Descriptive statistics

Jul 29, 2013Download as pptx, pdf34 likes12,604 views

1. The document discusses descriptive statistics, which is the study of how to collect, organize, analyze, and interpret numerical data. 2. Descriptive statistics can be used to describe data through measures of central tendency like the mean, median, and mode as well as measures of variability like the range. 3. These statistical techniques help summarize and communicate patterns in data in a concise manner.

Adapted from the Presentation
of Mrs. Zennifer L. Oberio
Presented by:
Balbido, Aileen U.
Latap, Kenneth John R.
Tejuco, Kerwin Chester

The Study of how to:
Collect
Organize
Analyze
& interpret
numerical information
DESCRIPTIVE STATISTICS

Categories or Qualities
Numbers are used simply as labels for groups or
classes
Number convey no numerical information
Example:1 for YES, 2 for NO
1 – Red, 2 – Yellow, 3 - Green
DESCRIPTIVE STATISTICS

Data may be ordered using inequality according
to their size or quality
Example: Mohs’ Scale of Hardness
1 – Talc 2 – Gypsum, 3 – Calcite 4 – Fluorite
DESCRIPTIVE STATISTICS

Example: Mohs’ Scale of Hardness
1 – Talc 2 – Gypsum, 3 – Calcite 4 – Fluorite
Data may be ranked (but no indication of how much of the
variable exists)
3>2 : Calcite is harder than gypsum
Differences and Ratios between data values are
meaningless
2 – 1 = 4 – 3 : The difference in hardness between gypsum and
talc is equal to the difference in hardness between fluorite and
calcite.
4 ÷ 2 – 2: Fluorite is twice as hard as gypsum.
DESCRIPTIVE STATISTICS

Differences between data values represent equal
amounts in the magnitude of the variable
measured
No true zero (the complete absence of the
variable measured)
Example: Temperatures in degrees Fahrenheit
and degrees Celsius
DESCRIPTIVE STATISTICS

Example: Temperatures in degrees Fahrenheit and degrees
Celsius
Ranking and taking differences are permitted.
100˚F > 98˚F : 100˚F is warmer than 98˚F.
100˚F - 98˚F = 52˚F - 50˚F : The same amount of heat is required
to raise the temperature of an object from 98˚F to 100˚F and
from 50˚F to 52˚F
Ratios are meaningless.
100˚F is twice as hot as 50˚F
In degrees Celsius, 100˚ is 37.8˚C and 50˚F is 10˚C.
DESCRIPTIVE STATISTICS

Has a true zero as a starting point for all
measurements.
Example: length, height, elapsed time, volume
Taking ratios and differences, and ranking are
permitted
DESCRIPTIVE STATISTICS

1 2 2 3 3
3 4 4 5 5
5 6 6 7 7
7 7 8 8 9
9 10 10 11 11
DESCRIPTIVE STATISTICS

Descriptive Statistics
DESCRIPTIVE STATISTICS

1 2 2 3 3
3 4 4 5 5
5 6 6 7 7
7 7 8 8 9
9 10 10 11 11
Data set from 25 subjects
DESCRIPTIVE STATISTICS

SHORTCOMINGSMerits
Takes into account every value in the data set. Always exists and
unique. Most useful of the three for inferential statistics.
Can be influenced by extremely high or low values (outliers)
DESCRIPTIVE STATISTICS

SHORTCOMINGSMerits
Not easily affected by outliers (extreme values). Always exists
and unique.
Less reliable than the mean – the medians of many samples
drawn from the same population will vary more widely than the
corresponding sample means.
DESCRIPTIVE STATISTICS

SHORTCOMINGSMerits
Requires no calculation, only counting
Not a stable measure – it depends only a few values
May not exist
Moy not be unique
DESCRIPTIVE STATISTICS

1 2 3 4 5 6 7 8 9 10 11
DESCRIPTIVE STATISTICS

A ‘quick and easy’ indication of variability
Provides no indication concerning the dispersion of the
values which wall between the two extremes
Relatively unstable measure of variability because it can be
influenced by change in the highest or lowest value
DESCRIPTIVE STATISTICS
MERITS SHORTCOMINGS

MERITS SHORTCOMINGS
More resistant to extreme values than the range
Does not utilize all the values in the data or set for its
computation
DESCRIPTIVE STATISTICS

MERITS SHORTCOMINGS
Use all the values in the data for its computation
DESCRIPTIVE STATISTICS

DESCRIPTIVE STATISTICS
below
above
median
8.375
(6 + 2.375)
3.625
(6 + 2.375)

DESCRIPTIVE STATISTICS
3.186
(6.12 – 2.934)
1 sd below
1 sd above
mean
9.054
( 6.12 + 2.934)

Data Set from 25 subjects
1 2 2 3 3
3 4 4 5 5
5 6 6 7 7
7 7 8 8 9
9 10 10 11 11
DESCRIPTIVE STATISTICS

Data Set from 25 subjects
1 2 2 3 3
3 4 4 5 5
5 6 6 7 7
7 7 8 8 9
9 10 10 11 11

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Descriptive statistics

1. Adapted from the Presentation of Mrs. Zennifer L. Oberio Presented by: Balbido, Aileen U. Latap, Kenneth John R. Tejuco, Kerwin Chester

2. The Study of how to: Collect Organize Analyze & interpret numerical information DESCRIPTIVE STATISTICS

3. DESCRIPTIVE STATISTICS

4. DESCRIPTIVE STATISTICS

5. Categories or Qualities Numbers are used simply as labels for groups or classes Number convey no numerical information Example:1 for YES, 2 for NO 1 – Red, 2 – Yellow, 3 - Green DESCRIPTIVE STATISTICS

6. Data may be ordered using inequality according to their size or quality Example: Mohs’ Scale of Hardness 1 – Talc 2 – Gypsum, 3 – Calcite 4 – Fluorite DESCRIPTIVE STATISTICS

7. Example: Mohs’ Scale of Hardness 1 – Talc 2 – Gypsum, 3 – Calcite 4 – Fluorite Data may be ranked (but no indication of how much of the variable exists) 3>2 : Calcite is harder than gypsum Differences and Ratios between data values are meaningless 2 – 1 = 4 – 3 : The difference in hardness between gypsum and talc is equal to the difference in hardness between fluorite and calcite. 4 ÷ 2 – 2: Fluorite is twice as hard as gypsum. DESCRIPTIVE STATISTICS

8. Differences between data values represent equal amounts in the magnitude of the variable measured No true zero (the complete absence of the variable measured) Example: Temperatures in degrees Fahrenheit and degrees Celsius DESCRIPTIVE STATISTICS

9. Example: Temperatures in degrees Fahrenheit and degrees Celsius Ranking and taking differences are permitted. 100˚F > 98˚F : 100˚F is warmer than 98˚F. 100˚F - 98˚F = 52˚F - 50˚F : The same amount of heat is required to raise the temperature of an object from 98˚F to 100˚F and from 50˚F to 52˚F Ratios are meaningless. 100˚F is twice as hot as 50˚F In degrees Celsius, 100˚ is 37.8˚C and 50˚F is 10˚C. DESCRIPTIVE STATISTICS

10. Has a true zero as a starting point for all measurements. Example: length, height, elapsed time, volume Taking ratios and differences, and ranking are permitted DESCRIPTIVE STATISTICS

11. DESCRIPTIVE STATISTICS

12. DESCRIPTIVE STATISTICS

13. 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 7 7 8 8 9 9 10 10 11 11 DESCRIPTIVE STATISTICS

14. Descriptive Statistics DESCRIPTIVE STATISTICS

15. DESCRIPTIVE STATISTICS

16. 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 7 7 8 8 9 9 10 10 11 11 DESCRIPTIVE STATISTICS

18. DESCRIPTIVE STATISTICS

19. 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 7 7 8 8 9 9 10 10 11 11 Data set from 25 subjects DESCRIPTIVE STATISTICS

20. 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 7 7 8 8 9 9 10 10 11 11 Data set from 25 subjects DESCRIPTIVE STATISTICS

21. DESCRIPTIVE STATISTICS

22. SHORTCOMINGSMerits Takes into account every value in the data set. Always exists and unique. Most useful of the three for inferential statistics. Can be influenced by extremely high or low values (outliers) DESCRIPTIVE STATISTICS

23. SHORTCOMINGSMerits Not easily affected by outliers (extreme values). Always exists and unique. Less reliable than the mean – the medians of many samples drawn from the same population will vary more widely than the corresponding sample means. DESCRIPTIVE STATISTICS

24. SHORTCOMINGSMerits Requires no calculation, only counting Not a stable measure – it depends only a few values May not exist Moy not be unique DESCRIPTIVE STATISTICS

25. DESCRIPTIVE STATISTICS

26. 1 2 3 4 5 6 7 8 9 10 11 DESCRIPTIVE STATISTICS

27. DESCRIPTIVE STATISTICS

28. 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 7 7 8 8 9 9 10 10 11 11 Data set from 25 subjects DESCRIPTIVE STATISTICS

29. A ‘quick and easy’ indication of variability Provides no indication concerning the dispersion of the values which wall between the two extremes Relatively unstable measure of variability because it can be influenced by change in the highest or lowest value DESCRIPTIVE STATISTICS MERITS SHORTCOMINGS

30. MERITS SHORTCOMINGS More resistant to extreme values than the range Does not utilize all the values in the data or set for its computation DESCRIPTIVE STATISTICS

31. MERITS SHORTCOMINGS Use all the values in the data for its computation DESCRIPTIVE STATISTICS

32. DESCRIPTIVE STATISTICS

34. DESCRIPTIVE STATISTICS below above median 8.375 (6 + 2.375) 3.625 (6 + 2.375)

35. DESCRIPTIVE STATISTICS

36. DESCRIPTIVE STATISTICS 3.186 (6.12 – 2.934) 1 sd below 1 sd above mean 9.054 ( 6.12 + 2.934)

37. Data Set from 25 subjects 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 7 7 8 8 9 9 10 10 11 11 DESCRIPTIVE STATISTICS

38. Data Set from 25 subjects 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 7 7 8 8 9 9 10 10 11 11

39. Data Set from 25 subjects 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 7 7 8 8 9 9 10 10 11 11

40. Data Set from 25 subjects 1 2 2 3 3 3 4 4 5 5 5 6 6 7 7 7 7 8 8 9 9 10 10 11 11

Descriptive statistics

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