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2.1 Discrete and Continuous Variables

2.1 Discrete and Continuous Variables. 2.1.1 Discrete Variable 2.1.2 Continuous Variable. 2.1.1 Discrete Variable. These are the heights of 20 children in a school. The heights have been measured correct to the nearest cm. For example . For example

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2.1 Discrete and Continuous Variables

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  1. 2.1 Discrete and Continuous Variables 2.1.1 Discrete Variable 2.1.2 Continuous Variable

  2. 2.1.1 Discrete Variable • These are the heights of 20 children in a school. The heights have been measured correct to the nearest cm. For example • . For example • 144 cm ( correct to the nearest cm) could have arisen from any value in the range 143.5cm  h < 144.5 cm. • Other examples of continuous data are • the speed of vehicles passing a particular point, • the masses of cooking apples from a tree, • the time taken by each of a class of children to perform a task. • **Continuous data cannot assume exact value, but can be given only within a certain range or measured to a certain degree of accuracy,**

  3. 2.1.2 Continuous Variable • There are the marks obtained by 30 pupils in a test: • the number of cars passing a checkpoint in a certain time, • the shoe sizes of children in a class, • the number of tomatoes on each of the plants in a greenhouse.

  4. 2.2 Frequency Tables 2.2.1 Frequency Tables for Discrete Data 2.2.2 Frequency Tables for Continuous Data • Relative Frequency is , where ri is the relative frequency for the class i • and N = Percentage Frequency can be obtained by multiplying the relative frequency by 100%. ri

  5. 2.2.1 Frequency Tables for Discrete Data

  6. 2.2.2 Frequency Tables for Continuous Data

  7. 2.3 Graphical Representation 2.3.1 Bar Charts 2.3.2 Histograms 2.3.3 Frequency Polygons and Frequency Curves 2.3.4 Cumulative Frequency Polygons and Curves 2.3.5 Stem-and-leaf Diagrams 2.3.6 Logarithmic graphs

  8. 2.3.1 Bar Charts • The frequency distribution of a discrete variable can be represented by a bar chart.

  9. 2.3.2 Histograms • A continuous frequency distribution CANNOT be represented by a bar chart. It is most appropriately represented by a histogram.

  10. 2.3.3 Frequency Polygons and Frequency Curves • Frequency Polygons • Frequency Curves • Relative frequency polygons • Relative frequency curves

  11. 2.3.4 Cumulative Frequency Polygons and Curves • Example • The heights of 30 broad bean plants were measured, correct to the nearest cm, 6 weeks after planting. The frequency distribution is given below. • Construct the cumulative frequency table. • Construct the cumulative frequency curve. • Estimate from the curve • the number of plants that were less than 10 cm tall; • the value of x, if 10% of the plants were of height x cm or more.

  12. 2.3.5 Stem-and-leaf Diagrams • 1) In the below diagram, stems are hundreds and leaves are units. • The set of data in the diagram represents: 111,123,147,148,223,227,355,363,380,421,423,500

  13. A householder’s weekly consumption of electricity in kilowatt-hours during a period of nine week in a winter were as follows: 338,354,341,353,351,341,353,346,341. Please completed stem and leaf diagram .

  14. Examination results of 11 students: • English:23,39,40,45,51,55,61,64,65,72,78 • Chinese:37,41,44,48,58,61,63,69,75,83,89 One way to compare their performances in the two subjects is by means of side by side stem-and-leaf diagrams.

  15. The comparison can be made more dramatic by back-to-back stem-and-leaf diagram.

  16. Answer

  17. 2.3.6 Logarithmic graphs

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