A marketing manager wants to evaluate whether three different advertising platforms-TV, social media, and print media-lead to different average sales performance across regional stores. She runs a 4-week advertising campaign, assigning one platform to a group of 5 stores each (15 stores total). After the campaign, she collects the average weekly sales (in \$1,000s) for each store during the campaign period. She wants to determine whether there is a statistically significant difference in mean sales among the three advertising platforms. State the null & alternative hypotheses for a one-way ANOVA test.
Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 1m
- 3. Describing Data Numerically2h 8m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables3h 28m
- 6. Normal Distribution & Continuous Random Variables2h 21m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 37m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals22m
- Confidence Intervals for Population Mean1h 26m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 33m
- 9. Hypothesis Testing for One Sample3h 32m
- 10. Hypothesis Testing for Two Samples4h 49m
- Two Proportions1h 12m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 2m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 24m
- 12. Regression1h 59m
- 13. Chi-Square Tests & Goodness of Fit2h 31m
- 14. ANOVA2h 1m
14. ANOVA
Introduction to ANOVA
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Four different high schools in local towns took random samples of 100 students in three grades, 10th−12th and collected data on the weekly time spent studying to see if students in each of these grades study on average for the same amount of time per week. The four schools ran ANOVA tests on their samples, and the F-Statistics were 2.35, 2.57, 2.81, and 3.93. Which F-Statistic is most likely to indicate the average study times across grades are not all the same?
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B
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D
Verified step by step guidance1
Step 1: Understand the purpose of the ANOVA test. ANOVA (Analysis of Variance) is used to determine if there are statistically significant differences between the means of three or more groups. In this case, it is used to test if the average study times across grades are the same or not.
Step 2: Recall the role of the F-Statistic in ANOVA. The F-Statistic is a ratio of the variance between group means to the variance within the groups. A higher F-Statistic indicates a greater likelihood that at least one group mean is significantly different from the others.
Step 3: Compare the given F-Statistics. The F-Statistics provided are 2.35, 2.57, 2.81, and 3.93. The higher the F-Statistic, the stronger the evidence against the null hypothesis (which assumes all group means are equal).
Step 4: Identify the F-Statistic most likely to indicate that the average study times are not all the same. Among the given values, 3.93 is the highest F-Statistic, suggesting the strongest evidence to reject the null hypothesis.
Step 5: Conclude that the F-Statistic of 3.93 is most likely to indicate that the average study times across grades are not all the same, as it provides the strongest evidence against the null hypothesis.
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