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Elementary statistics for foresters

Elementary statistics for foresters. Lecture 2 Socrates/Erasmus Program @ WAU Spring semester 2005/2006. Descriptive statistics. Descriptive statistics. Data grouping (frequency distribution) Graphical data presentation (histogram, polygon, cumulative histogram, cumulative histogram)

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Elementary statistics for foresters

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  1. Elementary statistics for foresters Lecture 2 Socrates/Erasmus Program @ WAU Spring semester 2005/2006

  2. Descriptive statistics

  3. Descriptive statistics • Data grouping (frequency distribution) • Graphical data presentation (histogram, polygon, cumulative histogram, cumulative histogram) • Measures of location (mean, quadratic mean, weighted mean, median, mode) • Measures of dispersion (range, variance, standard deviation, coefficient of variation) • Measures of asymmetry

  4. Descriptive statistics • Descriptive statistics are used to summarize or describe characteristics of a known set of data. • Used if we want to describe or summarize data in a clear and concise way using graphical and/or numerical methods.

  5. Descriptive statistics • For example: we can consider everybody in the class as a group to be described. Each person can be a source of data for such an analysis. • A characteristic of this data may be for example age, weight, height, sex, country of origin, etc.

  6. Descriptive statistics • Closer-to-forestry example: we can consider all pine stands in central Poland as a group to be characterized. • Each stand can be described by its area, age, site index, average height, QMD, volume per hectare, volume increment per hectare per year, amount of carbon sequestered, species composition, damage index, ...

  7. Frequency distribution

  8. Frequency distribution • Frequency distribution is an ordered statistical material (measurements) in classes (bins) built according to the investigated variable values

  9. Frequency distribution • How to build it? • determine classes (values/mid-points and class limits), depending on variable type • classify each unit/measurement to the appropriate class • sum units in each class

  10. Frequency distribution • Practical issues: • number of classes should be between 6 and 16 • classes should have identical widths • middle-class values/class mid-points should be chosen in such a way, that they are easy to manipulate

  11. Frequency distribution

  12. Graphical description of data • Pictures are very informative and can tell the entire story about the data. • We can use different plots for different sorts of variables. We can use for example bar plots (histograms), pie charts, box plots, ... .

  13. Graphical description of data

  14. Graphical description of data

  15. Graphical description of data

  16. Graphical description of data

  17. Numerical data description

  18. Sums and their properties

  19. Measures of location • Arithmetic mean • Quadratic mean • Weighted mean • Median • Mode • other

  20. Arithmetic mean

  21. Quadratic mean

  22. Properties of the mean Weighted mean ...

  23. Median • If observations of a variable are ordered by value, the median value corresponds to the middle observation in that ordered list. • The median value corresponds to a cumulative percentage of 50% (i.e., 50% of the values are below the median and 50% of the values are above the median).

  24. Median • The position of the median is calculated by the following formula:

  25. Median • How to calculate it? • If the detailed values are available, sort the data file and find an appropriate value • If the frequecy distribution is available, use the following formula:

  26. Mode • The mode is the most frequently observed data value. • There may be no mode if no value appears more than any other. • There may also be two (bimodal), three (trimodal), or more modes (multimodal). • In the case of grouped frequency distributions, the modal class is the class with the largest frequency.

  27. Mode • If there is no exact mode available in the data file, you can calculate its value by using: • an approximate Pearson formula • by using an interpolation

  28. Relationship between measures

  29. f(x) c 3c μo μe μ Relationship between measures

  30. f(x) c 3c μ μe μo x Relationship between measures

  31. Sample calculations

  32. Sample calculations

  33. Measures of dispersion • Range • Variance • Standard deviation • Coefficient of variation

  34. Range and variance • Range is a difference between the lowest and the highest value in the data set • Variance • average squared differences between data values and arithmetic mean

  35. Variance

  36. Variance

  37. Standard deviation and coefficient of variation

  38. Sample calculations

  39. Measures of asymmetry • Skewness: is a measure of the degree of asymmetry of a distribution. • If the left tail is more pronounced than the right tail, the function has negative skewness. • If the reverse is true, it has positive skewness. • If the two are equal, it has zero skewness.

  40. Skewness

  41. Skewness • Skewness can be calculated as a distance between mean and mode expressed in standard deviations:

  42. Acknowledgements • This presentation was made thanks to the support and contribution of dr Lech Wróblewski

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