# Intro to Probability & Stats/Lecture 1

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1. To understand and operate data, one needs to have at least
the basic knowledge of statistics
2. Statistics can be a powerful tool for making
3. This is a collection of methods allowing us to:

- collect data,

- organize, analyze and interpret the data,

- use the data to draw conclusions, make forecasts and take decisions.
Statistics
4. Statistics usually deals with large groups, or collections, of objects. It is not concerned with material objects themselves but with some of their characteristics that are of interest. These characteristics may be studied through measurements, responses, counts etc. What are these groups?
populations
5. A collection of all measurements, responses, or counts of interest is called a
Population
6. The most accurate information about a population can be obtained by
a census
7. a relatively small group (subcollection) of elements drawn from a population.
a sample
8. A sample may make 1/100, or 1/1000, or even a smaller part of the population, butif it is drawn correctly so it is
• a representative, it may yield sufficiently accurate
• information about the whole population.
9. A complete statistical research consists of three major steps (branches)
(1) Drawing a sample from the population. This is a very sensitive procedure, because if a sample was drawn incorrectly, the whole research will be worthless.

(2) Organizing and processing the information provided by the sample. This is the task of descriptive statistics.

• (3) Based on the result obtained for a sample, conclusions must be made on the whole population. In other words, the statistician who already
• knows the sample measurements must figure out the appropriate population measurements. This part is called inferential statistics.
10. What is inferential Statistics?
When the statistician who already knows the sample measure ments, figure out the appropriate population measurements
11. Any data that can be measured and expressed in numbers are referred to as
quantitative (numerical)
12. Examples of this data are weight, length, width, temperature, age, price etc.
quantitative (numerical) data
13. If data cannot be measured numerically, they are
qualitative (categorical) data
14. Examples of this data are colors, brands, opinions (yes or no, good or bad) etc.
qualitative (categorical) data
15. The data cannot be arranged in any logically justified order is
Nominal level
16. The data can be arranged in some meaningful order, but it is impossible to define the intervals between them is
Ordinal level
17. The data can be arranged in a meaningful order and the intervals between them can be defined, but comparing two data values by dividing one by another makes no sense. That applies, among others, to scales without an inherent zero like temperature scale (the zero depends on the measurement system being used).
Interval level.
18. The data can be arranged in a meaningful order, the intervals between them can be defined, and comparing two data values by dividing one by another makes sense.
Ratio level
19. What is the study called when a researcher performs measurement without modifying the subject.
an observational study
20. A scientist who is studying the environment tries to make his/her presence as inconspicuous as possible to not disturb the wildlife.What type of study is this?
an observational study
21. what type of study is it when a researcher applies some treatment to the object and then observes the effect of it.
an experimental study
22. What type of study is demonstrated by a scientist who is studying the behavior of mice creates various situations for them and then watches and records their reactions.
an experimental study
23. an experiment can be performed not only on a real object but also on its physical or mathematical model is called
a simulation : which are most efficient in computers
24. A sample drawn from a population must be
representative
25. This is when each element of the population has an equal chance to be selected.
Random sample
26. This means that in a set ofnumbers (usually in a certain interval) each number has an equal chance to be selected.
random number
27. One should use this sampling method for Large poplulations
Random Number Selection
28. In this procedure not just each member of a population but each sample of a given size has an equal chance to be selected.
Simple random sampling
29. A very large population may be divided in two or more large groups that share some similar characteristics. The research will be performed on each group separately; after that the results will be combined.
Stratified sampling
30. The members of the population are ordered insome way; a starting point is chosen randomly; then each kth member is selected.
Systematic sampling
31. The population is first divided into a large number of sections (clusters); then several clusters are selected at random. After that, all members of the selected clusters are surveyed. This method is convenient when a population is divided into clusters in a natural way.
Cluster Sampling
32. The sample consists of members that can be easily available
Convenience sampling
33. There are two kinds of errors that may occur when a sample is being drawn
• Nonsampling Error
• Sampling Error
34. This is an error caused by flaws of the sampling process.
(The sample is not representative.)
Nonsampling error
35. This error is caused by the random nature of the sample.In a carefully selected sample, a nonsampling error can be prevented, but the possibility of a sampling error always exists.
Sampling error
36. A collection of methods allowing us to collect data, organize, analyze and interpret the data, and use the data to draw conclusions, make forecasts and take decisions
Statistics
37. A collection of all measurements, responses, or counts of interest.
population
38. Taking information of every element of the population.
A census
39. A relatively small group (subcollection) of elements drawn from a population.
A sample
40. A numerical characteristic of a population is
Population parameter
41. Anumerical characteristic of a sample is a
Sample statistic
42. Organizing and processing the information provided by the sample.
Descriptive statistics
43. Making conclusions about the population parameter based on the information obtained for a sample.
Inferential statistics
44. Any data that can be measured and expressed in numbers.
Quantitative (numerical) data
45. Any data that cannot be measured numerically.
Qualitative (categorical) data
46. The data cannot be arranged in any logically justified order.
Nominal level of measurement
47. The data can be arranged in some meaningful order, but it is impossible to define the intervals between them.
Ordinal level of measurement
48. The data can be arranged in a meaningful order and the intervals between them can be defined, but comparing two data values by dividing one by another makes no sense.
Interval level of measurement
49. The data can be arranged in a meaningful order, the intervals between them can be defined, and comparing two data values by dividing one by another makes sense.
Ratio level of measurement
50. A study where a researcher performs measurement without modifying the subject.
Observational study
51. A study where a researcher applies some treatment to the object and then observes the effect of it.
Experimental study
52. A study that is performed not on a real object but on its physical or mathematical model.
Simulation
53. A number selected from a set of numbers in a procedure where each number has an equal chance to be selected.
Random number
54. Each element of the population has an equal chance to be selected.
Random sampling
55. Each sample of a given size has an equal chance to be selected.
Simple random sampling
56. A very large population is divided in two or more large groups (strata) that share some similar characteristics. The research is performed on each group separately; after that the results are combined.
Stratified sampling
57. The members of the population are ordered in some way; a starting point is randomly chosen; then each kth member is selected.
Systematic sampling
58. The population is first divided into a large number of sections (clusters); then several clusters are selected at random. After that, all members of the selected clusters are surveyed.
Cluster sampling
59. The sample that consists of data that can be easily available.
Convenience sampling
60. An error caused by flaws of the sampling process. (The sample is not representative.)
Nonsampling error
61. An error caused by the random nature of the sample.
Sampling error

### Card Set Information

 Author: Anonymous ID: 3769 Filename: Intro to Probability & Stats/Lecture 1 Updated: 2010-01-05 08:36:01 Tags: Lecture 1: Basic concepts of Statistics Folders: Description: Basic definitions, Types of Data, Methods of sampling Show Answers:

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