The dotplot below shows the number of books read in a month by students in a college class. Find the most and least frequent number of books read.
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 53m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample2h 19m
- 10. Hypothesis Testing for Two Samples3h 22m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
2. Describing Data with Tables and Graphs
Dot Plots
Problem 2.R.8
Textbook Question
In Exercises 7 and 8, use the data set shown in the table at the left, which represents the pollution indices (a unitless measure of pollution ranging from 0 to 100) for 24 U.S. cities. (Adapted from Numbeo)

Use a dot plot to display the data set. Describe any patterns.

1
Step 1: Understand the data set. The table contains pollution indices for 24 U.S. cities, ranging from 0 to 100. Each value represents a unitless measure of pollution.
Step 2: Organize the data. Extract all the pollution indices from the table and list them in ascending order to make it easier to plot the dot plot. For example: [20, 23, 23, 25, 27, 29, 30, 30, 31, 31, 32, 36, 37, 39, 39, 42, 43, 44, 45, 45, 45, 47, 50, 52, 63].
Step 3: Create a dot plot. On a horizontal axis, label the range of pollution indices (e.g., 20 to 63). For each pollution index value, place a dot above the corresponding number on the axis. If a value appears multiple times, stack the dots vertically.
Step 4: Analyze the dot plot. Look for patterns such as clustering of values, gaps, or outliers. For example, you might notice that most pollution indices are clustered between 30 and 50, with fewer cities having indices below 30 or above 50.
Step 5: Describe the patterns. Based on the dot plot, summarize the distribution of pollution indices. For instance, you might note that the data is moderately spread out, with a concentration of values in the mid-range (30-50), and a few cities with lower or higher pollution indices.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Dot Plot
A dot plot is a simple graphical display used to represent the frequency of data points in a dataset. Each value is represented by a dot above a number line, allowing for easy visualization of the distribution and frequency of the data. It is particularly useful for small to moderate-sized datasets, as it clearly shows clusters, gaps, and outliers.
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Descriptive Statistics
Descriptive statistics summarize and describe the main features of a dataset. This includes measures such as mean, median, mode, range, and standard deviation, which provide insights into the central tendency and variability of the data. Understanding these statistics is essential for interpreting the patterns observed in the dot plot.
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Patterns in Data
Identifying patterns in data involves looking for trends, clusters, or anomalies within the dataset. In the context of pollution indices, one might observe whether pollution levels are generally high or low, if there are any outliers, or if certain ranges of pollution indices are more common. Recognizing these patterns helps in understanding the overall environmental conditions represented by the data.
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