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McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 2 Descriptive Statistics: Tabular and Graphical Methods.

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Presentation on theme: "McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 2 Descriptive Statistics: Tabular and Graphical Methods."— Presentation transcript:

1 McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 2 Descriptive Statistics: Tabular and Graphical Methods

2 2-2 Descriptive Statistics 2.1Graphically Summarizing Qualitative Data 2.2Graphically Summarizing Quantitative Data 2.3Dot Plots 2.4Stem-and-Leaf Displays 2.5Crosstabulation Tables (Optional)

3 2-3 Descriptive Statistics Continued 2.6Scatter Plots (Optional) 2.7Misleading Graphs and Charts (Optional)

4 2-4 Graphically Summarizing Qualitative Data With qualitative data, names identify the different categories 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 classes.

5 2-5 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 2-6 The Resulting Frequency Distribution Jeep ModelFrequency Commander71 Grand Cherokee70 Liberty80 Wrangler30 251

7 2-7 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

8 2-8 The Resulting Relative Frequency and Percent Frequency Distribution Jeep Model Relative Frequency Percent Frequency Commander0.282928.29% Grand Cherokee0.278927.89% Liberty0.318731.78% Wrangler0.119511.95% 1.0000100.00%

9 2-9 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

10 2-10 Excel Bar Chart of the Jeep Sales Data

11 2-11 Excel Pie Chart of the Jeep Sales Data

12 2-12 Pareto Chart Pareto chart: A bar chart having the different kinds of defects listed on the horizontal scale –Bar height represents the frequency of occurrence –Bars are arranged in decreasing height from left to right –Sometimes augmented by plotting a cumulative percentage point for each bar

13 2-13 Excel Frequency Table and Pareto Chart of Labeling Defects

14 2-14 Graphically Summarizing Qualitative 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

15 2-15 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 –The histogram is a picture of the frequency distribution

16 2-16 Constructing a Frequency Distribution Steps in making a frequency distribution: 1.Find the number of classes 2.Find the class length 3.Form non-overlapping classes of equal width 4.Tally and count 5.Graph the histogram

17 2-17 Example 2.2 The Payment Time Case: A Sample of Payment Times 2229161518171213171615 1917102115141718122014 1615162022142519231519 18232216 19131824 26 1318171524151714181721 16212519202716171621 Table 2.4

18 2-18 Number of Classes Group all of the n data into K number of classes K is the smallest whole number for which 2 K  n In Examples 2.2 n = 65 –For K = 6, 2 6 = 64, < n –For K = 7, 2 7 = 128, > n –So use K = 7 classes

19 2-19 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

20 2-20 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

21 2-21 Seven Non-Overlapping Classes Payment Time Example Class 110 days and less than 13 days Class 213 days and less than 16 days Class 316 days and less than 19 days Class 419 days and less than 22 days Class 522 days and less than 25 days Class 625 days and less than 28 days Class 728 days and less than 31 days

22 2-22 Tally and Count the Number of Measurements in Each Class Class First 4 Tally Marks All 65 Tally MarksFrequency 10 < 13|||3 13 < 16|||| |||| ||||14 16 < 19|||||| |||| |||| |||| |||23 19 < 22I|||| |||| ||12 22 < 25||||| |||8 25 < 28||||4 28 < 31|1

23 2-23 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

24 2-24 Histograms Frequency Histogram Relative Frequency Histogram

25 2-25 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

26 2-26 A Right-Skewed Distribution

27 2-27 A Left-Skewed Distribution

28 2-28 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

29 2-29 A Percent Frequency Polygon of the Exam Scores

30 2-30 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

31 2-31 Frequency, Cumulative Frequency, and Cumulative Relative Frequency Distribution ClassFrequency Cumulative Frequency Cumulative Relative Frequency Cumulative Percent Frequency 10 < 13333/65=0.04624.62% 13 < 16141717/65=0.261526.15% 16 < 1923400.615461.54% 19 < 2212520.800080.00% 22 < 258600.923192.31% 25 < 284640.984698.46% 28 < 311651.0000100.00%

32 2-32 Ogive 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

33 2-33 A Percent Frequency Ogive of the Payment Times

34 2-34 Dot Plots On a number line, each data value is represented by a dot placed above the corresponding scale value

35 2-35 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

36 2-36 Car Mileage Example Refer to the Car Mileage Case –Data in Table 2.14 The stem-and-leaf display: 29 8 30 13455677888 31 0012334444455667778899 32 01112334455778 33 03 33 + 0.3 = 33.3 29 + 0.8 = 29.8

37 2-37 Car Mileage: Results Looking at the stem-and-leaf display, the distribution appears almost “symmetrical” –The upper portion (29, 30, 31) is almost a mirror image of the lower portion of the display (31, 32, 33) Stems 31, 32*, 32, and 33* –But not exactly a mirror reflection

38 2-38 Constructing a Stem-and-Leaf Display There are no rules that dictate the number of stem values Can split the stems as needed

39 2-39 Split Stems from Car Mileage Example Starred classes (*) extend from 0.0 to 0.4 Unstarred classes extend from 0.5 to 09 29 8 30* 134 30 55677888 31* 00123344444 31 55667778899 32* 011123344 32 55778 33* 03

40 2-40 Crosstabulation Tables (Optional) Classifies data on two dimensions –Rows classify according to one dimension –Columns classify according to a second dimension Requires three variable 1.The row variable 2.The column variable 3.The variable counted in the cells

41 2-41 Bond Fund Satisfaction Survey Data in Table 2.16 Fund TypeHighMediumLowTotal Bond Fund1512330 Stock Fund244230 Tax Deferred Annuity 1241540 Total40 20100

42 2-42 More on Crosstabulation Tables Row totals provide a frequency distribution for the different fund types Column totals provide a frequency distribution for the different satisfaction levels Main purpose is to investigate possible relationships between variables

43 2-43 Percentages One way to investigate relationships is to compute row and column percentages –Compute row percentages by dividing each cell’s frequency by its row total and expressing as a percentage –Compute column percentages by dividing by the column total

44 2-44 Row Percentage for Each Fund Type Data in Table 2.16 Fund TypeHighMediumLowTotal Bond Fund50.0%40.0%10.0%100% Stock Fund80.0%13.3%6.7%100% Tax Deferred Annuity 2.5%60.0%37.5%100%

45 2-45 Types of Variables In the bond fund example, we crosstabulated two qualitative variables Can use a quantitative variable versus a qualitative variable or two quantitative variables With quantitative variables, often define categories

46 2-46 Scatter Plots (Optional) Used to study relationships between two variables Place one variable on the x-axis Place a second variable on the y-axis Place dot on pair coordinates

47 2-47 Types of Relationships Linear: A straight line relationship between the two variables Positive: When one variable goes up, the other variable goes up Negative: When one variable goes up, the other variable goes down No Linear Relationship: There is no coordinated linear movement between the two variables

48 2-48 A Scatter Plot Showing a Linear Relationship

49 2-49 Misleading Graphs and Charts: Scale Break Mean Salaries at a Major University, 2004 - 2007 Break the vertical scale to exaggerate effect

50 2-50 Misleading Graphs and Charts: Horizontal Scale Effects Mean Salary Increases at a Major University, 2004 - 2007 Compress vs. stretch the horizontal scales to exaggerate or minimize the effect

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