1 / 20

Effective Frequency Distribution Methods in Data Analysis

Learn how to create grouped and ungrouped frequency distributions for quantitative variables, and explore various graphs for visualizing data effectively. The course covers constructing histograms, frequency polygons, cumulative distributions, and stem-and-leaf displays. Understand the conventions, benefits, and drawbacks of grouping data, along with interpreting relative and cumulative frequency distributions. Gain insights into constructing frequency distributions for qualitative variables and graphs such as bar graphs and pie charts. Avoid common pitfalls like misleading graphs and learn how to present data accurately. Enhance your statistical analysis skills with practical examples and techniques in this comprehensive guide.

myricks
Download Presentation

Effective Frequency Distribution Methods in Data Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 2 • Frequency Distributions and Graphs • I Frequency Distributions for Quantitative Variables • A. Ungrouped Frequency Distribution • 1. Each class interval contains a single score value

  2. Table 1. Taylor Manifest Anxiety Scores • (1) (2) 74 1 73 1 72 0 71 2 70 7 69 8 68 5 67 2 66 1 65 1 n = 28

  3. 2. Class interval size, i i = score real upper limit – score real lower limit B. Grouped Frequency Distribution 1. Each class interval spans two or more score values

  4. Table 2. IQ Data (1) (2) Xjf 160–169 1 150–159 1 140–149 0 130–139 2 120–129 3 110–119 6 100–109 8 90–99 3 80–89 1 70–79 1 n = 26

  5. 2. Size of 100–109 class interval

  6. C. Conventions Used in Constructing Frequency Distributions D. Determining the Number and Size of Class Intervals By Trial and Error E. Pros and Cons of Grouping Data

  7. F. Relative Frequency Distributions Table 3. IQ Data (1) (2) (3) (4) Xj f Prop f % f 160–169 1 .04 4 150–159 1 .04 4 140–149 0 .00 0 130–139 2 .08 8 120–129 3 .12 12 110–119 6 .23 23 100–109 8 .31 31 90–99 3 .12 12 80–89 1 .04 4 70–79 1 .04 4 n = 26 1.00 100

  8. G. Cumulative Frequency Distributions Table 4. IQ Data (1) (2) (3) (4) (5) (6) Xjf Prop f % f Cum prop f Cum % 160–169 1 .04 4 1.00 100 150–159 1 .04 4 .98 98 140–149 0 .00 0 .94 94 130–139 2 .08 8 .94 94 120–129 3 .12 12 .86 86 110–119 6 .23 23 .74 74 100–109 8 .31 31 .51 51 90–99 3 .12 12 .20 20 80–89 1 .04 4 .08 8 70–79 1 .04 4 .04 4 n = 26 1.00 100

  9. II Frequency Distribution for Qualitative Variables Table 5. Leading Cause of Death of Men in 2006 XProp f Accidents .07 Cancer .22 Heart disease .38 Stroke .08 Other .25 Total 1.00

  10. A. Conventions Used in Constructing Frequency Distributions for Qualitative Variables III Graphs for Quantitative Variables A. Histogram for IQ Data

  11. B. Frequency Polygon for IQ Data

  12. C. Cumulative Polygon for IQ Data

  13. D. Stem-and-Leaf Display for IQ Data • (1) (2) (3) • StemsLeaves f • 70–79 8 1 • 80–89 5 1 • 90–99 3 8 6 3 • 100–109 7 1 8 2 9 5 4 3 8 • 110–119 6 2 3 5 6 8 6 • 120–129 2 9 7 3 • 130–139 3 5 2 • 140–149 0 • 150–159 2 1 • 160–169 0 1

  14. IV Graphs for Qualitative Variables A. Bar Graph for Leading Cause of Death of Men in 2006

  15. B. Pie Chart for Leading Cause of Death of Men in 2006

  16. 1. Construction of pie charts Conversion of Prop f To Minutes Accidents .07  60 = 4.2 Cancer .22  60 13.2 + 4.2 =17.4 Heart disease .38  60 22.8 + 17.4 = 40.2 Stroke .08  60 4.8+ 40.2 =45.0 Other .25  60 15.0+ 45.0 =60.0

  17. V Shapes of Distributions

  18. VI Misleading Graphs Figure 1. Two plots of the same data. Figure (a) is misleading.

  19. A. Pictogram Figure 2. Figure (a) is misleading because it uses both height and area to represent computer sales

More Related