Statistics Unlocking the Power of Data Lock 5 Body Temperature Bootstrap

Statistics unlocking the power of data lock 5 body

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Statistics: Unlocking the Power of Data Lock 5 Body Temperature Bootstrap Distribution 98.26 98.4 Randomization Distribution H 0 : = 98.4 H a : ≠ 98.4
Statistics: Unlocking the Power of Data Lock 5 Intervals and Tests If a 95% CI misses the parameter in H 0 , then a two-tailed test should reject H 0 at a 5% significance level. If a 95% CI contains the parameter in H 0 , then a two-tailed test should not reject H 0 at a 5% significance level.
Statistics: Unlocking the Power of Data Lock 5 Intervals and Tests A confidence interval represents the range of plausible values for the population parameter If the null hypothesized value IS NOT within the CI, it is not a plausible value and should be rejected If the null hypothesized value IS within the CI, it is a plausible value and should not be rejected
Statistics: Unlocking the Power of Data Lock 5 Using bootstrapping, we found a 95% confidence interval for the mean body temperature to be (98.05 , 98.47 ) This does not contain 98.6 , so at = 0.05 α we would reject H 0 for the hypotheses H 0 : = 98.6 H a : ≠ 98.6 Body Temperatures
Statistics: Unlocking the Power of Data Lock 5 Both Father and Mother “Does a child need both a father and a mother to grow up happily?” α . als-parenthood-trumps-marriage/#fn-7199-1
Statistics: Unlocking the Power of Data Lock 5 Both Father and Mother “Does a child need both a father and a mother to grow up happily?” Let p be the proportion of adults aged 18-29 in 1997 who say yes. A 95% CI for p is (0.533, 0.607). Testing H 0 : p = 0.5 vs H a : p ≠ 0.5 with = 0.05, we α reject H 0 or do not reject H 0 ? Reject H 0 ; 0.5 is not within the CI, so is not a plausible value for p . als-parenthood-trumps-marriage/#fn-7199-1
Statistics: Unlocking the Power of Data Lock 5 Intervals and Tests Confidence intervals are most useful when you want to estimate population parameters Hypothesis tests and p-values are most useful when you want to test hypotheses about population parameters Confidence intervals give you a range of plausible values; p-values quantify the strength of evidence against the null hypothesis
Statistics: Unlocking the Power of Data Lock 5

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