draw

    Occurrence

    Linear Regression

    Ln = m * n + b

    m = 8.29, b =40.70

    Oscillation

    Data - A

     {Dn - Ln}

    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

    Presented 'AS IS' and for Entertainment Purposes Only.
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    Knowledge is Beyond Belief
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      JADELottery's avatar - YingYangYong 01.PNG
      The Quantum Master
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      Posted: December 10, 2006, 7:47 am - IP Logged

      Here are a few graphs of the table:

       

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        JADELottery's avatar - YingYangYong 01.PNG
        The Quantum Master
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        Posted: December 10, 2006, 8:38 am - IP Logged

        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, Sleep.

        Presented 'AS IS' and for Entertainment Purposes Only.
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        Order is a Subset of Chaos
        Knowledge is Beyond Belief
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          JADELottery's avatar - YingYangYong 01.PNG
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          Posted: December 30, 2006, 4:20 am - IP Logged

          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..

          Presented 'AS IS' and for Entertainment Purposes Only.
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          Order is a Subset of Chaos
          Knowledge is Beyond Belief
          Wisdom is Not Censored
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            JADELottery's avatar - YingYangYong 01.PNG
            The Quantum Master
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            Posted: January 24, 2007, 5:11 pm - IP Logged

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

            Presented 'AS IS' and for Entertainment Purposes Only.
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            Order is a Subset of Chaos
            Knowledge is Beyond Belief
            Wisdom is Not Censored
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              Honduras
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              August 29, 2005
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              Posted: February 13, 2007, 2:18 am - IP Logged

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

                JADELottery's avatar - YingYangYong 01.PNG
                The Quantum Master
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                Posted: February 15, 2007, 4:06 pm - IP Logged

                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.

                Presented 'AS IS' and for Entertainment Purposes Only.
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                Order is a Subset of Chaos
                Knowledge is Beyond Belief
                Wisdom is Not Censored
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                Jehocifer

                  JADELottery's avatar - YingYangYong 01.PNG
                  The Quantum Master
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                  Posted: February 27, 2007, 4:57 pm - IP Logged

                  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.

                  Presented 'AS IS' and for Entertainment Purposes Only.
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                  Order is a Subset of Chaos
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                    JADELottery's avatar - YingYangYong 01.PNG
                    The Quantum Master
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                    Posted: March 13, 2007, 5:51 pm - IP Logged

                    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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                      JADELottery's avatar - YingYangYong 01.PNG
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                      Posted: March 27, 2007, 5:10 am - IP Logged

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

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                        JADELottery's avatar - YingYangYong 01.PNG
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                        Posted: April 1, 2007, 4:32 pm - IP Logged

                        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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                        Order is a Subset of Chaos
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                          JADELottery's avatar - YingYangYong 01.PNG
                          The Quantum Master
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                          Posted: April 1, 2007, 5:03 pm - IP Logged

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

                          Index

                          n

                          Ball #01

                          Dn - Draw

                          Occurrence

                          Linear Regression

                          Ln = m * n + b

                          m = 8.29, b =40.70

                          Oscillation

                          Data - A

                           {Dn - Ln}

                          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...

                          Presented 'AS IS' and for Entertainment Purposes Only.
                          Any gain or loss is your responsibility.
                          Use at your own risk.

                          Order is a Subset of Chaos
                          Knowledge is Beyond Belief
                          Wisdom is Not Censored
                          Douglas Paul Smallish
                          Jehocifer

                            JADELottery's avatar - YingYangYong 01.PNG
                            The Quantum Master
                            West Concord, MN
                            United States
                            Member #21
                            December 7, 2001
                            3685 Posts
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                            Posted: April 1, 2007, 6:04 pm - IP Logged

                            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...

                            Presented 'AS IS' and for Entertainment Purposes Only.
                            Any gain or loss is your responsibility.
                            Use at your own risk.

                            Order is a Subset of Chaos
                            Knowledge is Beyond Belief
                            Wisdom is Not Censored
                            Douglas Paul Smallish
                            Jehocifer

                              JADELottery's avatar - YingYangYong 01.PNG
                              The Quantum Master
                              West Concord, MN
                              United States
                              Member #21
                              December 7, 2001
                              3685 Posts
                              Offline
                              Posted: April 1, 2007, 7:14 pm - IP Logged

                              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...

                              Presented 'AS IS' and for Entertainment Purposes Only.
                              Any gain or loss is your responsibility.
                              Use at your own risk.

                              Order is a Subset of Chaos
                              Knowledge is Beyond Belief
                              Wisdom is Not Censored
                              Douglas Paul Smallish
                              Jehocifer