(a) Observe it scatter plot. (b) Approximate the correlation between x and y coordinates. (c) Find the linear regression model by the least square.
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- The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.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?Assume that there is a positive linear correlation between the variable R (return rate in percent of financial investment) and the variable t (age in years of the investment) given by the regression equation R = 2.5t + 5.3. 1- If the investment continues to grow at a constant rate, what is the expected return rate when the investment is 7 years old?
- Assume that there is a positive linear correlation between the variable R (return rate in percent of a financial investment) and the variable t (age in years of the investment) given by the regression equation R = 2.3t + 4.8. Without further information, can we assume there is a cause-and-effect relationship between the return rate and the age of the investment? If the investment continues to grow at a constant rate, what is the expected return rate when the investment is 7 years old? If the investment continues to grow at a constant rate, how old is the investment when the return rate is 30%?The table contains data on vehicle speed (h) and fuel consumption (lt / 100km) of 5 randomly selected vehicles. Estimate the average fuel consumption of a vehicle traveling at 45 km / h using the simple linear regression equation between vehicle speed and fuel consumption. Speed 55 60 65 70 75 Consumption 11 10 9 8 7 Please choose one: a. 6 b. 5 c. 13 D. 8In an analysis of a sample of bivariate data concerning the soil acidity x (in pH) and germination time y (in days) for tomato seeds, a linear regression model is constructed and follows the equation y^=−1.22x+5y^=−1.22x+5 Select the most appropriate statement about the linear correlation exhibited. A. The data set exhibits strong positive linear correlation. B.We cannot determine anything about the strength or direction of linear correlation from this information alone. C. The data set exhibits strong negative linear correlation. D. The linear correlation coefficient is -1.22.
- The table contains data on vehicle speed (h) and fuel consumption (It / 100km) of 5 randomly selected vehicles. Estimate the average fuel consumption of a vehicle traveling at 80 km / h using the simple linear regression equation between vehicle speed and fuel consumption. Speed 55 60 65 70 75 Consumption 11 10 9 8 7 a)7 b)6 c)12 d)8If the points (x1, y1), (x2, y2),..., (xn, yn) lie on a straight line, what can you say about the regression line associated with these points?A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x).The results of the regression were: ˆyy^=a+bxa=-1.778b=0.143 (a) Write the equation of the Least Squares Regression line of the formˆyy^= + x(b) Which is a possible value for the correlation coefficient, rr? -0.853 0.853 -1.931 1.931 (c) If a country increases its life expectancy, the happiness index will decrease increase
- The least-squares regression equation is y=761.7x+13,208 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7483. In a particular region, 26.5 percent of adults 25 years and older have at least a bachelor's degree. The median income in this region is $30,074. Is this income higher than what you would expect? Why?A regression was run to determine if for a certain baseball season, winning percentage, y, and on-base percentage, x, are linearly related. The results were: ˆ y =a+bx a=-0.4875 b=2.94 For this baseball season, the lowest on-base percentage was 0.310 and the highest was 0.362. (a) Write the equation of the Least Square Regression line. ˆ y = -0.4875 Correct + 2.94 x (b) Which is a possible value for the correlation coefficient, r? -1.143 1.235 0.72 -0.68 Correct (c) What does the slope mean in the context of this problem? (d) As the on-base percentage increases by 5 percent, the predicted winning percentage increases Correct by 2.94 Incorrect percent. (e) Would it be a good idea to use this model to predict the winning percentage of a team whose on-base percentage is 0.156? Why or why not? (f) Based on this model, what would you expect the winning percentage to be for a team with on-base percentage 0.35? (Round your answer to three decimal places.)
