b. It is possible a beta error has been committed c. It is possible an alpha error has been committed d. Burn Rate is significant in the prediction of House Heat BTU’s e. No quadratic (squared) terms are included in this research model

b. It is possible a beta error has been committed c. It is possible an alpha error has been committed d. Burn Rate is significant in the prediction of House Heat BTU’s e. No quadratic (squared) terms are included in this research model

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.1: Measures Of Center

Problem 9PPS

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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?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. What is the slope of the population regression line?
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. The decrease in blood pressure y will vary about this subpopulation mean. What is the distribution of y for this subpopulation?
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? 0.35, which makes sense even though MARRIED? can only be a zero or one 0, because the calculated value is 0.35 so we round down. 0.55, which makes sense even though MARRIED? can only be a zero or one. Calculating a predicted value should not be done here because the dependent variable is a dummy variable. 1, because the calculated value is 0.55 so we round up.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? a 0, because the calculated value is 0.35 so we round down. b 0.55, which makes sense even though MARRIED? can only be a zero or one. c 0.35, which makes sense even though MARRIED? can only be a zero or one d Calculating a predicted value should not be done here because the dependent variable is a dummy variable. e 1, because the calculated value is 0.55 so we round up.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. Complete this sentence: For every additional year of education someone has:... a ...their chance of getting married by 40 increases by 0.03 percentage points. b ...their chance of getting married by 40 increases by 3 percentage points. c ...their chance of getting married by 40 increases by 0.03. d ...their chance of getting married by 40 increases by 3%. e This regression means nothing because the dependent variable is a dummy variable.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25.
The Tiliche Corp. analyst conducted 10 independent timing studies in the manual spray painting section of the finishing department. The product line under study revealed a direct relationship between spray painting time and product surface area. The following data were collected (ignore the rating factor): (image) To answer:(a) Find the linear model relating standard time (y) to surface area (x) using linear regression. b) How much time would you assign to spray painting a new part with a surface area of 250 in2? c) Obtain the fitted value of y and the corresponding residual for a particular assembly (study #3) with a surface area of 150 square inches. d) Perform a significance test of the regression using α= 0.05. Find the P-value for this test. What are your conclusions? e) Estimate the standard errors of the slope and the value of the intercept. f) Calculate the coefficient of determination R2 . g) Calculate the correlation coefficient r. Note: The exercise in the image is…
The Tiliche Corp. analyst conducted 10 independent timing studies in the manual spray painting section of the finishing department. The product line under study revealed a direct relationship between spray painting time and product surface area. The following data were collected (ignore the rating factor): (image) To answer:(a) Find the linear model relating standard time (y) to surface area (x) using linear regression. b) How much time would you assign to spray painting a new part with a surface area of 250 in2? c) Obtain the fitted value of y and the corresponding residual for a particular assembly (study #3) with a surface area of 150 square inches. Note: The exercise in the image is the original, it is in Spanish, but it is easy to understand.
In the above plot, a rejection of the null hypothesis that the population regression slope is equal to 0 implies: a) the correlation coefficient in the population is likely equal to 0 b) the correlation coefficient in the population is likely unequal to 0 c) the standard deviation of height is likely very large d) the standard deviation of weightis likely very small e) the correlation coefficient must be close to 1.0 f) a and c g) b and e h) b, d and e
A paper suggests that the simple linear regression model is reasonable for describing the relationship between y = eggshell thickness (in micrometers, µm) and x = egg length (mm) for quail eggs. Suppose that the population regression line is y = 0.125 + 0.007x and that ?e = 0.005. Then, for a fixed x value, y has a normal distribution with mean 0.125 + 0.007x and standard deviation 0.005. (a) What is the mean eggshell thickness for quail eggs that are 15 mm in length? ____ µm What is the mean eggshell thickness for quail eggs that are 17 mm in length? ____ µm (b) What is the probability that a quail egg with a length of 15 mm will have a shell thickness that is greater than 0.23 µm? _____ (c) Approximately what proportion of quail eggs of length 14 mm have a shell thickness of greater than 0.222? (Hint: The distribution of y at a fixed x is approximately normal. Round your answer to four decimal places.) ____ Approximately what proportion of quail eggs of length…