Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
2. Describing Data with Tables and Graphs
Frequency Distributions
Problem 2.c.1
Textbook Question
In Exercises 1–5, use the data listed in the margin, which are magnitudes (Richter scale) and depths (km) of earthquakes from Data Set 24 “Earthquakes” in Appendix B

Frequency Distribution Construct a frequency distribution of the magnitudes. Use a class width of 0.50 and use a starting value of 1.00.

1
Step 1: Identify the range of magnitudes in the dataset. Look at the smallest and largest values in the 'Magnitude' column from the table provided. This will help determine the number of classes needed for the frequency distribution.
Step 2: Determine the class width. The problem specifies a class width of 0.50, and the starting value is 1.00. Create intervals starting from 1.00, such as [1.00–1.50), [1.50–2.00), [2.00–2.50), and so on, until the largest magnitude is covered.
Step 3: Count the number of magnitudes that fall into each interval. For each interval, go through the 'Magnitude' column and tally how many values fall within that range. Ensure that the intervals are non-overlapping and exhaustive.
Step 4: Construct the frequency distribution table. Create a table with two columns: one for the intervals (e.g., [1.00–1.50), [1.50–2.00), etc.) and another for the frequency (the count of magnitudes in each interval).
Step 5: Verify the frequency distribution. Ensure that the sum of the frequencies equals the total number of magnitudes in the dataset. This confirms that all data points have been accounted for.

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