An online retailer would like to know whether the proportion of customers who visit the site and make a purchase is different than 50%. A simple random sample of customers is taken. The results of the sample are shown below. Customer sample What is the population parameter? made a purchase Observations Pick Confidence Level Critical value 1.95 What condition for using a z-distribution is met? test statistic (z) 1.8 Pick p-value 0.072 What is the level of significance? Ex: 0.12 What is the null hypothesis Ho? Pick What is the alternative hypothesis Ha? Pick Should Ho be rejected or does Ho fail to be rejected? Pick What conclusion can be drawn from the data? v evidence exists to support the claim that the number of customers who visit the site and make a purchase is not 50%. Pick

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**Hypothesis Testing for Proportions**

**Scenario:**
An online retailer wants to determine if the proportion of customers who visit the site and make a purchase differs from 50%. A simple random sample of customers is taken. The results of the sample are shown below.

**Data Summary:**

- **Customer Sample:**
  - Made a Purchase: **59**
  - Observations: **100**
  - Confidence Level: **95%**
  - Critical Value: **1.95**
  - Test Statistic (z): **1.8**
  - p-value: **0.072**

**Questions and Explanations:**

1. **What is the population parameter?**
   - The population parameter is the true proportion of customers who make a purchase after visiting the site.

2. **What condition for using a z-distribution is met?**
   - The condition is that the sample size is large enough for the normal approximation of the binomial distribution to be valid. In general, this condition is met if \(np \geq 10\) and \(n(1-p) \geq 10\).

3. **What is the level of significance?**
   - The level of significance is typically denoted by alpha (α) and could be provided or chosen based on the context. In hypothesis testing concerning proportions, common significance levels are 0.05, 0.01, etc.

4. **What is the null hypothesis \(H_0\)?**
   - The null hypothesis \(H_0\) is that the true proportion of customers who make a purchase is equal to 50%.

5. **What is the alternative hypothesis \(H_a\)?**
   - The alternative hypothesis \(H_a\) is that the true proportion of customers who make a purchase is different from 50%.

6. **Should \(H_0\) be rejected or does \(H_0\) fail to be rejected?**
   - This decision is based on the p-value. If the p-value is less than the significance level (α), then \(H_0\) should be rejected. Given the p-value is 0.072, and if the significance level α is 0.05, \(H_0\) fails to be rejected.

7. **What conclusion can be drawn from the data?**
   - Based on a p-value of 0.072, which is greater than
Transcribed Image Text:**Hypothesis Testing for Proportions** **Scenario:** An online retailer wants to determine if the proportion of customers who visit the site and make a purchase differs from 50%. A simple random sample of customers is taken. The results of the sample are shown below. **Data Summary:** - **Customer Sample:** - Made a Purchase: **59** - Observations: **100** - Confidence Level: **95%** - Critical Value: **1.95** - Test Statistic (z): **1.8** - p-value: **0.072** **Questions and Explanations:** 1. **What is the population parameter?** - The population parameter is the true proportion of customers who make a purchase after visiting the site. 2. **What condition for using a z-distribution is met?** - The condition is that the sample size is large enough for the normal approximation of the binomial distribution to be valid. In general, this condition is met if \(np \geq 10\) and \(n(1-p) \geq 10\). 3. **What is the level of significance?** - The level of significance is typically denoted by alpha (α) and could be provided or chosen based on the context. In hypothesis testing concerning proportions, common significance levels are 0.05, 0.01, etc. 4. **What is the null hypothesis \(H_0\)?** - The null hypothesis \(H_0\) is that the true proportion of customers who make a purchase is equal to 50%. 5. **What is the alternative hypothesis \(H_a\)?** - The alternative hypothesis \(H_a\) is that the true proportion of customers who make a purchase is different from 50%. 6. **Should \(H_0\) be rejected or does \(H_0\) fail to be rejected?** - This decision is based on the p-value. If the p-value is less than the significance level (α), then \(H_0\) should be rejected. Given the p-value is 0.072, and if the significance level α is 0.05, \(H_0\) fails to be rejected. 7. **What conclusion can be drawn from the data?** - Based on a p-value of 0.072, which is greater than
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