Hypothesis Testing on Proportions - Example and Solution
This video explains hypothesis testing on proportions using examples. Results show a significant difference.
00:00:00 This video provides an example and solution for hypothesis testing on proportions. It examines the effectiveness of a new medication compared to the existing one.
๐ The video discusses the concept of hypothesis testing for proportions.
๐ It uses an example of testing the effectiveness of a new medicine.
๐ข The hypothesis test involves comparing the observed proportion with the assumed proportion.
00:01:23 This video explains how to conduct a hypothesis test for proportions, using a specific example and its solution.
๐ The video discusses hypothesis testing for proportions.
๐ The alternative hypothesis is set to PH > 0.6, indicating that the new medicine is better than the current ones.
๐ The significance level is set to ฮฑ = 5% and the rejection region is determined accordingly.
00:02:44 Explanation of hypothesis testing for proportions using an example. Calculation steps and conclusion on the effectiveness of a new drug.
๐ Using the given information, we calculate the Z-score, which is 2.04.
๐ The resulting Z-score indicates that the new drug is significantly more effective.
๐ฌ Next, we discuss the hypothesis testing for two proportions and the case when a vote is not conducted.
00:04:06 A hypothesis testing example on the proportion of urban and surrounding populationโs agreement on a multipurpose building project.
๐ The video is about hypothesis testing for proportions.
๐๏ธ The example scenario involves a plan to build a multipurpose building in a city.
๐ A random sample is taken to determine if there is a significant difference in the proportion of city residents who approve the plan compared to residents around the city.
00:05:29 Example problem and explanation of hypothesis testing for proportions, determining the rejection region, and calculating the test statistic using the Z-table.
๐ Hypothesis testing for proportions involves comparing two hypotheses and determining the alternative hypothesis based on the given conditions.
๐ The significance level and the rejection region are determined to determine the critical value for hypothesis testing.
๐งฎ Calculations are performed using the Z-table to determine the rejection region and make the necessary calculations.
00:06:50 An example of hypothesis testing proportion with a calculation and explanation. Results show a significant difference.
๐ The video explains the concept of hypothesis testing for proportions in statistics.
๐ข It demonstrates how to calculate the test statistic and p-value for a given example.
โ The conclusion of the example is that the test statistic is greater than the critical value, leading to the rejection of the null hypothesis.
00:08:14 The video discusses hypothesis testing and provides an example with a solution. It concludes that the proportion of city S7's population approving the plan is greater than the proportion of people in the surrounding areas approving it.
๐ The video is about hypothesis testing for proportions.
๐ A critical value of 1.96 is used to determine the rejection region.
๐ Based on the critical value, it is concluded that the proportion of the population in city S7 who approve the plan is greater than the proportion in the surrounding city.
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