HIC Measurements Listed below are head injury criterion (HIC) measurements from crash tests of small, midsize, large, and SUV vehicles. In using the Kruskal-Wallis test, we must rank all of the data combined, and then we must find the sum of the ranks for each sample. Find the sum of the ranks for each of the four samples.
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- 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 12m
- 9. Hypothesis Testing for One Sample3h 29m
- 10. Hypothesis Testing for Two Samples4h 50m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- 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 6m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit1h 57m
- 14. ANOVA1h 57m
9. Hypothesis Testing for One Sample
Steps in Hypothesis Testing
Problem 13.3.2e
Textbook Question
Hospital Admissions For the matched pairs listed in Exercise 1, identify the following components used in the Wilcoxon signed-ranks test:
e. The value of the test statistic T
Verified step by step guidance1
Identify the matched pairs data provided in Exercise 1. The Wilcoxon signed-ranks test is used for paired data to test whether the median difference between pairs is zero.
Calculate the differences between the paired values. For each pair, subtract one value from the other (e.g., difference = value1 - value2).
Ignore the signs of the differences and rank the absolute values of the differences from smallest to largest. Assign ranks to these absolute differences, resolving ties by assigning the average rank to tied values.
Reapply the original signs of the differences to their corresponding ranks. This step ensures that the ranks reflect the direction (positive or negative) of the differences.
Sum the ranks of the positive differences and the ranks of the negative differences separately. The test statistic T is the smaller of these two sums (positive ranks sum or negative ranks sum).
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Wilcoxon Signed-Ranks Test
The Wilcoxon signed-ranks test is a non-parametric statistical test used to compare two related samples or matched pairs. It assesses whether their population mean ranks differ, making it suitable for data that do not meet the assumptions of normality required for parametric tests. This test is particularly useful in situations where the sample size is small or when the data are ordinal.
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Step 2: Calculate Test Statistic
Test Statistic T
In the context of the Wilcoxon signed-ranks test, the test statistic T represents the sum of the ranks of the positive differences between paired observations. This statistic is calculated after ranking the absolute differences between pairs, allowing researchers to determine the significance of the observed differences. A smaller T value indicates a stronger evidence against the null hypothesis of no difference.
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Step 2: Calculate Test Statistic
Matched Pairs Design
A matched pairs design is a statistical approach where subjects are paired based on certain characteristics, ensuring that each pair is similar in relevant aspects. This design helps control for variability and allows for a more accurate comparison of outcomes between the two conditions being studied. It is commonly used in experiments where the same subjects are measured under different conditions or treatments.
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Introduction to Matched Pairs
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