PLSC 214 Pre Midterm 1

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  1. Reasons to Sample over census
    • -cheaper
    • -quicker
    • -large populations may be inaccessible 
    • -destructiveness of the observation
    • --carefully obtained sample is better than sloppily conducted census
  2. Sampling Error
    • -due solely to particular units that have been selected 
    • -chance
    • -sampling bias(usually the result of poor planning)
  3. Protection Against Sampling Error
    • -chance->use a large enough sample
    • -sampling bias->have a good plan
  4. Non Sampling Error
    • -can occur in census or sampling
    • -results solely from the manner in which observations are made
    • -ex. inacurate measurement from malfunctioning instruments
  5. Types of Sampling(list)
    • -convenience
    • -representative
    • -random(with and without replacement)
  6. Convenience Sampling
    • -usually self selected (people that phone in to give opinion)
    • -bad type of sampling
    • -results regarded with caution
  7. Representative Sampling
    • -represent the characteristics of a population as closely as possible
    • -more reliable than convenience
  8. Random Sample
    • -most important type of sample
    • -people recruited for use are totally random(can be with or without replacement)
  9. Canadian Census
    • -every 5 years
    • -last one was May 10, 2011
    • -employ about 34,000 temp workers for it
    • -must be filled out by law
    • -first one taken in 1871(had 211 questions)
  10. Agriculture Census
    • -started in Manitoba in 1896, Sask and Alberta joined in 1906
    • -every 5 years
    • -separate from regular census
  11. Element
    -specific subject(object) about which the info is collected
  12. Variable
    -characteristic under study for each element
  13. Observation/measurement/data value
    -value OF a variable FOR an element
  14. Data/Data set
    -collection of observations on one or more variables
  15. Quantitative Variables
    • -can be measured numerically
    • -math can be performed on the results
    • -can be continuous or discrete
  16. Continuous Variables
    • -variable that can assume any numerical value 
    • -blood pressure, time, distance
  17. Discrete Variables
    • -countable units
    • -you can have 3 dogs, but not 2.3 dogs
    • -the variable cannot assume any number in its range, only full units
  18. Qualitative Variables
    -must be defined in non-numerical categories
  19. Raw Data
    -data recorded in the sequence in which it was collected
  20. Frequency Distribution
    -lists the number of the data occurrences for each category of data
  21. Relative Frequency Distribution
    -shows the percent of observations that belong to each category
  22. Pareto Chart
    -bar graph that shows the bars in decreasing order of frequency (can be frequency or relative frequency)
  23. Continuous Histograms
    -similar to bar graph, but no gaps between the bars
  24. Discrete Histograms
    -similar to bar graph but does have gaps between the bars (versus continuous)
  25. Parameter
    -descriptive measure of a population
  26. Statistic
    -descriptive measure of a sample
  27. Mean
    • -average of the data
    • -can be of sample or population
  28. Median
    • -the middle term of data points that have been arranged in a ranked order (increasing or decreasing)
    • -if you have an even number of terms, average the 2 middle terms
  29. Mode
    • -the number which occurs with the highest frequency
    • -can be unimodal, bimodal or multimodal
    • -only measure of central tendency for qualitative data
  30. Range
    • -difference between the highest and lowest data points
    • -influenced by outliers
  31. Standard Deviation
    • -tells you how closely the values of the data set are clustered around the mean
    • -low value= data is closer to mean than a large SD
    • -calculated by taking the square root of the variance
    • -always in same unit as data
    • -can never be negative, may be zero
  32. 5 Number Summary
    -include the lowest value, quartile 1 boundary, median, quartile 3 boundary and highest value
  33. law of large numbers
    • -the long run relative frequency of repeated independent events gets closer to the relative true frequency as the number of trials increases
    • -the more times you flip a coin the closer you will get to 50/50 results
  34. Sample Space
    -the set of all possible outcomes of the experiment
  35. Event
    • -any collection of outcomes from an experiment
    • -denoted using capital letters such as A
  36. Simple Event
    -includes only one of the final outcomes for an experiment and is denoted E

    -can only be achieved one way
  37. Compound Event
    • -collection of more than one outcome for an experiment
    • -can have multiple ways of achieving the requirement
  38. Classical Probability
    -done when all outcomes from an event are equally likely
  39. Relative Frequency Probability
    -used when all possible outcomes do not have an equal chance to occur
  40. Subjective Probability
    -probability assigned to an event based on subjective judgement (experience, information and belief)
  41. Marginal Probability
    • -probability of an event without considering any other event
    • -also called simple probability
  42. Conditional Probability
    -probability that an event will occur given than another event has already occured
  43. Mutually Exclusive Events
    • -events that cannot occur together
    • -events have no shared elements
    • -in a venn diagram the circles will not overlap
  44. Mutually Non Exclusive Events
    -where observing one thing does not rule out the other, they can both occur, or one or the other
  45. Complimentary Events
    -two always mutually exclusive events that taken together include all the outcomes for an experiment
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PLSC 214 Pre Midterm 1
2014-01-26 21:20:25
PLSC 214 Pre Midterm

PLSC 214 Pre Midterm 1
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