Descriptive Statistics: Frequency Distributions & Graphs

1. Chapter 2 Descriptive Statistics Larson/Farber 4th ed.

2. Chapter Outline • 2.1 Frequency Distributions and Their Graphs • 2.2 More Graphs and Displays • 2.3 Measures of Central Tendency • 2.4 Measures of Variation • 2.5 Measures of Position Larson/Farber 4th ed.

3. Overview Descriptive Statistics • Describes the important characteristics of a set of data. • Organize, present, and summarize data: 1. Graphically 2. Numerically Larson/Farber 4th ed. 3

4. Important Characteristics of Quantitative Data “Shape, Center, and Spread” • Center: A representative or average value that indicates where the middle of the data set is located. • Variation: A measure of the amount that the values vary among themselves. • Distribution: The nature or shape of the distribution of data (such as bell-shaped, uniform, or skewed).

5. Overview 2.1 Frequency Distributions and Their Graphs 2.2 More Graphs and Displays 2.3 Measures of Central Tendency 2.4 Measures of Variation 2.5 Measures of Position Larson/Farber 4th ed. 5

6. Section 2.1 Frequency Distributions and Their Graphs Larson/Farber 4th ed.

7. Frequency Distributions Frequency Distribution • A table that organizes data values into classes or intervals along with number of values that fall in each class (frequency, f). • Ungrouped Frequency Distribution – for data sets with few different values. Each value is in its own class. • Grouped Frequency Distribution: for data sets with many different values, which are grouped together in the classes.

8. Grouped and UngroupedFrequency Distributions Ungrouped Grouped

9. Ungrouped Frequency Distributions

10. Graphs of Frequency Distributions:Frequency Histograms frequency data values Frequency Histogram A bar graph that represents the frequency distribution. The horizontal scale is quantitative and measures the data values. The vertical scale measures the frequencies of the classes. Consecutive bars must touch. Larson/Farber 4th ed. 10

11. Frequency Histogram Ex. Peas per Pod

12. Relative Frequency Distributions and Relative Frequency Histograms Relative Frequency Histogram • Has the same shape and the same horizontal scale as the corresponding frequency histogram. • The vertical scale measures the relative frequencies, not frequencies. Relative Frequency Distribution • Shows the portion or percentage of the data that falls in a particular class. 12

13. Relative Frequency Histogram Has the same shape and horizontal scale as a histogram, but the vertical scale is marked with relative frequencies.

14. Grouped Frequency Distributions Grouped Frequency Distribution • For data sets with many different values. • Groups data into 5-20 classes of equal width.

15. Grouped Frequency Distribution Terms • Lower class limits: are the smallest numbers that can actually belong to different classes • Upper class limits: are the largest numbers that can actually belong to different classes • Class width: is the difference between two consecutive lower class limits 15

16. Labeling Grouped Frequency Distributions • Class midpoints: the value halfway between LCL and UCL: • Class boundaries: the value halfway between an UCL and the next LCL

17. Constructing a Grouped Frequency Distribution • Determine the range of the data: • Range = highest data value – lowest data value • May round up to the next convenient number • Decide on the number of classes. • Usually between 5 and 20; otherwise, it may be difficult to detect any patterns. • Find the class width: • . • Round up to the next convenient number.

18. Constructing a Frequency Distribution • Find the class limits. • Choose the first LCL: use the minimum data entry or something smaller that is convenient. • Find the remaining LCLs: add the class width to the lower limit of the preceding class. • Find the UCLs: Remember that classes must cover all data values and cannot overlap. • Find the frequencies for each class. (You may add a tally column first and make a tally mark for each data value in the class). Larson/Farber 4th ed.

19. “Shape” of Distributions Symmetric • Data is symmetric if the left half of its histogram is roughly a mirror image of its right half. Skewed • Data is skewed if it is not symmetric and if it extends more to one side than the other. Uniform • Data is uniform if it is equally distributed (on a histogram, all the bars are the same height or approximately the same height).

20. The Shape of Distributions Symmetric Uniform Skewed left Skewed Right

21. Outliers Unusual data values as compared to the rest of the set. They may be distinguished by gaps in a histogram. Outliers

22. Section 2.2 More Graphs and Displays Larson/Farber 4th ed.

23. Other Graphs Besides Histograms, there are other methods of graphing quantitative data: • Stem and Leaf Plots • Dot Plots • Time Series

24. Stem and Leaf Plots Represents data by separating each data value into two parts: the stem (such as the leftmost digit) and the leaf (such as the rightmost digit) Larson/Farber 4th ed. 24

25. Constructing Stem and Leaf Plots • Split each data value at the same place value to form the stem and a leaf. (Want 5-20 stems). • Arrange all possible stems vertically so there are no missing stems. • Write each leaf to the right of its stem, in order. • Create a key to recreate the data. • Variations of stem plots: • Split stems • Back to back stem plots. Larson/Farber 4th ed.

26. Constructing a Stem-and-Leaf Plot Include a key to identify the values of the data. Larson/Farber 4th ed.

27. Figure 2-5 Dot Plots Dot plot • Consists of a graph in which each data value is plotted as a point along a scale of values

28. Time Series (Paired data) Quantitative data time Time Series • Data set is composed of quantitative entries taken at regular intervals over a period of time. • e.g., The amount of precipitation measured each day for one month. • Use a time series chart to graph. Larson/Farber 4th ed. 28

29. Figure 2-8 Time-Series Graph Number of Screens at Drive-In Movies Theaters Ex. www.eia.doe.gov/oil_gas/petroleum/

30. Frequency Categories Graphing Qualitative Data Sets Pie Chart • A circle is divided into sectors that represent categories. Pareto Chart • A vertical bar graph in which the height of each bar represents frequency or relative frequency. Larson/Farber 4th ed.

31. Constructing a Pie Chart • Find the total sample size. • Convert the frequencies to relative frequencies (percent). Total: 219.7

32. Figure 2-6 Constructing Pareto Charts • Create a bar for each category, where the height of the bar can represent frequency or relative frequency. • The bars are often positioned in order of decreasing height, with the tallest bar positioned at the left.

33. Section 2.3 Measures of Central Tendency Larson/Farber 4th ed.

34. Measures of Central Tendency Measure of central tendency • A value that represents a typical, or central, entry of a data set. • Most common measures of central tendency: • Mean • Median • Mode Larson/Farber 4th ed.

35. Measure of Central Tendency: Mean Mean : The sum of all the data entries divided by the number of entries. • Population mean: • Sample mean: • Round-off rule for measures of center: Carry one more decimal place than is in the original values. Do not round until the last step.

36. Measure of Central Tendency: Median Median • The value that lies in the middle of the data when the data set is arranged in order from lowest to highest. . • Measures the center of an ordered data set by dividing it into two equal parts. • A sample mean is often referred to as x. • If the data set has an • odd number of entries: median is the middle data entry. • even number of entries: median is the mean of the two middle data entries. ~ Larson/Farber 4th ed.

37. Computing the Median 2 5 6 11 13 median is the exact middle value: 2 5 6 7 11 13 median is the mean of the by two numbers: If the data set has an: • odd number of entries: median is the middle data entry: • even number of entries: median is the mean of the two middle data entries: 37

38. Measure of Central Tendency: Mode Mode • The data entry that occurs with the greatest frequency. • If no entry is repeated the data set has no mode. • If two entries occur with the same greatest frequency, each entry is a mode (bimodal). a) 5.40 1.10 0.42 0.73 0.48 1.10 b) 27 27 27 55 55 55 88 88 99 c) 1 2 3 6 7 8 9 10 • Mode is 1.10 • Bimodal - 27 & 55 • No Mode

39. Comparing the Mean, Median, and Mode • All three measures describe an “average”. Choose the one that best represents a “typical” value in the set. • Mean: • The most familiar average. • A reliable measure because it takes into account every entry of a data set. • May be greatly affected by outliers or skew. • Median: • A common average. • Not as effected by skew or outliers. • Mode: May be used if there is an overwhelming repeat.

40. Choosing the “Best Average” The shape of your data and the existence of any outliers may help you choose the best average:

41. Section 2.4 Measures of Variation Larson/Farber 4th ed.

42. Measures of Variation (“Spread”) Another important characteristic of quantitative data is how much the data varies, or is spread out. The 2 most common method of measuring spread are: • Range • Standard deviation and Variance Larson/Farber 4th ed. 42

43. Range Range • The difference between the maximum and minimum data entries in the set. • The data must be quantitative. • Range = (Max. data entry) – (Min. data entry) Larson/Farber 4th ed.

44. Example: Finding the Range The wait time to see a bank teller is studied at 2 banks. Bank A has multiple lines, one for each teller. Bank B has a single wait line for 1st available teller. 5 wait times (in minutes) are sampled from each bank: Bank A: 5.2 6.2 7.5 8.4 9.2 Bank B: 6.6 6.8 7.5 7.7 7.9 Find the mean, median, and range for each bank.

45. Solution: Finding the Range • Bank A: Range = ? • Bank B: Range = ? • Note: The range is easy to compute, but only uses 2 values. Do the following 2 sets vary the same? • Set A: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 • Set B: 1, 10, 10, 10, 10, 10, 10, 10, 10, 10 Larson/Farber 4th ed.

46. Standard Deviation and Variance Measures the typical amount data deviates from the mean. Sample Variance, : Sample Standard Deviation, s: 46

47. Finding Sample Variance & Standard Deviation • Find the mean of the sample data set. • Find deviation of each entry. • Square each deviation. • Add to get the sum of the deviations squared. • Divide by n – 1 to get the sample variance. • Find the square root to get the sample standard deviation. 47

48. Find the Standard Deviation and Variance for Bank A (multi-line) Σ(x – x) = • Round to one more decimal than the data. • Don’t round until the end. • Include the appropriate units.

49. Find the Standard Deviation and Variance for Bank B (1 wait line) Σ(x – x) = • Round to one more decimal than the data. • Don’t round until the end. • Include the appropriate units.

50. Sample versus Population Standard Deviation and Variance Sample Population Statistics: Parameters: Mean x µ Standard sσ Deviation Variance s2σ2