Concept explainers
Critical Thinking: Interpreting Computer Printouts Refer to the description of a computer display for regression described in Problem 5. The following Minitab display gives information regarding the relationship between the body weight of a child (in kilograms) and the metabolic rate of the child (in 100 kcal/24 hr). The data are based on information from The Merck Manual (a commonly used reference in medical schools and nursing programs).
Predictor | Coef | SE Coef | T | P |
Constant | 0.8565 | 0.4148 | 2.06 | 0.084 |
Weight | 0.40248 | 0.02978 | 13.52 | 0.000 |
s = 0.517508 R-Sq = 96.8%
(a) Write out the least-squares equation.
(b) For each 1-kilogram increase in weight, how much does the metabolic rate of a child increase?
(c) What is the value of the sample
(d) Interpretation What percentage of the variation in y can be explained by the corresponding variation in x and the least-squares line? What percentage is unexplained?
For Problems 7-18, please do the following.
(a) Draw a
(b) Verify the given sums
(c) Find
(d) Graph the least-squares line on your scatter diagram. Be sure to use the point
(e) Interpretation Find the value of the coefficient of determination
Answers may vary slightly due to rounding.
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Chapter 4 Solutions
Understanding Basic Statistics
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- Cable TV The following table shows the number C. in millions, of basic subscribers to cable TV in the indicated year These data are from the Statistical Abstract of the United States. Year 1975 1980 1985 1990 1995 2000 C 9.8 17.5 35.4 50.5 60.6 60.6 a. Use regression to find a logistic model for these data. b. By what annual percentage would you expect the number of cable subscribers to grow in the absence of limiting factors? c. The estimated number of subscribers in 2005 was 65.3million. What light does this shed on the model you found in part a?arrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.1 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.0 278.0 253.1 258.8 279.0 258.0 274.4 262.2 Height 1785.3 1771.2 1675.9 1646.3 1859.2 1710.4 1789.2 1737.2 The regression equation is y=+x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 273.1 mm is mm. (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? OA. The result is exactly the same as the actual height of 1776 mm. OB. The result is very different from the actual height of 1776 mm. OC. The result is close to the actual height of 1776 mm. OD. The result does not make sense given the context of the data.arrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.9 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.2 277.8 Height 1785.3 1771.2 253.0 259.2 278.7 258.0 274.1 261.8 1675.9 1646.2 1858.9 1709.6 1788.8 1737.0 + C The regression equation is = y X. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.9 mm is mm. (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? A. The result is very different from the actual height of 1776 mm. B. The result is exactly the same as the actual height of 1776 mm. C. The result is close to the actual height of 1776 mm. D. The result does not make sense given the context of the data.arrow_forward
- Answer the given problem. Process the data in excel and present the results. A researcher is interested in knowing how well he can predict blood pressure from sodium intake. 1. Formulate the regression equation for the data. 2. What would be the likely blood pressure reading for a patient with a sodium intake of 6.0? of 8.5? Patients Sodium BP 1 6.5 151 2 7.3 170 3 6.6 165 4 7.5 172 5 7.6 187 6 6.7 161 7 6.7 169 8 7.8 192 9 7 185 10 7.4 190 T 11 6.2 145 12 6.7 OL 143 R E S6.7arrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.2 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.9 253.1 259.3 279.2 258.1 274.2 261.8 Height 1784.8 1771.3 1676.2 1646.1 1858.7 1710.1 1789.3 1736.7 Question content area bottom Part 1 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) Part 2 The best predicted height of a male with a foot length of 273.2 mm is enter your response here mm. (Round to the nearest integer as needed.) Part 3 How does the result compare to the actual height of 1776 mm? A. The result is close to the actual height of 1776 mm. B. The…arrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.8 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.3 252.9 258.7 279.2 258.0 274.2 262.3 Height 1785.0 1771.0 1675.9 1646.2 1858.8 1709.6 1788.7 1736.6 The regression equation is ŷ= + (x y= (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) YouTube no New Helluva Boss Recommended Zarrow_forward
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