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.
📚 The video discusses the concept of hypothesis testing for proportions.
💊 It uses an example of testing the effectiveness of a new medicine.
🔢 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.
🔑 The video discusses hypothesis testing for proportions.
🔑 The alternative hypothesis is set to PH > 0.6, indicating that the new medicine is better than the current ones.
🔑 The significance level is set to α = 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.
🔑 Using the given information, we calculate the Z-score, which is 2.04.
📊 The resulting Z-score indicates that the new drug is significantly more effective.
🔬 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 population’s agreement on a multipurpose building project.
📚 The video is about hypothesis testing for proportions.
🏙️ The example scenario involves a plan to build a multipurpose building in a city.
📊 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.
📚 Hypothesis testing for proportions involves comparing two hypotheses and determining the alternative hypothesis based on the given conditions.
🔍 The significance level and the rejection region are determined to determine the critical value for hypothesis testing.
🧮 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.
📚 The video explains the concept of hypothesis testing for proportions in statistics.
🔢 It demonstrates how to calculate the test statistic and p-value for a given example.
✅ 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.
😊 The video is about hypothesis testing for proportions.
🔍 A critical value of 1.96 is used to determine the rejection region.
📊 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.
Want to deep dive into this video?
