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Organizing and Presenting Data

Organizing and Presenting Data. GTECH 201 Session 11. Terminology. Classes Categories for grouping data Frequency Number of observations that fall in a class (frequency is a count) Frequency Distribution A listing of all classes along with their frequencies Relative Frequency

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Organizing and Presenting Data

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  1. Organizing and Presenting Data GTECH 201 Session 11

  2. Terminology • Classes • Categories for grouping data • Frequency • Number of observations that fall in a class (frequency is a count) • Frequency Distribution • A listing of all classes along with their frequencies • Relative Frequency • The ratio of the frequency of a class to the total number of observations • Relative Frequency Distribution • A listing of all classes along with their relative frequencies • Width/Class Interval • The difference between the upper and lower cut points (breaks) of a class

  3. Organizing Data • Classification Rules • Aim is to create categories or classes • First step is to compute range • Range = Largest Value – Smallest Value • Interval or Ratio Scale data only • Class Intervals • Width of Class Interval • Equal based on range • Unequal based on range • Quantile (Quartile or Quintile) • Natural

  4. Classification Methods • Natural breaks • Quantile • Manual • Equal interval

  5. How to Decide(on a classification scheme) • Rule of thumb: 3 - 7 classes • Classification histogram (see later today)

  6. How to Decide, part II

  7. Graphs • Line graph • Bar graph • Scatterplots

  8. Creating a Line Graph • The growth of the population of students at a Midwestern university is as follows

  9. Line Graph

  10. Bar Graphs • Here are data on the percent of females among people earning doctoral degrees in 1990, in several different fields of study

  11. Bar Graph

  12. Scatter Plots • Graph bi-variate data when both variables are measured in an interval/ratio or ordinal scale • Units for one variable are marked on the horizontal axis • Independent variable should always go on the horizontal, x axis

  13. Scatterplots • Survey of 3368 people asking them to estimate number of calories in common foods.

  14. Example • A city planner collected data on the number of school age children in each of 30 families. • Construct a grouped data table using classes based on a single value

  15. Computing Frequency • There are three ways you can create classes • a < but not equal to b b < but not equal to c • a – b, c – d, e - f • single value grouping

  16. Distributions • Histograms • Difference between histograms and bar graphs • Bars in a histogram are always vertical • Base scale is marked off in equal units; there is no base scale in a bar graph • Width of bars in a histogram have meaning • Bars in a histogram touch each other

  17. Constructing a Histogram • Histogram – height of bar equal to frequency of class represented • Bar extends from lowest value to highest value of the class

  18. Histogram Chart

  19. Frequency Polygons • Similar to a histogram • Midpoint of the class is indicated • Points connected by straight lines • Cumulative frequency polygon, ogive

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