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Frequency Distributions
- 1. Chapter 2 Frequency Distributions PowerPointLecture Slides Essentials of Statistics for the Behavioral Sciences Eighth Edition by Frederick J Gravetter and Larry B. Wallnau
- 2. Learning Outcomes • Understandhow frequency distributions are used1 • Organize data into a frequency distribution table…2 • …and into a grouped frequency distribution table3 • Know how to interpret frequency distributions4 • Organize data into frequency distribution graphs5 • Know how to interpret and understand graphs6
- 3. Tools You WillNeed • Proportions (math review, Appendix A) – Fractions – Decimals – Percentages • Scales of measurement (Chapter 1) – Nominal, ordinal, interval, and ratio – Continuous and discrete variables (Chapter 1) • Real limits (Chapter 1)
- 4. 2.1 Frequency Distributions •A frequency distribution is – An organized tabulation – Showing the number of individuals located in each category on the scale of measurement • Can be either a table or a graph • Always shows – The categories that make up the scale – The frequency, or number of individuals, in each category
- 5. 2.2 Frequency DistributionTables • Structure of Frequency Distribution Table – Categories in a column (often ordered from highest to lowest but could be reversed) – Frequency count next to category • Σf = N • To compute ΣX from a table – Convert table back to original scores or – Compute ΣfX
- 6. Proportions and Percentages Proportions •Measures the fraction of the total group that is associated with each score • • Called relative frequencies because they describe the frequency ( f ) in relation to the total number (N) Percentages N f pproportion • Expresses relative frequency out of 100 • • Can be included as a separate column in a frequency distribution table )100()100( N f ppercentage
- 7. Example 2.3 Frequency, Proportionand Percent X f p = f/N percent = p(100) 5 1 1/10 = .10 10% 4 2 2/10 = .20 20% 3 3 3/10 = .30 30% 2 3 3/10 = .30 30% 1 1 1/10 = .10 10%
- 8. Learning Check • Usethe Frequency Distribution Table to determine how many subjects were in the study • 10A • 15B • 33C • Impossible to determineD X f 5 2 4 4 3 1 2 0 1 3
- 9. Learning Check -Answer • Use the Frequency Distribution Table to determine how many subjects were in the study • 10A • 15B • 33C • Impossible to determineD X f 5 2 4 4 3 1 2 0 1 3
- 10. Learning Check • Forthe frequency distribution shown, is each of these statements True or False? • More than 50% of the individuals scored above 3T/F • The proportion of scores in the lowest category was p = 3T/F X f 5 2 4 4 3 1 2 0 1 3
- 11. Learning Check -Answer • For the frequency distribution shown, is each of these statements True or False? • Six out of ten individuals scored above 3 = 60% = more than halfTrue • A proportion is a fractional part; 3 out of 10 scores = 3/10 = .3 False X f 5 2 4 4 3 1 2 0 1 3
- 12. Grouped Frequency Distribution Tables •If the number of categories is very large they are combined (grouped) to make the table easier to understand • However, information is lost when categories are grouped – Individual scores cannot be retrieved – The wider the grouping interval, the more information is lost
- 13. “Rules” for ConstructingGrouped Frequency Distributions • Requirements (Mandatory Guidelines) – All intervals must be the same width – Make the bottom (low) score in each interval a multiple of the interval width • “Rules of Thumb” (Suggested Guidelines) – Ten or fewer class intervals is typical (but use good judgment for the specific situation) – Choose a “simple” number for interval width
- 14. Discrete Variables inFrequency or Grouped Distributions • Constructing either frequency distributions or grouped frequency distributions for discrete variables is uncomplicated – Individuals with the same recorded score had precisely the same measurements – The score is an exact score
- 15. Continuous Variables in FrequencyDistributions • Constructing frequency distributions for continuous variables requires understanding that a score actually represents an interval – A given “score” actually could have been any value within the score’s real limits – The recorded value was rounded off to the middle value between the score’s real limits – Individuals with the same recorded score probably differed slightly in their actual performance
- 16. Continuous Variables in FrequencyDistributions • Constructing grouped frequency distributions for continuous variables also requires understanding that a score actually represents an interval • Consequently, grouping several scores actually requires grouping several intervals • Apparent limits of the (grouped) class interval are always one unit smaller than the real limits of the (grouped) class interval. (Why?)
- 17. Learning Check • AGrouped Frequency Distribution table has categories 0-9, 10-19, 20-29, and 30-39. What is the width of the interval 20-29? • 9 pointsA • 9.5 pointsB • 10 pointsC • 10.5 pointsD
- 18. Learning Check -Answer • A Grouped Frequency Distribution table has categories 0-9, 10-19, 20-29, and 30-39. What is the width of the interval 20-29? • 9 pointsA • 9.5 pointsB • 10 points (29.5 – 19.5 = 10)C • 10.5 pointsD
- 19. Learning Check • Decideif each of the following statements is True or False. • You can determine how many individuals had each score from a Frequency Distribution Table T/F • You can determine how many individuals had each score from a Grouped Frequency Distribution T/F
- 20. Learning Check -Answer • The original scores can be recreated from the Frequency Distribution Table True • Only the number of individuals in the class interval is available once the scores are grouped False
- 21. 2.3 Frequency DistributionGraphs • Pictures of the data organized in tables – All have two axes – X-axis (abscissa) typically has categories of measurement scale increasing left to right – Y-axis (ordinate) typically has frequencies increasing bottom to top • General principles – Both axes should have value 0 where they meet – Height should be about ⅔ to ¾ of length
- 22. Data Graphing Questions •Level of measurement? (nominal; ordinal; interval; or ratio) • Discrete or continuous data? • Describing samples or populations? The answers to these questions determine which is the appropriate graph
- 23. Frequency Distribution Histogram •Requires numeric scores (interval or ratio) • Represent all scores on X-axis from minimum thru maximum observed data values • Include all scores with frequency of zero • Draw bars above each score (interval) – Height of bar corresponds to frequency – Width of bar corresponds to score real limits (or one-half score unit above/below discrete scores)
- 24. Figure 2.1 Frequency DistributionHistogram
- 25. Grouped Frequency Distribution Histogram Samerequirements as for frequency distribution histogram except: • Draw bars above each (grouped) class interval – Bar width is the class interval real limits – Consequence? Apparent limits are extended out one-half score unit at each end of the interval
- 26. Figure 2.2 Grouped FrequencyDistribution Histogram
- 27. Block Histogram • Ahistogram can be made a “block” histogram • Create a bar of the correct height by drawing a stack of blocks • Each block represents one per case • Therefore, block histograms show the frequency count in each bar
- 28. Figure 2.3 Frequency DistributionBlock Histogram
- 29. Frequency Distribution Polygons •List all numeric scores on the X-axis – Include those with a frequency of f = 0 • Draw a dot above the center of each interval – Height of dot corresponds to frequency – Connect the dots with a continuous line – Close the polygon with lines to the Y = 0 point • Can also be used with grouped frequency distribution data
- 30.
- 31. Figure 2.5 Grouped DataFrequency Distribution Polygon
- 32. Graphs for Nominalor Ordinal Data • For non-numerical scores (nominal and ordinal data), use a bar graph –Similar to a histogram –Spaces between adjacent bars indicates discrete categories • without a particular order (nominal) • non-measurable width (ordinal)
- 33. Figure 2.6 -Bar graph
- 34. Population Distribution Graphs •When population is small, scores for each member are used to make a histogram • When population is large, scores for each member are not possible – Graphs based on relative frequency are used – Graphs use smooth curves to indicate exact scores were not used • Normal – Symmetric with greatest frequency in the middle – Common structure in data for many variables
- 35. Figure 2.7 Bar Graphof Relative Frequencies
- 36. Figure 2.8 –IQ Population Distribution Shown as a Normal Curve
- 37. Box 2.1 -Figure 2.9 Use and Misuse of Graphs
- 38. 2.4 Frequency DistributionShape • Researchers describe a distribution’s shape in words rather than drawing it • Symmetrical distribution: each side is a mirror image of the other • Skewed distribution: scores pile up on one side and taper off in a tail on the other – Tail on the right (high scores) = positive skew – Tail on the left (low scores) = negative skew
- 39. Figure 2.10 -Distribution Shapes
- 40. Learning Check • Whatis the shape of this distribution? • SymmetricalA • Negatively skewedB • Positively skewedC • DiscreteD
- 41. Learning Check -Answer • What is the shape of this distribution? • SymmetricalA • Negatively skewedB • Positively skewedC • DiscreteD
- 42. Learning Check • Decideif each of the following statements is True or False. • It would be correct to use a histogram to graph parental marital status data (single, married, divorced...) from a treatment center for children T/F • It would be correct to use a histogram to graph the time children spent playing with other children from data collected in children’s treatment center T/F
- 43. Learning Check -Answer • Marital Status is a nominal variable; a bar graph is requiredFalse • Time is measured continuously and is an interval variable True
- 44. Figure 2.11- Answersto Learning Check Exercise 1 (p. 51)
- 45.
Editor's Notes
- #5 Terminology associated with frequency distributions is one of the least “standardized” across disciplines and texts students might encounter. Instructors may wish to emphasize the importance of being precise with the terms provided by the text authors, but also be aware that in other texts or courses the terms might be slightly different.
- #7 The ability to quickly and comfortably covert between fractions (proportions) , decimal fractions (relative frequency) and percentages is fundamental to success in this course. Some students struggle with reconciling the fact that although these are three distinct metrics, they all point to the same “deep” meaning.
- #14 This slide shows a recommended treatment for the four guidelines presented in the text. The text presents it slightly differently by indicating these are “guidelines” rather than absolute requirements. However, violating guideline 4 distorts the information conveyed and violating guideline 3 makes it much more difficult to assimilate the information conveyed by the table. Consequently, each instructor should clarify expectations for her class: are these “guidelines” or “rules?”
- #17 Why? Real limits extend ½ unit above and below each score. So the upper apparent score actually include ½ unit above it. Likewise, the lower apparent score acturally extends ½ unit below it. ½ + ½ = 1 “extra” unit included between real limits than is included between apparent limits.
- #23 The wide availability of graphing software may led an instructor to dispense with this slide. On the other hand, talking students through this discussion with a concrete example may help them better understand the issues and prevent some of the software-generated nonsense submitted by naïve users.
- #24 Instructors may wish to identify one of the most common errors made by beginning students: dropping scores from the histogram if the frequency is zero.
- #25 FIGURE 2.1 An example of a frequency distribution histogram. The same set of quiz scores is presented in a frequency distribution table and in a histogram.
- #27 FIGURE 2.2 An example of a frequency distribution histogram for grouped data. The same set of children’s heights is presented in a frequency distribution table and in a histogram.
- #29 FIGURE 2.3 A frequency distribution in which each individual is represented by a block placed directly above the individual’s score. For example, three people had scores of X = 2.
- #30 The importance of “closing the polygon” (sometimes called “anchoring the polygon”) can be humorously illustrated by describing the polygon floating away like an escaping helium-filled balloon.
- #31 FIGURE 2.4 An example of a frequency distribution polygon. The same set of data is presented in a frequency distribution table and in a polygon.
- #32 FIGURE 2.5 An example of a frequency distribution polygon for grouped data. The same set of data is presented in a grouped frequency distribution table and in a polygon.
- #34 FIGURE 2.6 A bar graph showing the distribution of personality types in a sample of college students. Because personality type is a discrete variable measured on a nominal scale, the graph is drawn with space between the bars.
- #36 FIGURE 2.7 A frequency distribution showing the relative frequency for two types of fish. Notice that the exact number of fish is not reported; the graph simply says that there are twice as many bluegill as there are bass.
- #37 FIGURE 2.8 The population distribution of IQ scores: an example of a normal distribution.
- #38 FIGURE 2.9 Two graphs showing the number of homicides in a city over a 4-year period. Both graphs show exactly the same data. However, the first graph gives the appearance that the homicide rate is high and rising rapidly. The second graph gives the impression that the homicide rate is low and has not changed over the 4-year period.
- #39 Many students are confused by skewness and focus on the main cluster of scores instead of the tail. Instructors may wish to relay the following visual aid to retaining the correct information. First make a fist with thumbs pointed out and away from the fist. Next rotate wrists so the fingers are visible (rather than the back of the hands) and point thumbs away from the body on both sides. The thumb on the right hand points to the right and the direction of positive numbers on a number line, and represents the tail in a positively skewed distribution. The thumb on the left hand points to the left and the direction of negative numbers on the number line, and represents the tail in a negatively skewed distribution.
- #40 FIGURE 2.10 Examples of different shapes for distributions.