# 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
 Author: jaz584 ID: 258462 Card Set: PLSC 214 Pre Midterm 1 Updated: 2014-01-26 21:20:25 Tags: PLSC 214 Pre Midterm Folders: Description: PLSC 214 Pre Midterm 1 Show Answers: