Concept explainers
The data given in the previous exercise on x = Call-to- shock time (in minutes) and y = Survival rate (percent) were used to compute the equation of the least-squares line, which was
ŷ = 101.33 − 9.30x
The newspaper article “FDA OKs Use of Home Defibrillators” (San Luis Obispo Tribune, November 13, 2002) reported that “every minute spent waiting for paramedics to arrive with a defibrillator lowers the chance of survival by 10 percent.” Is this statement consistent with the given least-squares line? Explain.
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Chapter 5 Solutions
Introduction to Statistics and Data Analysis
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Elementary Statistics: Picturing the World (7th Edition)
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- Moisture content in percent by volume (x) and conductivity in mS/m (y) were measured for 50 soil specimens. The means and standard deviations were = 8.1, sx=1.2, y = 30.4, sy=1.9. The correlation between conductivity and moisture was computed to be r= 0.810. Find the equation of the least-squares line for predicting soil conductivity from moisture content. (Round the final answers to three decimal places.) y = Xarrow_forwardA study was conducted to assess the relationship between students’s score in final exam (y) and number of hours spent for exam (x) in each day. Data on a random sample 20 students were obtained and a regression model was estimated; and the least squares estimates obtained are: intercept a=28.5 and slope b=4.3 with SE(b)=Sb=0.017. The SS are: TSS=2540 and ESS=850. ****** QA) What is the difference between exam score obtained by two students one who studied 5 hours and the other who studied 9 hours per day. QB) In the above Question 1, find 95% CI for the slope and interpret it. In the above Question 1, find and interpret the coefficient of determination (r-square value).arrow_forwardA student studying statistics examined whether a relationship exists between the city miles per gallon (mpg) and highway miles per gallon (mpg) for cars and trucks. The miles per gallon of a vehicle describe the typical number of miles the vehicle can drive on one gallon of gas. Using a random sampleof 50 cars and trucks, the student obtained the least squares regression equation: predicted highway mpg = 1.14 .(city mpg) + 5.8 a. Predict the highway mpg for a vehicle that gets an average of 24 miles per gallon when driving in the city. b. Describe the meaning of the number 1.14 in the regression equation above?arrow_forward
- Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (F), as the explanatory variable and the number of ice bags sold that day as the response variable. The least squares regression (LSR) line for the data is Bags = -151.05 +2.65Temp. On one of the observed days, the temperature was 82 °F and 68 bags of ice were sold. Determine the number of bags of ice predicted to be sold by the LSR line, Bags, when the temperature is (82\ \text (°F. J\\) Enter your answer as a whole number, rounding if necessary. Bags = 1.11 residual Incorrect Using the predicted value you just found, compute the residual at this temperature. 1.11 Incorrect ice bags ice bagsarrow_forwardIsabelle is a crime scene investigator. She found a footprint at the site of a recent murder and believes the footprint belongs to the culprit. To help identify possible suspects, she is investigating the relationship between a person's height and the length of his or her footprint. She consulted her agency's database and found cases in which detectives had recorded the length of people's footprints, x, and their heights (in centimetres), y. The least squares regression line of this data set is: y = 2.488x + 114.001 omplete the following sentence: The least squares regression line predicts that someone whose footprint is one centimetre longer should be centimetres taller.arrow_forwardIn a particular manufacturing process, the useful life of a cutting tool is linearly related to the speed at which the tool is operated. The data in the accompanying table were derived from life tests for the two different brands of cutting tools currently used in the production process. For which brand would you feel more confident using the least squares line to predict useful life for a given cutting speed? E Click the icon to view the data. Since the standard deviation (s = for Brand B is than the standard deviation for Brand A (s = ), Brand B would be a predictor for the useful life for a given cutting speed. (Type an integer or a decimal rounded to three decimal places.) - X Data Table Useful Life (hours) Cutting Speed (meters per minute) Brand A Brand B 30.0 4.7 6.0 30.0 3.5 6.5 30.0 5.4 5.0 40.0 5.4 6.0 40.0 4.0 4.5 40.0 2.5 5.0 50.0 4.4 4.5 50.0 2.8 4.0 50.0 1.0 3.6 60.0 4.0 3.7 60.0 2.0 3.0 60.0 1.1 2.4 70.0 1.5 1.5 70.0 0.5 2.0 70.0 4.0 1.0arrow_forward
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