Blue Crabs A marine researcher claims that the stomachs of blue crabs from one location contain more fish than the stomachs of blue crabs from another location. The stomach contents of a sample of 25 blue crabs from Location A contain a mean of 320 milligrams of fish and a standard deviation of 60 milligrams. The stomach contents of a sample of 15 blue crabs from Location B contain a mean of 280 milligrams of fish and a standard deviation of 80 milligrams. At , α= 0.01can you support the marine researcher’s claim? Assume the population variances are equal.
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 25m
- 9. Hypothesis Testing for One Sample3h 57m
- 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 24m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA1h 57m
10. Hypothesis Testing for Two Samples
Two Means - Unknown, Unequal Variance
Problem 8.RE.2
Textbook Question
In Exercises 1–4, classify the two samples as independent or dependent and justify your answer.
Sample 1: The weights of 45 oranges
Sample 2: The weights of 40 grapefruits
Verified step by step guidance1
Step 1: Understand the concept of independent and dependent samples. Independent samples are those where the observations in one sample do not influence or are not related to the observations in the other sample. Dependent samples, on the other hand, involve paired or related observations, such as before-and-after measurements or matched pairs.
Step 2: Analyze the given samples. Sample 1 consists of the weights of 45 oranges, and Sample 2 consists of the weights of 40 grapefruits. Consider whether there is any inherent relationship or pairing between the weights of the oranges and the weights of the grapefruits.
Step 3: Determine if the samples are paired or related. Since the weights of the oranges and grapefruits are measured independently and there is no indication that the oranges and grapefruits are matched or paired in any way, the samples are likely independent.
Step 4: Justify the classification. The samples are independent because the weights of the oranges do not influence or depend on the weights of the grapefruits, and there is no pairing or relationship between the two groups.
Step 5: Conclude the classification. Based on the analysis, classify the two samples as independent and provide reasoning that supports this conclusion.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Independent Samples
Independent samples refer to two or more groups that are not related or influenced by each other. In statistical analysis, this means that the selection or outcome of one sample does not affect the other. For example, if you measure the weights of oranges and grapefruits from different sources, the results from one do not impact the results from the other.
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Dependent Samples
Dependent samples, also known as paired samples, occur when the samples are related or matched in some way. This can happen when the same subjects are measured under different conditions or at different times. For instance, if you measured the weights of the same oranges before and after a specific treatment, those samples would be dependent.
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Justification in Statistical Analysis
Justification in statistical analysis involves providing reasoning or evidence to support the classification of samples as independent or dependent. This includes explaining the relationship between the samples, such as whether they come from different populations or if they are linked through a common factor. Clear justification is essential for ensuring the validity of the statistical methods applied.
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