CHAPTER 1 Exploring Data

1. CHAPTER 1Exploring Data Introduction Data Analysis: Making Sense of Data

2. Data Analysis: Making Sense of Data • IDENTIFY the individuals and variables in a set of data • CLASSIFY variables as categorical or quantitative

3. Data Analysis Statisticsis the science of data. Data Analysis is the process of organizing, displaying, summarizing, and asking questions about data. • Individuals • objects described by a set of data • Variable • any characteristic of an individual • Categorical Variable • places an individual into one of several groups or categories. • Quantitative Variable • takes numerical values for which it makes sense to find an average.

4. Data Analysis A variablegenerally takes on many different values. • We are interested in how often a variable takes on each value. • Distribution • tells us what values a variable takes and how often it takes those values. Dotplot of MPG Distribution Variable of Interest: MPG

5. How to Explore Data Examine each variable by itself. Then study relationships among the variables. Start with a graph or graphs Add numerical summaries

6. From Data Analysis to Inference Population Sample Collect data from a representative Sample... Make an Inference about the Population. Perform Data Analysis, keeping probability in mind…

7. Data Analysis: Making Sense of Data • A dataset contains information on individuals. • For each individual, data give values for one or more variables. • Variables can be categorical or quantitative. • The distribution of a variable describes what values it takes and how often it takes them. • Inference is the process of making a conclusion about a population based on a sample set of data.

8. Analyzing Categorical Data • DISPLAY categorical data with a bar graph • IDENTIFY what makes some graphs of categorical data deceptive • CALCULATE and DISPLAY the marginal distribution of a categorical variable from a two-way table • CALCULATE and DISPLAY the conditional distribution of a categorical variable for a particular value of the other categorical variable in a two-way table • DESCRIBE the association between two categorical variables

9. Categorical Variables Categorical variables place individuals into one of several groups or categories. Variable Values Count Percent

10. Displaying Categorical Data Frequency tables can be difficult to read. Sometimes is is easier to analyze a distribution by displaying it with a bar graph or pie chart.

11. Graphs: Good and Bad Bar graphs compare several quantities by comparing the heights of bars that represent those quantities. Our eyes, however, react to the area of the bars as well as to their height. • When you draw a bar graph, make the bars equally wide. It is tempting to replace the bars with pictures for greater eye appeal. • Don’t do it! • There are two important lessons to keep in mind: • beware the pictograph, and • watch those scales.

12. Two-Way Tables and Marginal Distributions When a dataset involves two categorical variables, we begin by examining the counts or percents in various categories for one of the variables. A two-way table describes two categorical variables, organizing counts according to a row variable and a column variable. What are the variables described by this two-way table? How many young adults were surveyed?

13. Two-Way Tables and Marginal Distributions The marginal distribution of one of the categorical variables in a two-way table of counts is the distribution of values of that variable among all individuals described by the table. Note: Percents are often more informative than counts, especially when comparing groups of different sizes. • How to examine a marginal distribution: • Use the data in the table to calculate the marginal distribution (in percents) of the row or column totals. • Make a graph to display the marginal distribution.

14. Two-Way Tables and Marginal Distributions Examine the marginal distribution of chance of getting rich.

15. Relationships Between Categorical Variables A conditional distribution of a variable describes the values of that variable among individuals who have a specific value of another variable. • How to examine or compare conditional distributions: • Select the row(s) or column(s) of interest. • Use the data in the table to calculate the conditional distribution (in percents) of the row(s) or column(s). • Make a graph to display the conditional distribution. • Use a side-by-side bar graph or segmented bar graph to compare distributions.

16. Relationships Between Categorical Variables Calculate the conditional distribution of opinion among males. Examine the relationship between gender and opinion.

18. Relationships Between Categorical Variables Can we say there is an association between gender and opinion in the population of young adults? Making this determination requires formal inference, which will have to wait a few chapters. Caution! Even a strong association between two categorical variables can be influenced by other variables lurking in the background.

19. Data Analysis: Making Sense of Data • DISPLAY categorical data with a bar graph • IDENTIFY what makes some graphs of categorical data deceptive • CALCULATE and DISPLAY the marginal distribution of a categorical variable from a two-way table • CALCULATE and DISPLAY the conditional distribution of a categorical variable for a particular value of the other categorical variable in a two-way table • DESCRIBE the association between two categorical variables

20. Displaying Quantitative Data with Graphs • MAKE and INTERPRET dotplots and stemplots of quantitative data • DESCRIBE the overall pattern of a distribution and IDENTIFY any outliers • IDENTIFY the shape of a distribution • MAKE and INTERPRET histograms of quantitative data • COMPARE distributions of quantitative data

21. Dotplots One of the simplest graphs to construct and interpret is a dotplot. Each data value is shown as a dot above its location on a number line. • How to make a dotplot: • Draw a horizontal axis (a number line) and label it with the variable name. • Scale the axis from the minimum to the maximum value. • Mark a dot above the location on the horizontal axis corresponding to each data value.

22. Examining the Distribution of a Quantitative Variable The purpose of a graph is to help us understand the data. After you make a graph, always ask, “What do I see?” • How to Examine the Distribution of a Quantitative Variable • In any graph, look for the overall pattern and for striking departures from that pattern. • Describe the overall pattern of a distribution by its: • Shape • Center • Spread • Note individual values that fall outside the overall pattern. These departures are called outliers. Don’t forget your SOCS!

23. Describing Shape When you describe a distribution’s shape, concentrate on the main features. Look for rough symmetry or clear skewness. A distribution is roughly symmetricif the right and left sides of the graph are approximately mirror images of each other. A distribution is skewed to the right (right-skewed) if the right side of the graph (containing the half of the observations with larger values) is much longer than the left side. It is skewed to the left (left-skewed) if the left side of the graph is much longer than the right side. Symmetric Skewed-left Skewed-right

24. Comparing Distributions Some of the most interesting statistics questions involve comparing two or more groups. Always discuss shape, center, spread, and possible outliers whenever you compare distributions of a quantitative variable. Compare the distributions of household size for these two countries. Don’t forget your SOCS!

25. Stemplots Another simple graphical display for small data sets is a stemplot. (Also called a stem-and-leaf plot.) Stemplots give us a quick picture of the distribution while including the actual numerical values. • How to make a stemplot: • Separate each observation into a stem (all but the final digit) and a leaf (the final digit). • Write all possible stems from the smallest to the largest in a vertical column and draw a vertical line to the right of the column. • Write each leaf in the row to the right of its stem. • Arrange the leaves in increasing order out from the stem. • Provide a key that explains in context what the stems and leaves represent.

26. Stemplots These data represent the responses of 20 female AP Statistics students to the question, “How many pairs of shoes do you have?” Construct a stemplot. Key: 4|9 represents a female student who reported having 49 pairs of shoes. 1 93335 2 664233 3 1840 4 9 5 0701 1 33359 2 233466 3 0148 4 9 5 0017 1 2 3 4 5 Stems Add leaves Order leaves Add a key

27. Stemplots When data values are “bunched up”, we can get a better picture of the distribution by splitting stems. Two distributions of the same quantitative variable can be compared using a back-to-back stemplot with common stems. Females Males Females 333 95 4332 66 410 8 9 100 7 Males 0 4 0 555677778 1 0000124 1 2 2 2 3 3 58 4 4 5 5 0 0 1 1 2 2 3 3 4 4 5 5 “split stems” Key: 4|9 represents a student who reported having 49 pairs of shoes.

28. Histograms Quantitative variables often take many values. A graph of the distribution may be clearer if nearby values are grouped together. The most common graph of the distribution of one quantitative variable is a histogram. • How to make a histogram: • Divide the range of data into classes of equal width. • Find the count (frequency) or percent (relative frequency) of individuals in each class. • Label and scale your axes and draw the histogram. The height of the bar equals its frequency. Adjacent bars should touch, unless a class contains no individuals.

31. Histograms This table presents data on the percent of residents from each state who were born outside of the U.S. Number of States Percent of foreign-born residents

32. Using Histograms Wisely Here are several cautions based on common mistakes students make when using histograms. • Cautions! • Don’t confuse histograms and bar graphs. • Don’t use counts (in a frequency table) or percents (in a relative frequency table) as data. • Use percents instead of counts on the vertical axis when comparing distributions with different numbers of observations. • Just because a graph looks nice, it’s not necessarily a meaningful display of data.

33. Data Analysis: Making Sense of Data • MAKE and INTERPRET dotplots and stemplots of quantitative data • DESCRIBE the overall pattern of a distribution • IDENTIFY the shape of a distribution • MAKE and INTERPRET histograms of quantitative data • COMPARE distributions of quantitative data

34. Describing Quantitative Data with Numbers • CALCULATE measures of center (mean, median). • CALCULATE and INTERPRET measures of spread (range, IQR, standard deviation). • CHOOSE the most appropriate measure of center and spread in a given setting. • IDENTIFY outliers using the 1.5 × IQR rule. • MAKE and INTERPRET boxplots of quantitative data. • USE appropriate graphs and numerical summaries to compare distributions of quantitative variables.

35. Measuring Center: The Mean The most common measure of center is the ordinary arithmetic average, or mean. To find the mean (pronounced “x-bar”) of a set of observations, add their values and divide by the number of observations. If the n observations are x1, x2, x3, …, xn, their mean is: In mathematics, the capital Greek letter Σ is short for “add them all up.” Therefore, the formula for the mean can be written in more compact notation:

36. Measuring Center: The Median Another common measure of center is the median. The median describes the midpoint of a distribution. The medianis the midpoint of a distribution, the number such that half of the observations are smaller and the other half are larger. To find the median of a distribution: Arrange all observations from smallest to largest. If the number of observations n is odd, the median is the center observation in the ordered list. If the number of observations n is even, the median is the average of the two center observations in the ordered list.

37. Measuring Center Use the data below to calculate the mean and median of the commuting times (in minutes) of 20 randomly selected New York workers. 0 5 1 005555 2 0005 3 00 4 005 5 6 005 7 8 5 Key: 4|5 represents a New York worker who reported a 45-minute travel time to work.

38. Measuring Spread: The Interquartile Range (IQR) A measure of center alone can be misleading. A useful numerical description of a distribution requires both a measure of center and a measure of spread. How To Calculate The Quartiles And The IQR: To calculate the quartiles: Arrange the observations in increasing order and locate the median. The first quartile Q1is the median of the observations located to the left of the median in the ordered list. The third quartile Q3 is the median of the observations located to the right of the median in the ordered list. The interquartile range (IQR) is defined as: IQR = Q3 – Q1

39. Find and Interpret the IQR Travel times for 20 New Yorkers: Median = 22.5 Q3= 42.5 Q1= 15 IQR = Q3 – Q1 = 42.5 – 15 = 27.5 minutes Interpretation: The range of the middle half of travel times for the New Yorkers in the sample is 27.5 minutes.

40. Identifying Outliers In addition to serving as a measure of spread, the interquartile range (IQR) is used as part of a rule of thumb for identifying outliers. The 1.5 x IQR Rule for Outliers Call an observation an outlier if it falls more than 1.5 x IQR above the third quartile or below the first quartile. In the New York travel time data, we found Q1=15 minutes, Q3=42.5 minutes, and IQR=27.5 minutes. For these data, 1.5 x IQR = 1.5(27.5) = 41.25 Q1 - 1.5 x IQR = 15 – 41.25 = -26.25 Q3+ 1.5 x IQR = 42.5 + 41.25 = 83.75 Any travel time shorter than -26.25 minutes or longer than 83.75 minutes is considered an outlier. 0 5 1 005555 2 0005 3 00 4 005 5 6 005 7 8 5

41. The Five-Number Summary The minimum and maximum values alone tell us little about the distribution as a whole. Likewise, the median and quartiles tell us little about the tails of a distribution. To get a quick summary of both center and spread, combine all five numbers. The five-number summaryof a distribution consists of the smallest observation, the first quartile, the median, the third quartile, and the largest observation, written in order from smallest to largest. Minimum Q1 Median Q3 Maximum

42. Boxplots (Box-and-Whisker Plots) The five-number summary divides the distribution roughly into quarters. This leads to a new way to display quantitative data, the boxplot. • How To Make A Boxplot: • A central box is drawn from the first quartile (Q1) to the third quartile (Q3). • A line in the box marks the median. • Lines (called whiskers) extend from the box out to the smallest and largest observations that are not outliers. • Outliers are marked with a special symbol such as an asterisk (*).

43. Construct a Boxplot Consider our New York travel time data: Median = 22.5 Max=85 Recall, this is an outlier by the 1.5 x IQR rule Q1= 15 Min=5 Q3= 42.5

44. Measuring Spread: The Standard Deviation The most common measure of spread looks at how far each observation is from the mean. This measure is called the standard deviation. Consider the following data on the number of pets owned by a group of 9 children. • Calculate the mean. • Calculate each deviation. • deviation = observation – mean deviation: 1 - 5 = - 4 deviation: 8 - 5 = 3 = 5

45. Measuring Spread: The Standard Deviation 3) Square each deviation. 4) Find the “average” squared deviation. Calculate the sum of the squared deviations divided by (n-1)…this is called the variance. 5) Calculate the square root of the variance…this is the standard deviation. “average” squared deviation = 52/(9-1) = 6.5 This is the variance. Standard deviation = square root of variance =

46. Measuring Spread: The Standard Deviation The standard deviationsxmeasures the average distance of the observations from their mean. It is calculated by finding an average of the squared distances and then taking the square root. The average squared distance is called the variance.

47. Choosing Measures of Center and Spread We now have a choice between two descriptions for center and spread • Mean and Standard Deviation • Median and Interquartile Range • Choosing Measures of Center and Spread • The median and IQR are usually better than the mean and standard deviation for describing a skewed distribution or a distribution with outliers. • Use mean and standard deviation only for reasonably symmetric distributions that don’t have outliers. • NOTE: Numerical summaries do not fully describe the shape of a distribution. ALWAYS PLOT YOUR DATA!

48. Organizing a Statistical Problem As you learn more about statistics, you will be asked to solve more complex problems. Here is a four-step process you can follow. • How to Organize a Statistical Problem: A Four-Step Process • State: What’s the question that you’re trying to answer? • Plan: How will you go about answering the question? What statistical techniques does this problem call for? • Do: Make graphs and carry out needed calculations. • Conclude: Give your conclusion in the setting of the real-world problem.

49. Data Analysis: Making Sense of Data • CALCULATE measures of center (mean, median). • CALCULATE and INTERPRET measures of spread (range, IQR, standard deviation). • CHOOSE the most appropriate measure of center and spread in a given setting. • IDENTIFY outliers using the 1.5 × IQR rule. • MAKE and INTERPRET boxplots of quantitative data. • USE appropriate graphs and numerical summaries to compare distributions of quantitative variables.