A company making covid tests is putting advertisements for the covid test on various websites. The company using two versions of the advertisements version A had more “information” than version B which means version had a lower focus on emotional appeal The first variable is “Version: Which version of the advertisement is placed on a webpage version A or version B” The second variable is “clicks: number of clicks” • What is a suitable graphical display for investigating the relationship between the variables • Just considering the version A test the claim the mean is different to 200

For investigating the relationship between two variables, where one is categorical (Version A or Version B) and the other is numerical (number of clicks), suitable graphical displays include: 1. **Bar Chart**: This could show the average number of clicks for each version of the advertisement. It's useful for comparing the mean clicks between the two versions. 2. **Box Plot**: This would show the distribution of clicks for each version, including the median, quartiles, and potential outliers. It's useful for comparing the central tendency and variability between the two versions. 3. **Violin Plot**: Similar to a box plot but also includes a kernel density estimation. It's useful for comparing the distribution shapes between the two versions along with central tendency and variability. Regarding the claim that the mean number of clicks for version A is different from 200, a hypothesis test is needed to determine if there is a statistically significant difference between the observed mean of clicks for version A and the hypothesized mean (200 clicks). The steps to test this claim would be: 1. **State the Hypotheses**: - Null hypothesis (H0): The mean number of clicks for version A is 200. - Alternative hypothesis (H1): The mean number of clicks for version A is not 200. 2. **Choose a Significance Level**: Typically, a 5% significance level (α = 0.05) is used, but this can vary depending on the context. 3. **Collect Data**: Obtain the click data for version A of the advertisement. 4. **Calculate the Test Statistic**: Use the sample data to calculate the mean and standard deviation. Then compute the test statistic under the assumption that H0 is true. For a large sample size, use a z-test; for a smaller sample size, use a t-test. 5. **Determine the P-Value**: The p-value tells you the probability of seeing the observed effect (or more extreme) if the null hypothesis is true. 6. **Make a Decision**: If the p-value is less than the significance level, reject the null hypothesis; otherwise, do not reject it. 7. **Interpret the Results**: Based on the decision, conclude whether or not there is evidence to support the claim that the mean number of clicks is different from 200. To perform the hypothesis test, you would need the actual data on the number of clicks for version A. If you have that data, statistical software or even a spreadsheet can be used to conduct the test and create the visualizations.