To estimate ages of trees, rangers use a simple regression model that relates the tree diameter (inches) to their age (years). The model is useful because tree diameter has an association to tree age (count of cross section rings). 8 trees were sampled. The regression model is:                    TreeAge= -0.15228+6.451 (Diameter) d) If in fact, the tree age is 76 years for a tree with diameter 9.5 inches, the residual is approximately       ["", ""]  . The age has been a(n) [a7].

For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy

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?

To estimate ages of trees, rangers use a simple regression model that relates the tree diameter (inches) to their age (years). The model is useful because tree diameter has an association to tree age (count of cross section rings). 8 trees were sampled. The regression model is:                    TreeAge= -0.15228+6.451 (Diameter)   d) If in fact, the tree age is 76 years for a tree with diameter 9.5 inches, the residual is approximately       ["", ""]  . The age has been a(n) [a7]. e) If the correlation , then the coefficient of determination is [a8].

A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy).The results of the regression were:y=ax+b a=-0.629 b=39.045 r2=0.806404 r=-0.898 Use this to predict the number of situps a person who watches 6.5 hour(s) of TV can do, and please round your answer to a whole number.

A popular television show recently released a video preview for the upcoming season on its website. As fans of the show discover the video, the number of views of the preview video has grown each day during the last 2 weeks. The number of days since the release of the video and the natural log of the number of video views is shown in the scatterplot.     Based on the scatterplot and residual plot, what type of model would be appropriate for summarizing the relationship between days and number of views? An exponential model is appropriate because the residual plot does not show a clear pattern. A linear model is appropriate because the scatterplot shows a strong, positive linear relationship. A power model is appropriate because the scatterplot of days and natural log of views is strong and linear. An exponential model is appropriate because the relationship between days and the natural log of views is linear, and the residual plot does not show a clear pattern.

When doing linear regression, what does a large residual indicate?

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.817 b=32.111 r²=0.877969 r=-0.937 Use this to predict the number of situps a person who watches 13.5 hour(s) of TV can do, and please round your answer to a whole number.

A FIFA World Cup football is dropped from 35 different heights (in cm) and the height of the bounce is recorded (in cm.) The regression analysis gives the model bounce = -0.1 +0.70 drop. Predict the height of the bounce if dropped from 64 cm. 44.7 cm 44.8 cm 64.6 cm 91.57 cm 44.9 cm

A regression analysis was performed to determine if there is a relationship between hours ofTV watched per day r) and number of sit ups a person can do (y). The results of the regression were: у-ах+b a=-0.827 b=35.329 r2=0.540225 r=-0.735 Use this to predict the number of sit ups a person who watches 13.5 hours of TV can do, and please round your answer to a whole number.