Intro to Probability & Stats/Lecture 1

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Intro to Probability & Stats/Lecture 1
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2010-01-05 03:36:01
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Lecture 1: Basic concepts of Statistics
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Basic definitions, Types of Data, Methods of sampling
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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
    Business and financial decisions
  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

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