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Chapter 2

Statistics for Business (Env). Chapter 2. Descriptive Statistics: Tabular and Graphical Methods. Descriptive Statistics. 2.1 Graphically Summarizing Qualitative Data 2.2 Graphically Summarizing Quantitative Data 2.3 Dot Plots 2.4 Stem-and-Leaf Displays ( Optional ) 2.6 Scatter Plots

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Chapter 2

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  1. Statistics for Business(Env) Chapter 2 Descriptive Statistics: Tabular and Graphical Methods

  2. Descriptive Statistics 2.1 Graphically Summarizing Qualitative Data 2.2 Graphically Summarizing Quantitative Data 2.3 Dot Plots 2.4 Stem-and-Leaf Displays (Optional) 2.6 Scatter Plots 2.7 Misleading Graphs and Charts (Optional)

  3. Types of Variables/data Interval Ratio Nominal Ordinal

  4. Graphically Summarizing Qualitative Data • With qualitative data, names identify the different and non-overlapping categories (classes) • This data can be summarized using a frequency distribution • Frequency distribution: A table that summarizes the number of items in each of several non-overlapping categories/classes.

  5. Example 2.1: Describing 2006 Jeep Purchasing Patterns • Table 2.1 lists all 251 vehicles sold in 2006 by the greater Cincinnati Jeep dealers • Table 2.1 does not reveal much useful information • A frequency distribution is a useful summary • Simply count the number of times each model appears in Table 2.1

  6. Table 2.1 lists all 251 vehicles sold in 2006 • by the greater Cincinnati Jeep dealers

  7. The Resulting Frequency Distribution

  8. Relative Frequency and Percent Frequency • Relative frequency summarizes the proportion of items in each class • For each class, divide the frequency of the class by the total number of observations • Multiply times 100 to obtain the percent frequency

  9. The Resulting Relative Frequency and Percent Frequency Distribution

  10. Bar Charts and Pie Charts • Bar chart: A vertical or horizontal rectangle represents the frequency for each category • Height can be frequency, relative frequency, or percent frequency • Pie chart: A circle divided into slices where the size of each slice represents its relative frequency or percent frequency

  11. Excel Bar Chart of the Jeep Sales Data

  12. Excel Pie Chart of the Jeep Sales Data Pie chart is usually for percent Frequency Distribution.

  13. Pareto Chart • Pareto chart: a type of chart that contains both bars and a line graph • A bar chart having the different kinds of categories listed on the horizontal scale • Bar height represents the frequency of occurrence • Bars are arranged in decreasing height from left to right • Augmented by plotting a cumulative percentage point for each bar

  14. Excel Frequency Table and Pareto Chart of Labeling Defects

  15. Graphically Summarizing Quantitative Data • Often need to summarize and describe the shape of the distribution • One way is to group the measurements into classes of a frequency distribution and then displaying the data in the form of a Histogram • AHistogramis a graph in which the class (numerical)midpoints or limits are marked on the horizontal axis and the class frequencies on the vertical axis.

  16. Frequency Distribution • A frequency distribution is a list of data classes with the count of values that belong to each class • “Classify and count” • The frequency distribution is a table • Show the frequency distribution in a histogram • picture of the frequency distribution (tabulated frequencies) shown as bars and the bars are drawn adjacent to each other.

  17. Constructing a Frequency Distribution Steps in making a frequency distribution: • Find the number of classes • Find the class length • Form non-overlapping classes of equal width • Tally and count • Graph the histogram

  18. Example 2.2 The Payment Time Case:A Sample of Payment Times (in days after billing) Table 2.4

  19. Determine the number of Classes • If the number of classes is • too small lack of detail • too large some classes will be empty • Group all of the n data into K number of classes • General rule: K is the smallest whole number for which 2K n • In Examples 2.2 n = 65 • For K = 6, 26 = 64, < n • For K = 7, 27 = 128, > n • So use K = 7 classes

  20. Number of Classes In General

  21. Determine the Class Length • Find the length of each class as the largest measurement minus the smallest divided by the number of classes found earlier (K) • For Example 2.2, (29-10)/7=2.7143 • Because payments measured in days, round to three days

  22. Form Non-Overlapping Classes of Equal Width • The classes start on the smallest value • This is the lower limit of the first class • The upper limit of the first class is smallest value + class length • In the example, the first class starts at 10 days and goes up to 13 days • The next class starts at this upper limit and goes up by class length • And so on

  23. Seven Non-Overlapping Classes Payment Time Example

  24. Tally and Count the Number of Measurements in Each Class

  25. Histogram • Rectangles represent the classes • The base represents the class length • The height represents • the frequency in a frequency histogram, or • the relative frequency in a relative frequency histogram

  26. Histograms Frequency Histogram Relative Frequency Histogram

  27. Example of a frequency distribution histogram for discrete data The frequency distribution of quiz scores in a class. The score, X, is the number of problems that is answered correctly.

  28. Example of a frequency distribution histogram for continuous data

  29. CONTINUOUS VARIABLES AND REAL LIMITS

  30. CONTINUOUS VARIABLES AND REAL LIMITS For a continuous variable, each score actually corresponds to an interval on the scale. The boundaries that separate these intervals are called real limits. The real limit separating two adjacent scores is located exactly halfway between the scores. Each score has two real limits, one at the top of its interval called the upper real limit and one at the bottom of its interval called the lower real limit.

  31. Some Common Distribution Shapes • Skewed to the right: The right tail of the histogram is longer than the left tail • Skewed to the left: The left tail of the histogram is longer than the right tail • Symmetrical: The right and left tails of the histogram appear to be mirror images of each other

  32. skewed to the left skewed to the right

  33. A Right-Skewed Distribution

  34. A Left-Skewed Distribution

  35. Frequency Polygons • Plot a point above each class midpoint at a height equal to the frequency of the class • Useful when comparing two or more distributions

  36. Example 2.3: Comparing The Grade Distribution for Two Statistics Exams • Table 2.8 (in textbook) gives scores earned by 40 students on first statistics exam • Table 2.9 gives the scores on the second exam after an attendance policy • Due to the way exams are reported, used the classes: 30<40, 40<50, 50<60, 60<70, 70<80, 80<90, and 90<100

  37. A Percent Frequency Polygon of the Exam Scores

  38. A Percent Frequency Polygon Comparing the Two Exam Scores

  39. Cumulative Distributions • Another way to summarize a distribution is to construct a cumulative distribution • To do this, use the same number of classes, class lengths, and class boundaries used for the frequency distribution • Rather than a count, we record the number of measurements that are less than the upper boundary of that class • In other words, a running total

  40. Frequency, Cumulative Frequency, and Cumulative Relative Frequency Distribution

  41. Ogive (Cumulative Frequency distribution) • Ogive: A graph of a cumulative distribution • Plot a point above each upper class boundary at height of cumulative frequency • Connect points with line segments • Can also be drawn using • Cumulative relative frequencies • Cumulative percent frequencies

  42. A Percent Frequency Ogive of the Payment Times

  43. Frequency Distribution For Hours Studying

  44. Cumulative Frequency Distribution For Hours Studying

  45. Dot Plots • On a number line, each data value is represented by a dot placed above the corresponding scale value • Dot plots are useful for detecting outliers • Unusually large or small observations that are well separated from the remaining observations

  46. Dot Plots Example

  47. Stem-and-Leaf Display • Purpose is to see the overall pattern of the data, by grouping the data into classes • the variation from class to class • the amount of data in each class • the distribution of the data within each class • Best for small to moderately sized data distributions

  48. A set of 24 quiz scores presented as raw data and organized in a Stem-and-Leaf Display

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