Y = [98, 112.5, 84, 102.5, 102.5, 50.5, 90, 77, 112, 150, 128, 133, 85, 112];

X = [65.3, 69, 56.5, 62.8, 63.5, 51.3, 64.3, 56.3, 66.5, 72, 64.8, 67, 57.5, 66.5];

X = J( N Row( X ), 1 ) || X; // 切片の列に1を入れる

beta = Inv( X` * X ) * X` * Y; // 最小2乗推定

resid = Y - X * beta; // 残差、Y - 推定値

sse = resid` * resid; // 誤差平方和

Show( beta, sse );

// データテーブルを開く

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );

// データを行列に移す

x = (Column( "年齢" ) << Get Values ) || (Column( "身長(インチ)" ) << Get Values);

x = J(N Row( x ), 1, 1) || x;

y = Column( "体重(ポンド)" ) << Get Values;

// 回帰分析の計算

xpxi = Inv( x` * x );

beta = xpxi * x` * y; // パラメータ推定値

resid = y - x * beta; // 残差

sse = resid` * resid; // 誤差平方和

dfe = N Row( x ) - N Col( x );  // 自由度

mse = sse / dfe; // 誤差の平均平方、誤差分散推定値

// 推定値の追加計算

stdb = Sqrt( Vec Diag( xpxi ) * mse ); // 推定値の標準誤差

alpha = .05;

qt = Students t Quantile( 1 - alpha / 2, dfe );

betau95 = beta + qt * stdb; // 上側95％信頼限界

betal95 = beta - qt * stdb; // 下側95％信頼限界

tratio = beta :/ stdb; // Studentのt値

probt = ( 1 - TDistribution( Abs( tratio ), dfe ) ) * 2; // p値

// 結果の表示

New Window( "ビッグ クラスの回帰分析",

	Table Box(

		String Col Box( "項",{"切片", "年齢", "身長(インチ)"} ),

		Number Col Box( "推定値", beta ),

		Number Col Box( "標準誤差", stdb ),

		Number Col Box( "t値", tratio ),

		Number Col Box( "p値", probt ),

		Number Col Box( "下側95%", betal95 ),

		Number Col Box( "上側95%", betau95 ) ) );

factor = [1, 2, 3, 1, 2, 3, 1, 2, 3];

y = [1, 2, 3, 4, 3, 2, 5, 4, 3];

Design Nom( factor );

x = J( 9, 1, 1) || Design Nom( factor );

M = ( x` * x || x` * y )

        |/

  ( y` * x || y` * y );

FullFit = Sweep( M, [1, 2, 3] ); // フルモデルのあてはめ

InterceptOnly = Sweep( M, [1] ); // 切片のみのモデル

dt = New Table( "Data" );

dt << New Column( "y", Set Values( [1, 2, 3, 4, 3, 2, 5, 4, 3] ) );

dt << New Column( "factor", "Nominal", Values( [1, 2, 3, 1, 2, 3, 1, 2, 3] ) );

obj = Fit Model(

	Y( :y ),

	Effects( :factor ),

	Personality( "Standard Least Squares" ),

	Run(

		y << {Plot Actual by Predicted( 0 ), Plot Residual by Predicted( 0 ),

		Plot Effect Leverage( 0 )}

	)

);

ranova = obj << Report;

ranova[Outline Box( 6 )] << Close( 0 );

ranova[Outline Box( 7 )] << Close( 1 );

ranova[Outline Box( 9 )] << Close( 1 );

ranova[Outline Box( 5 ), Number Col Box( 2 )] << Select;

ranova[Outline Box( 6 ), Number Col Box( 1 )] << Select;