In a regression problem with 1 output variable and p input variables, we carry out a a principal components regression with p principal components constructed from the input variables. This will result in     a significant level of dimensionality reduction. PCR is one of the leading methods for dimension reduction.     no dimensionality reduction whatsoever.     a potentially good level of dimensionality reduction depending on the quality of the principal components     us making a bad choice if we care about the output variable. In that case we should have used partial least squares as a better high dimensionality reduction technique.

Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?

Ordinary least squares method was used to fit a regression model to predict income (in thousands of dollars) from the following predictors: X1 = education X2 = gender (where male = 0 and female = 1) X3 = interaction between education and gender %3D The model produced the following coefficients: B, = -11.52, B, = 2.99, ß, = 1.01, ßz = -0.94. On average, how much income do men and women get if they have 20 years of education? Key A Men: $48.28 thousand; Women: $30.49 thousand B Men: $48.28 thousand; Women: $28.08 thousand C Men: $59.80 thousand; Women: $41.00 thousand D Men: $59.80 thousand; Women: $20.20 thousand

What are the assumptions of multiple linear regressions only?