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STA 291 Fall 2009

STA 291 Fall 2009. Lecture 4 Dustin Lueker. Bar Graph (Nominal/Ordinal Data). Histogram: for interval (quantitative) data Bar graph is almost the same, but for qualitative data Difference:

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STA 291 Fall 2009

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  1. STA 291Fall 2009 Lecture 4 Dustin Lueker

  2. Bar Graph (Nominal/Ordinal Data) • Histogram: for interval (quantitative) data • Bar graph is almost the same, but for qualitative data • Difference: • The bars are usually separated to emphasize that the variable is categorical rather than quantitative • For nominal variables (no natural ordering), order the bars by frequency, except possibly for a category “other” that is always last STA 291 Fall 2009 Lecture 4

  3. Pie Chart(Nominal/Ordinal Data) • First Step • Create a frequency distribution STA 291 Fall 2009 Lecture 4

  4. We could display this data in a bar chart… • Bar graph • If the data is ordinal, classes are presented in the natural ordering STA 291 Fall 2009 Lecture 4

  5. Pie Chart • Pie is divided into slices • Area of each slice is proportional to the frequency of each class STA 291 Fall 2009 Lecture 4

  6. Pie Chart for Highest Degree Achieved STA 291 Fall 2009 Lecture 4

  7. Stem and Leaf Plot • Write the observations ordered from smallest to largest • Looks like a histogram sideways • Contains more information than a histogram, because every single observation can be recovered • Each observation represented by a stem and leaf • Stem = leading digit(s) • Leaf = final digit STA 291 Fall 2009 Lecture 4 7

  8. Stem and Leaf Plot Stem Leaf # 20 3 1 19 18 17 16 15 14 13 135 3 12 7 1 11 334469 6 10 2234 4 9 08 2 8 03469 5 7 5 1 6 034689 6 5 0238 4 4 46 2 3 0144468999 10 2 039 3 1 67 2 ----+----+----+----+ STA 291 Fall 2009 Lecture 4 8

  9. Stem and Leaf Plot • Useful for small data sets • Less than 100 observations • Practical problem • What if the variable is measured on a continuous scale, with measurements like 1267.298, 1987.208, 2098.089, 1199.082 etc. • Use common sense when choosing “stem” and “leaf” • Can also be used to compare groups • Back-to-Back Stem and Leaf Plots, using the same stems for both groups. • Murder Rate Data from U.S. and Canada • Note: it doesn’t really matter whether the smallest stem is at top or bottom of the table STA 291 Fall 2009 Lecture 4 9

  10. PRESIDENT AGE PRESIDENT AGE PRESIDENT AGE Washington 67 Fillmore 74 Roosevelt 60 Adams 90 Pierce 64 Taft 72 Jefferson 83 Buchanan 77 Wilson 67 Madison 85 Lincoln 56 Harding 57 Monroe 73 Johnson 66 Coolidge 60 Adams 80 Grant 63 Hoover 90 Jackson 78 Hayes 70 Roosevelt 63 Van Buren 79 Garfield 49 Truman 88 Harrison 68 Arthur 56 Eisenhower 78 Tyler 71 Cleveland 71 Kennedy 46 Polk 53 Harrison 67 Johnson 64 Taylor 65 McKinley 58 Nixon 81 Stem and Leaf Plot Ford 93 Reagan 93 STA 291 Fall 2009 Lecture 4 10

  11. Summary of Graphical and Tabular Techniques • Discrete data • Frequency distribution • Continuous data • Grouped frequency distribution • Small data sets • Stem and leaf plot • Interval data • Histogram • Categorical data • Bar chart • Pie chart • Grouping intervals should be of same length, but may be dictated more by subject-matter considerations STA 291 Fall 2009 Lecture 4 11

  12. Good Graphics • Present large data sets concisely and coherently • Can replace a thousand words and still be clearly understood and comprehended • Encourage the viewer to compare two or more variables • Do not replace substance by form • Do not distort what the data reveal STA 291 Fall 2009 Lecture 4 12

  13. Bad Graphics • Don’t have a scale on the axis • Have a misleading caption • Distort by using absolute values where relative/proportional values are more appropriate • Distort by stretching/shrinking the vertical or horizontal axis • Use bar charts with bars of unequal width STA 291 Fall 2009 Lecture 4 13

  14. Sample/Population Distribution • Frequency distributions and histograms exist for the population as well as for the sample • Population distribution vs. sample distribution • As the sample size increases, the sample distribution looks more and more like the population distribution STA 291 Fall 2009 Lecture 4 14

  15. Population Distribution • The population distribution for a continuous variable is usually represented by a smooth curve • Like a histogram that gets finer and finer • Similar to the idea of using smaller and smaller rectangles to calculate the area under a curve when learning how to integrate • Symmetric distributions • Bell-shaped • U-shaped • Uniform • Not symmetric distributions: • Left-skewed • Right-skewed • Skewed STA 291 Fall 2009 Lecture 4 15

  16. Skewness Symmetric Right-skewed Left-skewed STA 291 Fall 2009 Lecture 4

  17. Summarizing Data Numerically • Center of the data • Mean • Median • Mode • Dispersion of the data • Sometimes referred to as spread • Variance, Standard deviation • Interquartile range • Range STA 291 Fall 2009 Lecture 4

  18. Measures of Central Tendency • Mean • Arithmetic average • Median • Midpoint of the observations when they are arranged in order • Smallest to largest • Mode • Most frequently occurring value STA 291 Fall 2009 Lecture 4

  19. Sample Mean • Sample size n • Observations x1, x2, …, xn • Sample Mean “x-bar” STA 291 Fall 2009 Lecture 4

  20. Population Mean • Population size N • Observations x1 , x2 ,…, xN • Population Mean “mu” • Note: This is for a finite population of size N STA 291 Fall 2009 Lecture 4

  21. Mean • Requires numerical values • Only appropriate for quantitative data • Does not make sense to compute the mean for nominal variables • Can be calculated for ordinal variables, but this does not always make sense • Should be careful when using the mean on ordinal variables • Example “Weather” (on an ordinal scale) Sun=1, Partly Cloudy=2, Cloudy=3, Rain=4, Thunderstorm=5 Mean (average) weather=2.8 • Another example is “GPA = 3.8” is also a mean of observations measured on an ordinal scale STA 291 Fall 2009 Lecture 4

  22. Mean • Center of gravity for the data set • Sum of the values above the mean is equal to the sum of the values below the mean STA 291 Fall 2009 Lecture 4

  23. Mean (Average) • Mean • Sum of observations divided by the number of observations • Example • {7, 12, 11, 18} • Mean = STA 291 Fall 2009 Lecture 4

  24. Mean • Highly influenced by outliers • Data points that are far from the rest of the data • Not representative of a typical observation if the distribution of the data is highly skewed • Example • Monthly income for five people 1,000 2,000 3,000 4,000 100,000 • Average monthly income = • Not representative of a typical observation STA 291 Fall 2009 Lecture 4

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