the scores of students on the math portion of a college entrance exam. Based on data from the administrator of the exam, scores are normally distributed with μ=525. The teacher obtains a random sample of 2200 students, puts them through the reviewclass, and finds that the mean math score of the 2200 students is 530 with a standard deviation of 112. Complete parts (a) through (d) below. LOADING... Click the icon to view the t-Distribution Area in the Right Tail. (a) State the null and alternative hypotheses. Let μ be the mean score. Choose the correct answer below. A. H0: μ<525, H1: μ>525 B. H0: μ=525, H1: μ>525 C. H0: μ>525, H1: μ≠525 D. H0: μ=525, H1: μ≠525 (b) Test the hypothesis at the α=0.10 level of significance. Is a mean math score of 530 statistically significantly higher than 525? Conduct a hypothesis test using the P-value approach. Find the test statistic. t0= (Round to two decimal places as needed.) Approximate the P-value corresponding to the critical value of t. The P-value is . Is the sample mean statistically significantly higher? Yes No (c) Do you think that a mean math score of 530 versus 525 will affect the decision of a school admissions administrator? In other words, does the increase in the score have any practical significance? No, because the score became only 0.95% greater. Yes, because every increase in score is practically significant. (d) Test the hypothesis at the α=0.10 level of significance with n=350 students. Assume that the sample mean is still 530 and the sample standard deviation is still 112. Is a sample mean of 530 significantly more than 525? Conduct a hypothesis test using the P-value approach. Find the test statistic. t0= (Round to two decimal places as needed.) Approximate the P-value corresponding to the critical value of t. The P-value is Is the sample mean statistically significantly higher? Yes No What do you conclude about the impact of large samples on the P-value? A. As n increases, the likelihood of rejecting the null hypothesis increases. However, large samples tend to overemphasize practically significant differences. B. As n increases, the likelihood of rejecting the null hypothesis increases. However, large samples tend to overemphasize practically insignificant differences. C. As n increases, the likelihood of not rejecting the null hypothesis increases. However, large samples tend to overemphasize practically significant differences. D. As n increases, the likelihood of not rejecting the null hypothesis increases. However, large samples tend to overemphasize practically insignificant difference
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
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