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winning time and year is positive because the slope is negative. winning time and year is positive because the slope is positive. winning time and year is negative because the slope is negative. al winning time for the gold medal was 41.83 seconds. Use the reg 094. (Round your answer to two decimal places.)

winning time and year is positive because the slope is negative. winning time and year is positive because the slope is positive. winning time and year is negative because the slope is negative. al winning time for the gold medal was 41.83 seconds. Use the reg 094. (Round your answer to two decimal places.)

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Suppose the winning time in an Olympic race over the years 1924 to 1992 can be described by the regression equation below. Winning time = 261 0.1096 (year) (a) Is the relationship between winning time and year positive or negative? Explain. The correlation between winning time and year is negative because the slope is positive. The correlation between winning time and year is positive because the slope is negative. O The correlation between winning time and year is positive because the slope is positive. O The correlation between winning time and year is negative because the slope is negative. (b) In 1994, suppose the actual winning time for the gold medal was 41.83 seconds. Use the regression equation to predict the winning time for 1994. (Round your answer to two decimal places.) S Compare the prediction to what actually happened. The actual winning time was the same as the predicted time. O The actual winning time was slower than the predicted time. O The actual winning time was faster than the predicted time. (c) Explain what the slope of -0.1096 indicates in terms of how winning times change from year to year. Winning times decrease, on average, by 0.1096 years per second. O Winning times increase, on average, by 0.1096 years per second. O Winning times increase, on average, by 0.1096 seconds per year. O Winning times decrease, on average, by 0.1096 seconds per year. (d) Why should we not use this regression equation to predict the winning time in the 2050 Olympics? O The data used for the regression equation were for the years 1924 to 1992. Extrapolating as far beyond this range of years as 2050 could be extremely misleading. It is expected that the winning times will taper off eventually. The human body has limits and the winning times cannot keep decreasing at the same rate forever. O The data used for the regression equation were for the years 1924 to 1992. Extrapolating beyond this range of years is the very reason that this type of analysis is done. If the relationship is linear and accurate now, there is no reason to believe that it will change in the future. The winning times will keep decreasing at the same rate until 2050.
Transcribed Image Text:Suppose the winning time in an Olympic race over the years 1924 to 1992 can be described by the regression equation below. Winning time = 261 0.1096 (year) (a) Is the relationship between winning time and year positive or negative? Explain. The correlation between winning time and year is negative because the slope is positive. The correlation between winning time and year is positive because the slope is negative. O The correlation between winning time and year is positive because the slope is positive. O The correlation between winning time and year is negative because the slope is negative. (b) In 1994, suppose the actual winning time for the gold medal was 41.83 seconds. Use the regression equation to predict the winning time for 1994. (Round your answer to two decimal places.) S Compare the prediction to what actually happened. The actual winning time was the same as the predicted time. O The actual winning time was slower than the predicted time. O The actual winning time was faster than the predicted time. (c) Explain what the slope of -0.1096 indicates in terms of how winning times change from year to year. Winning times decrease, on average, by 0.1096 years per second. O Winning times increase, on average, by 0.1096 years per second. O Winning times increase, on average, by 0.1096 seconds per year. O Winning times decrease, on average, by 0.1096 seconds per year. (d) Why should we not use this regression equation to predict the winning time in the 2050 Olympics? O The data used for the regression equation were for the years 1924 to 1992. Extrapolating as far beyond this range of years as 2050 could be extremely misleading. It is expected that the winning times will taper off eventually. The human body has limits and the winning times cannot keep decreasing at the same rate forever. O The data used for the regression equation were for the years 1924 to 1992. Extrapolating beyond this range of years is the very reason that this type of analysis is done. If the relationship is linear and accurate now, there is no reason to believe that it will change in the future. The winning times will keep decreasing at the same rate until 2050.
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