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 56m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 17m
- 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 - ExcelBonus23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - ExcelBonus28m
- Confidence Intervals for Population Means - ExcelBonus25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 8m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - ExcelBonus42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - ExcelBonus27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 37m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - ExcelBonus28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - ExcelBonus12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - ExcelBonus9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - ExcelBonus21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - ExcelBonus12m
- Two Variances and F Distribution29m
- Two Variances - Graphing CalculatorBonus16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - ExcelBonus8m
- Finding Residuals and Creating Residual Plots - ExcelBonus11m
- Inferences for Slope31m
- Enabling Data Analysis ToolpakBonus1m
- Regression Readout of the Data Analysis Toolpak - ExcelBonus21m
- Prediction Intervals13m
- Prediction Intervals - ExcelBonus19m
- Multiple Regression - ExcelBonus29m
- Quadratic Regression15m
- Quadratic Regression - ExcelBonus10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 29m
9. Hypothesis Testing for One Sample
Steps in Hypothesis Testing
Problem 13.2.10
Textbook Question
In Exercises 9–12, use the sign test for the claim involving nominal data.
Medical Malpractice In a study of 1228 randomly selected medical malpractice lawsuits, it was found that 856 of them were dropped or dismissed (based on data from the Physicians Insurers Association of America). Use a 0.01 significance level to test the claim that there is a difference between the rate of medical malpractice lawsuits that go to trial and the rate of such lawsuits that are dropped or dismissed.
Verified step by step guidance1
Step 1: Understand the problem and identify the hypothesis. The claim is that there is a difference between the rate of medical malpractice lawsuits that go to trial and the rate of those that are dropped or dismissed. This is a two-tailed test because we are testing for a difference, not a specific direction.
Step 2: Define the null and alternative hypotheses. The null hypothesis (H₀) is that the proportion of lawsuits dropped or dismissed is equal to the proportion of lawsuits that go to trial. The alternative hypothesis (H₁) is that the proportions are not equal. In mathematical terms: H₀: p = 0.5 and H₁: p ≠ 0.5.
Step 3: Calculate the test statistic. The sign test is based on the number of positive and negative differences. Here, the number of lawsuits dropped or dismissed is 856, and the total number of lawsuits is 1228. The number of lawsuits that go to trial is 1228 - 856 = 372. Use the smaller of these two values (372) as the test statistic.
Step 4: Determine the critical value. Since the significance level is 0.01 and this is a two-tailed test, divide the significance level by 2 (0.01 / 2 = 0.005) to find the critical region in each tail. Use a binomial distribution table or normal approximation (if applicable) to find the critical value for the given sample size and significance level.
Step 5: Compare the test statistic to the critical value and make a decision. If the test statistic falls within the critical region, reject the null hypothesis (H₀). Otherwise, fail to reject the null hypothesis. Conclude whether there is sufficient evidence to support the claim that there is a difference between the rates of lawsuits that go to trial and those that are dropped or dismissed.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Sign Test
The sign test is a non-parametric statistical method used to evaluate the median of a single sample or to compare two related samples. It is particularly useful for nominal data, where the data can be categorized into two groups. The test counts the number of positive and negative signs in the data, allowing researchers to determine if there is a significant difference between the groups without assuming a normal distribution.
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Step 2: Calculate Test Statistic
Significance Level
The significance level, often denoted as alpha (α), is the threshold used to determine whether to reject the null hypothesis in hypothesis testing. A significance level of 0.01 indicates that there is a 1% risk of concluding that a difference exists when there is none (Type I error). In this context, it means that the results must be very strong to be considered statistically significant.
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Step 4: State Conclusion Example 4
Nominal Data
Nominal data is a type of categorical data that represents distinct categories without any inherent order or ranking. Examples include gender, race, or the outcome of a lawsuit (dropped or dismissed). In statistical analysis, nominal data is often analyzed using non-parametric tests like the sign test, as traditional parametric tests require interval or ratio data.
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Visualizing Qualitative vs. Quantitative Data
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