Lecture 5 Hypothesis Testing in Multiple Linear Regression
Lecture 5Hypothesis Testing in Multiple LinearRegressionBIOST 515January 20, 20041Types of tests Overall test Test for addition of a single variable Test for addition of a group of variables2Overall testyi= 0+xi1 1+ +xip p+ iDoes theentireset of independent variables contributesignificantly to the prediction ofy?3Test for an addition of a single variableDoes the addition ofoneparticular variable of interest addsignificantly to the prediction ofyacheived by the otherindependent variables already in the model?yi= 0+xi1 1+ +xip p+ i4Test for addition of a group of variablesDoes the addition of somegroupof independent variables ofinterest add significantly to the prediction ofyobtainedthrough other independent variables already in the model?yi= 0+xi1 1+ +xi,p 1 p 1+xip p+ i5The ANOVA tableSource ofSums of squaresDegrees ofMeanE[Mean square]variationfreedomsquareRegressionS SR= X y n y2pSSRpp 2+ RX CXC RErrorSSE=y y X y n (p+ 1)SSEn (p+1) 2TotalSST O=y y n y2n 1XCis the matrix of centered predictors:XC=0BB@x11 x1x12 x2 x1p xpx21 x1x22 x2 x2p x1xn2 x2 xnp xp1CCAand R= ( 1, , p).
The regression sums of squares due to X2 when X1 is already in the model is SSR(X2|X1) = SSR(X)−SSR(X1) with r degrees of freedom. This is also known as the extra sum of squares due to X2. SSR(X2|X1) is independent of MSE. We can test H 0: β2 = 0 with the statistic F 0 = SSR(X2|X1)/r MSE ∼ F r,n−p−1.
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