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Topic Minitab, Fit Regression Model Problem Using Categorical Variable

Minitab, Fit Regression Model Problem Using Categorical Variable

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This topic contains 8 replies, has 4 voices, and was last updated by  Robert Butler 4 months ago.

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  • #573649 Reply

    I’m trying to find a regression with 2 continuous predictors and a categorical predictor using Fit Regression Model in Minitab.

    For all categories the constant coefficient changes, but the two other ones are always exactly the same. Is it because I have to many categories? How can I resolve this problem?

    `Regression Equation

    Categorical
    a Z = 0.0444 + 0.4582 X + 0.004750 Y

    b Z = 0.0572 + 0.4582 X + 0.004750 Y

    c Z = 0.0749 + 0.4582 X + 0.004750 Y

    d Z = 0.1523 + 0.4582 X + 0.004750 Y

    e Z = 0.629 + 0.4582 X + 0.004750 Y

    f Z = 0.0141 + 0.4582 X + 0.004750 Y

    #574053 Reply

    A bit broad….hard to even suggest without seeing the data. It would seem your X’s are highly correlated.

    #574104 Reply

    With a model y = b0 + b1x1 + b2x2 + b3x3, where x1 and x2 are your continuous x’s and x3 is your categorical x, you can expect to see the same coefficients for b1 and b2. I presume this is the model being fit above.

    If you think the slopes for b1 and b2 could differ depending on the level of your categorical variable, then you can fit a model with interactions to test this hypothesis:

    y = b0 + b1x1 + b2x2 + b3x3 + b4x1x3 + b5x2x3 (and perhaps b6x1x2 for good measure)

    Once you include the interactions in your model (via Stat > Regression > Regression > Fit Regression Model > Model > highlight the Predictors > Interactions through order 2 > Add), you will then see different coefficients.

    #578811 Reply

    Chris Seider, heres the data:

    `Y X Categorical Z
    4 1.6 a 0.7
    0.73 1.2 a 0.3
    3.6 1.6 a 0.7
    0.47 1.2 a 0.3
    5 1.6 a 0.7
    1 1.2 a 0.3
    2.95 0.762 a 0.41
    0.38 0.8 d 0.25
    6.6 0.51 a 0.26
    6.6 0.51 a 0.26
    2.5 0.9 a 0.7
    0.38 0.8 d 0.25
    0.326 0.7 a 0.4
    6.2 0.9652 a 0.3
    2 1.2 a 0.8
    29.5 1.6 a 1.4
    3 1.2 a 0.4
    17.5 2.3 b 1.2
    5 1.3 c 0.78
    5 1.3 c 0.78
    11.2 0.85 a 0.75
    7.7 0.85 a 0.56
    3.1 0.7499858 c 0.9
    4 1.27 a 0.3
    30.27 1.499997 c 1.8
    43.67 1.499997 c 1.8
    43.67 1.499997 c 1.8
    15.66 1.27 d 0.65
    6.1 1.1 a 0.6
    11.129 1.27 d 0.65
    18.662 1.011936 d 0.7
    1.67 2.3 b 0.6
    0.57 1.9 c 0.3
    1 0.7 a 0.8
    4.42 1.099999999 c 0.86
    3.4 1.2 c 0.6
    3.2 1.397 d 0.2
    0.29 0.6 a 0.41
    3.4 1.2 c 0.6
    11.1 1.016 a 1.4
    16 0.6 a 0.6
    4.75 1.3 a 0.85
    9 1.6 f 0.8
    5.5 1.1 c 0.8
    45 0.68 a 0.4
    5.07 0.8636 a 0.4
    3.5 0.9 c 0.78
    2.4 0.9 c 0.75
    1.5 0.67 c 0.27
    19 0.6 a 0.15
    1.86 1.016 c 0.8
    0.5 1.2 a 0.1
    18.4 1.3 a 0.82
    8.3 0.499999999 a 0.35
    1.4 0.508 c 0.3
    1.5 0.7 f 0.31
    10.29 1.3 a 0.4
    1.45 0.75 c 0.3
    14.512 1.524 d 0.65
    17.548 0.762 a 0.22
    23.951 0.762 a 0.22
    14.875 0.762 a 0.22
    10.289 1.016 d 0.65
    23.534 1.524 d 1.2
    0.59 0.762 c 0.4
    1.05 1.1 c 0.35
    2.8 0.9 a 0.4
    4 0.95 c 0.4
    3 1.0922 a 0.28
    3.2 0.8 c 0.3
    3.2 0.8 a 0.3
    1.6 0.8 c 0.3
    1.6 0.8 c 0.3
    1.6 0.8 c 0.3
    1.6 0.8 f 0.3
    10.4 2.25 f 0.8
    9.94 0.45 a 0.4
    3.5 0.4 a 0.25
    14.1 0.68 a 0.6
    4.8 0.8 b 0.8
    1.45 0.6 a 0.15
    1.45 0.6 a 0.15
    17.6 0.6 a 0.35
    1.33 1.13 c 0.3
    1.497 1.016 c 0.72
    8.5 1.016 a 0.6
    8.8 1.778 d 0.8
    0.15 1.016 a 0.38
    1.5 1.143 a 1.2
    3.6 1.016 f 1.06
    3.45 0.8 a 0.6
    3.45 0.8 a 0.6
    6 1.2 a 0.8
    5 1.25 c 0.7
    4 0.7 f 0.3
    1.63 1.143 f 0.72
    4.25 0.35 a 0.15
    4.25 0.35 a 0.15
    9.04 1.27 f 0.4
    10.5 1.4 c 0.64
    5.01 0.52 a 0.2
    2.97 0.95 a 1.59
    21 1.016 a 0.78
    9.7 1.2 c 0.7
    1.115 0.9779 a 0.35
    1.79 0.9 a 0.39
    1.3 0.7 c 0.27
    45 0.75 a 0.8
    25 1.143 a 1.2
    0.31 1.016 b 0.2
    0.31 1.016 b 0.2
    11.2 0.55 a 0.3
    6.5 1.2 a 0.4
    5.5 1.1 a 0.35
    7.05 0.9 c 0.66
    1.7 0.65 a 0.3
    1.7 0.65 a 0.3
    13.28 1.016 a 0.65
    9.5 0.762 a 0.3
    9.5 0.762 a 0.3
    0.38 0.999998 d 0.28
    11 1.016 a 0.6
    1.1 1.016 b 0.43
    10 0.45 a 0.15
    19.3 0.6 a 0.3
    2.467 1.27 a 0.31
    9.45 0.485 a 0.31
    9.45 0.485 a 0.31
    14 0.65 a 0.3
    1.27 1.94056 b 0.86
    1.625 1.143 c 0.4
    1.594 0.889 a 0.49
    3 1.016 a 0.75
    5.12 1.1938 b 0.77
    5.12 1.1938 b 0.77
    0.3 0.6 c 0.1
    3 0.9 a 0.24
    7 0.4 a 0.2
    0.77 1.199999886 b 0.75
    11.35 0.44 a 0.22
    1.05 1.016 b 0.43
    12 0.45 a 0.1
    2.8 0.9 c 0.2
    3.8 0.9 c 0.63
    0.34 1.25 c 0.4
    0.34 1.25 c 0.4
    21.3 1.2 a 1.2
    5.16 2.032 d 0.65
    7 0.56 a 0.17
    7 0.56 a 0.17
    15.7 0.8 c 0.65
    29.5 1.016 b 0.4
    9 0.65 a 0.2
    40 0.6 a 0.3
    40 0.6 a 0.3
    40 0.6 a 0.3
    1.3 0.6 f 0.37
    13.9 1.3 a 0.6
    8.64 1.3 a 0.6
    3.14 1.2 c 0.49
    10.86 0.8382 c 0.3
    10.86 0.8382 a 0.3
    10.86 0.8382 a 0.3
    11 1.016 a 0.78
    11 1.016 a 0.78
    11 1.016 a 0.78
    11 1.016 a 0.78
    1.65 0.85 a 0.4
    1 0.7 c 0.3
    8.5 1.1 a 1.2
    4.815 1.1938 b 0.66
    4.815 1.1938 b 0.66
    3.74 0.65 a 0.4
    3.74 0.65 a 0.4
    4.03 1.143 c 0.4
    3.65 0.9 a 0.4
    1.2 1.2 b 0.4
    2.7 0.6 a 1.3
    2.7 0.6 a 1.3
    1.01 1.016 a 0.4
    0.88 0.8 c 0.6
    0.4 0.762 f 0.25
    1.1 1.016 f 0.25
    2.37 1.016 c 0.36
    4 1.2 a 0.8
    10 0.8 a 0.8
    10 0.8 a 0.8
    1.62 0.9 c 0.35
    2.75 1.9 c 0.3
    8.8 0.9 a 0.7
    64 0.66 a 0.3
    18 0.65 a 0.49
    7.4 0.889 a 0.9
    7.4 0.889 a 0.9
    9 1.1 a 0.7
    9 1.1 a 0.7
    6.04 0.55 a 0.4
    10.7 0.6 a 0.35
    30.12 0.65 a 0.31
    2 1.2 c 0.84
    1.26 1.524 b 1.36
    8.817 0.4826 a 0.55
    5.3 1.8 c 0.9
    3.5 0.8 a 0.45
    1.1 1.14 c 0.27
    3.45 0.9 a 0.7
    3.45 0.9 a 0.7
    0.84 0.9 f 0.3
    0.8 0.8 a 0.7
    6 0.4 a 0.3
    1.09 0.7874 a 0.35
    3.8 1.524 d 0.8
    32.9 0.6 a 0.8
    1.55 0.8 c 0.3
    1.55 0.8 f 0.3
    1.2 0.8 f 0.8
    4.354 0.9906 a 0.58
    4.354 0.9906 a 0.96
    0.98 1.499999999 b 0.6
    12 0.45 a 0.1
    1.66 1.016 c 0.4
    5.5 1.27 a 0.8
    6 0.9 a 0.8
    7 1.3 c 0.7
    0.3 0.6 c 0.25
    17 0.7112 a 0.8
    9.04 1.27 f 0.33
    0.8 1.1 a 0.2
    23 0.6 a 0.34
    6 0.6 a 0.45
    2.8 0.91 a 0.6
    3.5 1.2 c 0.3
    42.1 0.8636 a 0.4
    0.21 0.762 f 0.1
    8 1.016 a 0.8
    15.9 0.45 a 0.24
    10 0.45 a 0.15
    14.9 0.45 a 0.2
    20 0.45 a 0.2
    20 0.45 a 0.2
    20 0.45 a 0.2
    1.3 1.016 f 0.35
    8.64 1.3 a 0.6
    10 0.6 a 0.2
    2.6 0.74 a 0.7
    3 1.15 c 0.4
    1.4 0.7 c 0.6
    15 0.52 a 0.35
    30 0.59 a 0.35
    20 0.55 a 0.35
    4.2 1.397 a 0.6
    4.2 1.397 a 0.6
    8.64 1.3 a 0.6
    7.15 1.1 c 1.1
    16 0.55 a 0.4
    8.5 0.55 a 0.3
    0.22 0.9144 a 0.25
    15.8 0.55 a 0.6
    1.022 0.8 b 0.46
    0.6 0.8 c 0.3
    0.433 1.016 a 0.35
    32.6 0.8636 a 0.4
    3.2 1.524 b 0.1
    6 1.27 a 0.75
    1.2 1.27 a 0.67
    2.3 1.778 b 0.76
    0.8 0.9 a 0.8
    1.107 1.0668 f 0.29
    1.1 0.699999999 a 0.6
    4.3092 1.25 c 0.6
    45.5 1.3 b 1.2
    3.511 0.762 f 0.85
    3.594 0.762 f 0.85
    0.95 1.9304 c 0.7
    14.1 0.68 a 0.6
    5.1 0.8 a 0.6
    13 1.2 c 0.8
    2.6 1.524 c 0.62
    9.6 1.2 a 0.6
    2.1 0.9 c 1.2
    50 0.6096 a 0.3
    50 0.6096 a 0.3
    20 0.6096 a 0.3
    20 0.6096 a 0.3
    9.1 0.8636 a 0.4
    1.84 0.9906 c 0.7
    0.49 0.9906 c 0.7
    8 1.016 a 0.8
    16.757 1.99898 a 0.7
    35 0.7 a 0.4
    45.8 1.016 a 0.4
    8.752 1.999996 a 0.9
    8.752 1.999996 a 0.9
    30 1.143 a 0.75
    30 1.143 a 0.75
    2.06 0.9 c 0.3
    8.01 0.45 a 0.4
    1.3 0.6 a 0.25
    3.9 0.8 a 0.2
    1.73 1.016 a 0.21
    6.9 0.9 a 0.6
    6.7 0.8 a 0.88
    6.2 1.2 a 0.8
    2.64 1.1 a 0.2
    2.64 1.1 a 0.2
    8.4 0.508 a 0.35
    16.5 0.4 a 0.15
    13 2.2 a 1.7
    3 1.2 a 0.4
    2 0.7 c 0.3
    0.34 1.25 c 0.4
    14 1.7 b 0.64
    10.14 0.45 a 0.4
    1.544 1.0922 f 0.77
    43 0.7 a 0.6
    22.3 0.6 a 0.4
    1.63 0.8 a 0.34
    1.54 0.8 c 0.3
    0.54 0.8 a 0.3
    16.4 0.63 a 0.35
    6.1 0.56 a 0.3
    35 0.6096 a 0.3
    35 0.6096 a 0.3
    7 0.9 a 0.7
    1.5 0.9 a 0.2
    1 0.9 a 0.2
    5.4 0.9 a 0.7
    5.2 0.7 a 1.2
    7.4 1.27 a 0.8
    7.4 1.27 a 0.8
    5.05 0.45 a 0.4
    1.92 0.8 d 0.3
    24 1.6 b 0.7
    0.76 0.6 c 0.6
    11.6 1.2 a 0.7
    15.8 1.2 a 0.8
    7.7 1.2 a 0.6
    22.6 1.2 a 0.9
    3.5 1.143 a 0.9
    12 0.55 a 0.35
    20.8 0.5588 a 0.35
    6.3 1.016 a 0.8
    18.5 0.55 a 0.6
    2.4 1.0922 a 0.9
    7.4 0.39 a 0.13
    14 1.52 a 0.56
    14.66 0.45 a 0.3
    0.78 0.780034 a 0.9
    6 1.8 a 0.6
    0.73 0.889 a 0.65
    17.5 1.2 a 0.8
    1.25 1.1 c 1.28
    2.8 0.6 c 0.3
    11.5 0.55 a 0.2
    7.2 1.8 c 0.52
    2.64 1.1 a 0.35
    2.64 1.1 a 0.35
    9.16 1.3 a 0.7
    9.16 1.3 a 0.7
    3.5 0.9 a 0.4
    3.5 0.9 a 0.4
    13.658 0.5334 a 1.32
    14.3416 0.89916 a 0.95
    1.164 1.016 a 0.6
    4.74 1.8 c 0.35
    10 0.7 a 0.4
    12.5 1.016 a 0.25
    0.78 0.9 a 0.25
    3.25 0.9 a 0.25
    20.63 0.65 a 0.3
    6 1.1 a 0.6
    0.22 0.762 a 0.21
    1.7 0.508 f 0.65
    18 0.6 a 0.25
    18 0.6 a 0.25
    0.9 0.889 f 0.7
    0.47 0.999998 f 0.12
    11 0.4826 a 0.25
    5.7 1.3 a 0.8
    1.5 1.397 c 0.3
    23 1.6 a 1.1
    5.6 0.95 a 0.3
    3.4 0.75 a 0.3
    11 1.3 a 0.8
    4.25 1.386 c 0.3
    2.25 0.9 c 0.7
    7.4 1.27 a 0.75
    0.4 0.6 c 0.1
    15 0.6 a 0.3
    35 0.7 a 0.7
    35 0.6 a 0.25
    6.45 1.778 a 0.8
    7.35 1.143 d 0.3
    0.17 0.381 a 0.3
    19 0.66 a 0.3
    27 0.7 a 0.3
    27 0.7 a 0.3
    1.37 0.9 a 0.45
    1.22 1.1 a 0.56
    2.029 0.889 a 0.4
    1.2 0.75 f 0.35
    3.85 1.524 a 0.4
    11.57 1.27 a 0.7
    1.4 0.8382 c 0.4
    7.3 0.9 a 0.8
    7 1.3 a 0.8
    43.67 1.499997 c 1.8
    27 0.6 a 0.25
    37 0.6 a 0.25
    4 0.8 a 0.6
    6.04 2.1844 b 0.8
    2.1 1.2 a 0.3
    5.1 0.6 a 0.2
    2.4 1.2 a 1.2
    0.44 0.9 a 0.2
    2.1 0.9 c 0.25
    35.9 0.7 a 0.4
    34 0.6 a 0.4
    1.85 0.77343 a 0.7
    4.94 0.9 a 0.6
    10 0.6 a 0.3
    25.3 0.9 a 0.6
    1.18 0.51 c 0.4
    1.101 0.762 a 0.1
    2.7 0.4 a 0.2
    1.4 0.45 a 0.4
    5 0.9 c 0.7
    3.6 0.75 a 0.6
    3.1 0.9 a 0.3
    1 0.6 f 0.37
    5.4 0.9 a 0.7
    0.9 0.8 a 0.2
    5.4 0.9 a 0.7
    0.9 0.8 a 0.2
    5.4 0.9 a 0.7
    0.9 0.8 a 0.2
    5.4 0.9 a 0.7
    0.9 0.8 a 0.2
    0.4 0.8 a 0.3
    3.2 0.9 a 0.7
    3.2 0.9 a 0.7
    4.1 0.9 a 0.8
    2 0.7 c 0.3
    2 0.7 c 0.3
    2 0.4 a 0.8
    1.1 0.9 a 0.25
    4 0.9 a 0.2
    9.3 0.6 a 0.3
    0.5 1.1 a 0.4
    3.85 1.1 c 0.9
    5.7 0.7 a 0.3
    10.5 1.7 b 1.8
    11.98 1.63 b 0.7
    4.7 1.6 a 0.8
    0.82 0.8 a 0.8
    6.5 0.9 a 0.7
    24.1 0.45 a 0.2
    0.62 1.1 a 0.23
    7.6 1.4224 a 0.4
    2.4 1.2 c 0.8
    8.2 0.45 a 0.3
    9 0.8 a 0.8
    9 0.8 a 0.8
    19 0.45 a 0.25
    5 0.6 a 0.3
    7 0.9 a 0.8
    0.14 0.9 a 0.2
    2.5 0.8 a 0.6
    5.4 0.9 a 0.7
    0.9 0.8 a 0.2
    19 0.65 a 0.25
    0.42 1.016 a 0.2
    1.68 0.999998 a 0.42
    1.1 0.8 c 0.6
    1 0.8 c 0.6
    0.989 0.65 c 0.4
    4.8 1.25 c 0.7
    12.63 1.1 f 0.6
    9.3 0.6 a 0.3
    13 1.4986 a 0.7
    5.745 1.35 a 1.15
    11.31 1.25 a 1.2
    8 0.8 a 0.8
    2.15 1.524 a 0.7
    3.1 1.524 a 0.7
    31.33 0.7 a 1.35
    16.7 1.57 a 0.75
    16.7 1.57 a 0.75
    11.98 1.63 b 0.7
    3.1 0.45 a 0.3
    3.6 1.2 a 0.3
    14 1.28 a 0.4
    21.21 0.635 a 0.3
    7 0.9 a 0.8
    0.14 0.9 a 0.2
    6.4 0.9 a 0.8
    0.12 0.9 a 0.2
    27.6 1.29 d 1.2
    1.7 1.4 c 0.6
    7.15 1.1 c 1.1
    0.33 0.45 c 0.4
    6.9 0.88 a 0.35
    6.25 1.499999999 a 0.3
    4.02 0.45 a 0.3
    79.74 1.524 d 1.9
    79.74 1.524 d 1.9
    0.9484 1.6764 c 0.34
    15 1.524 c 1.14
    10.5 1.016 a 0.8
    7 0.73 a 0.3
    1.73 1.3 d 0.7
    1.89 1.3 d 0.8
    0.44 0.9 d 0.3
    0.69 0.9 d 0.45
    6.2 1.2 c 0.8
    58 0.55 a 0.3
    0.87 0.7 a 0.3
    15.25 1.25 a 1.2
    9.8 1.35 a 1.1
    3.3 1.4 a 1.1
    0.91 0.7 a 0.18
    2.41 1.2 a 0.6
    20 0.65 a 0.3
    3.5 0.8 a 0.6
    1.36 0.7 a 0.48
    9.8 1.27 a 0.6
    15.7 2.032 a 1.89
    2 2.032 a 0.22
    5 1.2 c 0.33
    4 0.45 a 0.1
    1.5 1.1176 a 0.8
    1.5 1.1176 a 0.8
    2.85 1.0668 f 0.3
    0.041 0.55 b 0.2
    6 1.3 c 0.6
    2.1 1.1 a 0.3
    11.17 1.016 a 0.75
    5.1 1.2 a 0.7
    38 0.65 a 0.3
    0.5 0.7 c 0.3
    1.5 1.6 a 0.3
    0.155 0.4 a 0.4
    2.5 0.8 c 0.4
    31 0.65 a 0.1
    31 0.65 a 0.25
    7 0.45 a 0.1
    20 1.2 a 0.9
    1.79 1.524 c 0.8
    18 0.6 a 0.15
    52 0.7 a 0.2
    38 0.65 a 0.2
    14 0.7 a 0.15
    9 0.7 a 0.15
    1.8 0.8 a 0.4
    3.2 1.32 c 0.6
    1.3 1.2 c 0.4
    15 0.6 a 0.3
    4.7 1.3 c 0.7
    70 1.27 a 1.1
    3.8 0.549999998 a 0.8
    4 0.47 a 0.1
    0.4 0.7 a 0.6
    13 1.00076 a 0.7
    1 1.1 a 0.4
    1.1 1.14 c 0.27
    3.45 0.9 a 0.7
    3.45 0.9 a 0.7
    1.5 0.7 c 0.27
    25.5 0.53 a 0.25
    8 0.45 a 0.1
    8 0.38 a 0.1
    8 0.38 a 0.1
    2.08 1.143 a 0.43
    0.78 0.9 a 0.2
    66.55 2.0066 c 1.8
    0.55 0.7 c 0.48
    3.5 0.95 c 0.4
    3.9 1.1 f 0.3
    7.65 1.27 c 0.33
    0.73 1.016 a 1.3
    3.511 0.762 f 0.85
    3.594 0.762 f 0.85
    5.4 1.1 a 0.8
    2.6 1.1 c 0.3
    5 1.2 a 0.35
    5 1.2 a 0.35
    27 0.88 a 0.3
    97 1.1 a 0.8
    1 0.75 a 0.21
    1.2 0.8 a 0.3
    5.26 1.3 a 0.9
    3.214 1.2 c 0.7
    1.39 1.016 b 0.4
    43.9 2.0066 c 1.8
    30.54 2.0066 c 1.8
    0.544 0.7 c 0.2
    1.4301 1.5748 a 0.51
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    #578835 Reply

    @cseider I’ve put the data on my last post.

    #578900 Reply

    @mparet I tried your solution but it doesn’t work. This is what I get:

    Regression Analysis: Z versus X, Y, Categorical

    The following terms cannot be estimated and were removed:
    X*Categorical, Y*Categorical

    #579115 Reply

    You’re doing something wrong. If we take your data and check the distribution of categorical variables we get the following: a:577, b:40, c:146, d:32, e:1, f:47. That is a gross imbalance. If we drop e and call a our reference a and build a series of dummy variables we will have the following:

    a: d1 = 0, d2 = 0,d3 = 0, d4 = 0
    b: d1 = 1, d2 = 0,d3 = 0, d4 = 0
    c: d1 = 0, d2 = 1,d3 = 0, d4 = 0
    d: d1 = 0, d2 = 0,d3 = 1, d4 = 0
    f: d1 = 0, d2 = 0,d3 = 0, d4 = 1

    A check of co-linearity indicates X, Z, and the dummy variables are sufficiently independent of one another. Thus the full model is
    Full Model: Y = 11.07 -4.2*X +8.7*Z -3.6*d1 -6.2*d2 -.7*d3 -8.0*d4

    since category a is the reference the expression for the full mode for category a would be
    Y = 11.07 -4.2*X +8.7*Z
    for b it would be
    Y = 11.07 -4.2*X +8.7*Z – 3.6
    etc.

    If we run backward elimination to reduce the model to significant terms only we get
    Reduced Model: Y = 11.4 -4.9*X +8.8*Z -5.8*d2 -7.6*d4

    The associated p-values are X: P = .0006, Z: P <.0001, d2: P<.0001, d4: P<.0001, d1 and d3 are not significant which means category b and category d are dropped from the final model.

    #579123 Reply

    An additional thought. An examination of the residual plots of the full model indicate you have a problem with respect to the range of the values of Y. The pattern of residuals against predicted has a distinct < pattern. If you run the analysis against log Y the residual plot looks like it should – a shotgun blast. The other thing worth noting is that the full model and the reduced model for Y are the same – all terms are significant. Therefore I’d recommend running the analysis against log Y and then take the predicted Y and back transform to get your predicted Y value.

    #579124 Reply

    …Ok, lets try that upload of the residual plots again

    Attachments:
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Reply To: Minitab, Fit Regression Model Problem Using Categorical Variable
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