poiny Running heart rate Jose is a physician who is researching to what extent running affects heart rate in 18-25 year-olds. He believes that less than 50% of 18-25 year-olds will experience a heart...


poiny<br>Running heart rate<br>Jose is a physician who is researching to what extent running affects heart rate in 18-25 year-olds. He believes that less than<br>50% of 18-25 year-olds will experience a heart rate of more than 120 beats per minute (bpm) after running a mile.<br>Jose recruits a random sample of 464 people between the ages of 18 and 25 to run a mile and records their heart rate. He finds that 184 have a heart<br>rate of more than 120 bpm.<br>Round all calculated answers to 4 decimal places.<br>

Extracted text: poiny Running heart rate Jose is a physician who is researching to what extent running affects heart rate in 18-25 year-olds. He believes that less than 50% of 18-25 year-olds will experience a heart rate of more than 120 beats per minute (bpm) after running a mile. Jose recruits a random sample of 464 people between the ages of 18 and 25 to run a mile and records their heart rate. He finds that 184 have a heart rate of more than 120 bpm. Round all calculated answers to 4 decimal places.
4. Calculate the p-value.<br>5. Which of the statements below are correct interpretations of the p-value? You should choose all that are correct interpretations.<br>A. The p-value is the proportion of times in repeated sampling that the alternative hypothesis is true.<br>B. This p-value suggests that based on this sample there is strong evidence that the null model is not compatible with the data.<br>C. This p-value suggests that based on this sample there is extremely strong evidence that the null model is not compatible with the data.<br>D.<br>The p-value is the probability that the null hypothesis is true.<br>E. The p-value is the probability of obtaining a sample result at least as or more in favor of the alternative hypothesis if the null hypothesis is true.<br>F. If we repeat the hypothesis test many times, the p-value is the proportion of times our test statistic will be close to the expected value of the null<br>distribution.<br>

Extracted text: 4. Calculate the p-value. 5. Which of the statements below are correct interpretations of the p-value? You should choose all that are correct interpretations. A. The p-value is the proportion of times in repeated sampling that the alternative hypothesis is true. B. This p-value suggests that based on this sample there is strong evidence that the null model is not compatible with the data. C. This p-value suggests that based on this sample there is extremely strong evidence that the null model is not compatible with the data. D. The p-value is the probability that the null hypothesis is true. E. The p-value is the probability of obtaining a sample result at least as or more in favor of the alternative hypothesis if the null hypothesis is true. F. If we repeat the hypothesis test many times, the p-value is the proportion of times our test statistic will be close to the expected value of the null distribution.

Jun 11, 2022
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