The image contains five hypothesis testing problems where you're asked to set up the null hypothesis ($H_0$) and the alternative hypothesis ($H_a$) for each scenario. Below is a detailed solution:

1. Online Retailer - More Than 90% of Visitors Make a Purchase

• $H_0$: The proportion of visitors who make a purchase is less than or equal to 90%. $H_0: p \leq 0.90$
• $H_a$: The proportion of visitors who make a purchase is greater than 90%. $H_a: p > 0.90$

2. Soda Machine - Dispenses 500 Milliliters Per Bottle

• $H_0$: The mean amount of soda dispensed is 500 milliliters. $H_0: \mu = 500 \text{ mL}$
• $H_a$: The mean amount of soda dispensed is not 500 milliliters. $H_a: \mu \neq 500 \text{ mL}$

3. Coffee Advertisement - Amount of Pure Milk in a Cup of Coffee

• $H_0$: The mean amount of pure milk in a cup of coffee is 0.1 liters. $H_0: \mu = 0.1 \text{ L}$
• $H_a$: The mean amount of pure milk in a cup of coffee is not 0.1 liters. $H_a: \mu \neq 0.1 \text{ L}$

4. Marketers - At Least 85% of Adults Own a Cell Phone

• $H_0$: The proportion of adults who own a cell phone is less than 85%. $H_0: p < 0.85$
• $H_a$: The proportion of adults who own a cell phone is at least 85%. $H_a: p \geq 0.85$

5. Researcher - Students Spend an Average of 23 Hours Preparing for Final Exams

• $H_0$: The mean time spent preparing for final exams is 23 hours. $H_0: \mu = 23 \text{ hours}$
• $H_a$: The mean time spent preparing for final exams is not 23 hours. $H_a: \mu \neq 23 \text{ hours}$

Would you like more details on any of these setups or have any other questions?

Here are 5 questions you might consider based on the content:

1. How do you determine whether to use a one-tailed or two-tailed test in hypothesis testing?
2. What is the significance level in hypothesis testing, and how is it chosen?
3. Can you explain Type I and Type II errors in the context of hypothesis testing?
4. How do you calculate the test statistic for a proportion test?
5. What are the steps involved in performing a hypothesis test from start to finish?

Tip: Always clearly define your null and alternative hypotheses before conducting any statistical test to ensure the correct interpretation of results.