1. Statistics · the study of information Data · information

2. Background ·. Statistics was first. developed. in the 17th and
Background   · Statistics was first developed in the 17th and 18th Centuries as a way of solving problems

3. · Why were people dying of. cholera. · How could the king of
·     Why were people dying of cholera? ·     How could the king of France be more successful at cards? ·     Can the weather be predicted?

4. Statistics grew rapidly and became a formal branch of mathematics during the Industrial Revolution of the 19th Century.

5. · Factories wanted to. produce quality products. at a minimum cost
·     Factories wanted to produce quality products at a minimum cost. ·    The insurance industry developed by using statistics to assess risk.

6. With the information age (late 20th Century), Statistics became essential for interpreting vast amounts of data.

7. · Public opinion polls · Forensic science · Need for accountability

8. In the 21st Century, technology is making statistics easier and more accessible to everyone.

9. 2 main branches of statistics

10. Descriptive Statistics describes data

11. · organizes in a meaningful way · tells what is “average”

12. · tells how spread out the. data is · tells how an individual
·     tells how spread out the data is ·     tells how an individual piece of data compares to the others

13. Inferential Statistics interprets data

14. · makes generalizations. and predictions based on
·     makes generalizations and predictions based on the data ·     determines whether a result is “significant” 

15. Significant. A result is SIGNIFICANT. in statistics if it is unlikely
Significant A result is SIGNIFICANT in statistics if it is unlikely to have happened by chance

16. · “Significant” is the single. most important term in. statistics
·   “Significant” is the single most important term in statistics. ·     IMPORTANT: “significant” does NOT necessarily mean big or important

17. Population a large group we want to find out about

18. Parameter. a piece of information
Parameter a piece of information about a population ·    It is often impossible or impractical to find parameters.

19. Sample a small group that we use to represent a population

20. Statistic (singular) a piece of information about a sample

21. · We use statistics. (information about. samples) to estimate
·      We use statistics (information about samples) to estimate parameters (information about populations).

22. REMEMBER: Population  Parameter Sample  Statistic

23. 4 main ways to gather data:

24. Sampling ·. Using a small group to. represent the population ·
Sampling · Using a small group to represent the population · Most common method used in statistics, and the method we will focus on

25. ·. If done carefully,. sampling can give good. information about the
· If done carefully, sampling can give good information about the population.

26. Census ·. a sample that includes the. entire population ·
Census · a sample that includes the entire population · usually not practical, and often impossible

27. Experiment 1. Divide the sample into two. (or more groups) 2
Experiment 1.  Divide the sample into two (or more groups) 2.  Treat the groups differently. 3.  Check for differences.

28. Terms involved in experiments · Control group vs
Terms involved in experiments ·    Control group vs. experimental group ·    Observation vs. Treatment

29. · Placebo · Double-blind

30. Simulation ·. A small-scale model of a. real-world situation ·
Simulation · A small-scale model of a real-world situation · In modern times, most often done on computer

31. · Could also be a constructed model, like a science fair project

32. MORE ABOUT SAMPLING Ideally, a sample should be representative of the population.

33. characteristics of the. sample should be similar
characteristics of the sample should be similar to those of the population ·    A “good” sample is representative; a “bad” sample isn’t.

34. 5 Types of samples:

35. Convenience Sample ·. A sample chosen because. it is EASY to obtain ·
Convenience Sample ·   A sample chosen because it is EASY to obtain ·    Often a “bad” sample

36. · However, for casual purposes, it’s often the best we can obtain

37. Examples: ·. Asking your friends or co-. workers ·
Examples: ·   Asking your friends or co- workers ·   Self-selected samples, like internet or phone-in polls

38. Systematic Sample ·. Ahead of time you decide. on a SYSTEM for how you
Systematic Sample ·   Ahead of time you decide on a SYSTEM for how you will choose the sample

39. · Ex.: Numbering off, and picking every 7th person

40. ·. IMPORTANT: Before you. begin, you’ve decided who
·   IMPORTANT: Before you begin, you’ve decided who will and won’t be part of the sample.

41. Stratified Sample

42. 1. Divide the population into. groups, based on some
1.  Divide the population into groups, based on some characteristic (race, sex, etc.)

43. 2.  Choose samples from each group (usually in the same proportion the groups come up in the population).

44. ·. Makes a formal effort to. include a variety ·. BUT … May not be
·   Makes a formal effort to include a variety   ·  BUT … May not be representative of the population on any other characteristic besides the one you chose

45. Cluster Sample ·. Chose one (or a few). CLUSTERS. o
Cluster Sample ·   Chose one (or a few) CLUSTERS o  complete sub-groups o    close to each other in time or space o    usually clusters are places (town, ZIP- code, school etc.)

46. ·. Take a CENSUS of the. clusters (include. everybody). ·
·  Take a CENSUS of the clusters (include everybody). ·   The clusters are your sample. ·   Example: test-marketing

47. Random Sample · IMPORTANT: Random does NOT mean “haphazard”.

48. ·. Everyone in the population. has an equal chance of. being selected
·   Everyone in the population has an equal chance of being selected. ·     You find an organized way ahead of time that guarantees the selection is fair.

49. ·. You have no idea ahead. of time who will or won’t
·   You have no idea ahead of time who will or won’t be in your population.

50. ·. Random samples are the. “best” samples. o. Have the best chance
·   Random samples are the “best” samples. o   Have the best chance of being truly representative of the population

51. BUT … · Random samples are often difficult to gather.

52. To get random samples, you can use: ·. Games of chance ·
To get random samples, you can use: ·    Games of chance ·     Random number tables

53. · Random number generators (on calculators or computers)

54. NOTE: In most real-world situations, various types of sampling are combined.

55. For example, stratified random samples are often used in market research and by the government.

56. 4 levels of data:

57. Nominal Data

58. Divide the sample into. named categories. o. No category is
Divide the sample into named categories o   No category is considered better or worse than another.

59. Count up how many are in. each category ·. Provides the least
Count up how many are in each category · Provides the least information of any level of measurement

60. Ordinal Data

61. Divide into categories that. can be arranged in a. definite ORDER from
Divide into categories that can be arranged in a definite ORDER from bottom to top. o    There is a definite continuum, with one category above another.

62. Count up how many are in each category. · Still just a count, but the order provides more information.

63. Nominal and ordinal data are also called qualitative data, because they describe qualities (named characteristics).

64. Interval data

65. The data is NUMBERS but … · there is NO definite ZERO.

66. o. You can compare the. numbers by. subtracting (how many
o  You can compare the numbers by subtracting (how many more one is than another).

67. o. You can’t divide (can’t. tell how many times. one number is of
o   You can’t divide (can’t tell how many times one number is of another).

68. Most common examples. are time and temperature ·
Most common examples are time and temperature · Things that can involve negative numbers are also interval data.

69. In interval data, it doesn’t. name sense to say “twice”
In interval data, it doesn’t name sense to say “twice” or “half” when you compare the numbers.

70. Ratio data

71. NUMBERS, with a DEFINITE ZERO (or bottom of some sort).

72. o. You can both subtract. and divide the. numbers. o
o    You can both subtract and divide the numbers o   Terms like “twice” and “half” make sense with ratio data.

73. Most numerical data is ratio data

74. Considered the “best” kind. of data ·. Usually the hardest kind of
Considered the “best” kind of data · Usually the hardest kind of data to gather

75. Interval and ratio data are also called quantitative data, because they describe quantities (numbers).