# ERA153

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1. Descriptive Statistics
Summarize a data set
2. Inferential Statistics
• reveal the larger group through the smaller group's characteristics
• representative samples
3. Population
all the members of a defined group
4. Sample
• any subset of a population
• works for interval data
• works for ratio data
5. Qualitative variables
differ by category rather than by amount but there are measurable differences
6. Constants
never vary, hold little analytical value
7. Variables
they have different manifestations
8. A Research Design
formal plan for gathering and analyzing data
9. Independent Variable
the variable thought to affect another
10. Dependent variable
the variable influenced
11. Data Scale
different kinds of measures gauge different qualities
12. Measurement
using rules to asign numbers
13. Nominal data
indicate a category, yields the least amount of information about an object
14. Ordinal data
• allows ranking
• greater than
• less than
• percentile scores
15. Interval data
indicates degree of difference
16. Ratio data
• includes a zero
• uncommon in educational measurement
17. Interval Scale Data
how much greater or less
18. Descriptive Statistics
calculated so that one can know the essential characteristics of data sets without having to refer to each individual measure.
19. Central tendency
most typical in a data set
20. Measures of Central Tendency
mode, median, mean
21. Mode
most frequently occuring measure in a group
22. Unimodal
one mode
23. bimodal
two modes
24. Median
• the point below which half the scores in the group occur.
• isn't calculated as much as it is identified.
• the middle most number
25. Mean
most commonly used measure of central tendency is the arithmetic average
26. Outliers
measures in a group that are so high or so low compared to the others that they will have an undue effect on the statistics.
27. Range
the difference between the highest and the lowest
28. Quartiles
Fourths of the range
29. Interquartile Range
stretches from the 25th to the 75th percentile in a distribution.
30. Semi-interquartile Range
half the interquartile range
31. Variance
the sum of the squared score to mean differences divided by n-1
32. Standard Deviation
the square root of the variance
33. Frequency Distribution
data are displayed so that their variety and their frequency of occurrence are both apparent.
34. Class Intervals
grouping the data in a frequency distribution rather than listing them individually
35. Apparent Limits
represented by the lowest and highest integers in the category
36. Actual Limits
extend the interval up and down by 1/2 point
37. Stem and Leaf Display or Stem Plots
liar all values according to stem (the numbers preceding the final value) and leave (the final digit)
38. Pie Charts and Bar Charts
used to represent proportional differences in data categories either by triangular wedges or with bars of different sizes
graphs are created by vertical and horizongal ines which intersect at right angles. The four sections which result are each called a quadrant.
40. Normal Distribution
Gaussian Distribution
takes on the bell shape because it is symmetrical and unimodal and the standard deviation is 1/6 of the range.
41. Point of Inflection
a normal curve moves outward more quickly than downward occurs at +/- one standard deviation from the mean
42. positive skew
when the mean is larger than the median
43. negative skew
when the mean is smaller than the median
44. Kurtosis
• describes how much spread there is in a distribution
• skewness
• defines how bunched up the data is
45. Mesokurtic
• Normal distribution
• standard deviation is about 1/6 R
46. Platykurtic
• distribution with too much variability
• Standard deviation is greater than 1/6 R
47. Leptokurtic
• little variability
• standard deviation is less than 1/6 R
48. Standard Normal Distribution
there is only one standard normal distribution
49. Z transformation
• scores = 0
• standard deviation = 1
50. Modified Standard Score
created so that it has a prespecifiied mean and standard deviation
51. The Distribution of Sample Means
population based on the means of samples rather than on individual scores. it allows one to determine whether a particular sample is likely to have been drawn from the specified population which is the z test
52. Central Limit Theorem
A population of sample means will be normal even if the distribution of individual scores wasn't
53. Sampling Error
the difference between characteristics of the sample and those of the population
54. law of large numbers
indicates that error diminishes as sample size increases
55. Standard Error of the Mean
measure of variability in the distribution of sample means. It is the standard deviation of all the sample means that constitute the distribution of sample means.
56. Statistically Significant
that an outcome isn't likely to have occured by chance
57. Alpha level
• the probability of incorrectly determining a statistically significant result
• occurs if when the null hypothesis is erroneously rejected.
• if further testing with new data indicates that the initial finding of statistical significance was in errror, an alpha error occured with that first test.
58. Type II or Beta error
occurs when one incorrectly concludes that a result isn't statistically significant.
59. Confidence Intervals for Z
intervals within which the population mean represented by a sample will probably occur.
60. stastistics v. parameters
characteristics of sample v characteristics of population

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 Author: julidei ID: 135003 Filename: ERA153 Updated: 2012-02-13 22:44:26 Tags: Educational Statistics Fresno State Folders: Description: terms from the book Show Answers:

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