Using R for Introductory Statistics : John Verzani

Using R for introductory statistics 284Table 10.3 Number of bedrooms and sale priceof a home in thousandsprice $300 $250 $400 $550 $317 $389 $425 $289 $389bedrooms 3 3 4 5 4 3 6 3 410.7 The more beer you drink, the more your blood alcohol level (BAL) rises. Table 10.4contains a data set on beer consumption. Make a scatterplot with a regression line and95% prediction intervals drawn. Test the hypothesis that one beer raises your BAL by0.02% against the alternative that it raises it less.10.8 For the same blood-alcohol data, do a significance test that the intercept is 0 witha two-sided alternative.Table 10.4 Beer consumption and blood alcohollevelbeers 5 2 9 8 3 7 3 5 3 ~BAL 0.10 0.03 0.19 0.12 0.04 0.095 0.07 0.06 0.02 0.0510.9 The lapse rate is the rate at which temperature drops as you increase elevation. Somehardy students were interested in checking empirically whether the lapse rate of 9.8°C/km was accurate. To investigate, they grabbed their thermometers and their Suuntowrist altimeters and recorded the data in Table 10.5 on their hike. Draw a scatterplot withregression line and investigate whether the lapse rate is 9.8 °C/km/km. (It helps toconvert to the rate of change °F per feet, which is 5.34 degrees per 1,000 feet.) Test thehypothesis that the lapse rate is 5.34 degrees per 1,000 feet against a two-sidedalternative.Table 10.5 Elevation and temperaturemeasurementselevation (ft) 600 1000 1250 1600 1800 2100 2500 2900temperature (°F) 56 54 56 50 47 49 47 4510.10 A seal population is counted over a ten-year period. The counts are reported inTable 10.6. Make a scatterplot and find the regression line. What is the predicted valuefor 1963? Would you use this to predict the population in 2004? Why or why not?Table 10.6 Seal population from 1952 to 1962year pop. year pop. year pop year pop1952 724 1955 1,392 1958 1,212 1961 1,9801953 176 1956 1,392 1959 1,672 1962 2,1161954 920 1957 1,448 1960 2,06810.11 For the homedata (UsingR) data set, find the regression equation to predict theyear-2000 value of a home from its year-1970 value. Make a prediction for an $80,000