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Statistical Methods > Chapter 2 > Flashcards

Chapter 2 Flashcards

1
Q

frequency distribution

A

records number of times each possible thing occurs during an experiment

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2
Q

histogram

A

groups adjacent values together to give a visual picture, obscuring noise while preserving important data trends, looks like bar graph

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3
Q

real lower limit

A

smallest value that would be classed as falling into the interval, like rounding

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4
Q

real upper limit

A

largest value that would be classed as being in the interval, like rounding

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5
Q

midpoint

A

average of the upper and lower limit presented for convenience

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6
Q

outlier

A

extreme value that is widely separated from the rest of the data, frequently representing errors in recording data (but not always)

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7
Q

normal curve

A

bell-shaped curve that is symmetrical around the center of the distribution

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8
Q

kernel density plot

A

pays no attention to mean and standard deviation, instead holds to the idea that each observation might have been slightly different

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9
Q

stem-and-leaf display

A

Tukey, exploratory data analysis, helpful for comparing 2 different distributions

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10
Q

leading digits

A

most significant digits, form the stem (vertical axis) of the stem-and-leaf display

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11
Q

stem

A

vertical axis of the stem-and-leaf display, formed by the leading digits/most significant digits

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12
Q

trailing digits

A

less significant digits, form the leaves (horizontal elements) of the stem-and-leaf display

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13
Q

leaves

A

horizontal elements of the stem-and-leaf display, formed by the trailing digits/less significant digits

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14
Q

bimodal

A

graph having two predominant peaks instead of one (even when these peaks are not exactly the same height)

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15
Q

unimodal

A

distribution having only one major peak

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16
Q

modality

A

refers to the number of major peaks in a distribution

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17
Q

negatively skewed

A

distribution with tail going out to the right (they point to the negative)

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18
Q

positively skewed

A

distribution with tail going out to the left (they point toward the positive)

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19
Q

skewness

A

statistical measures of the degree of asymmetry

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20
Q

kurtosis

A

the relative concentration of scores in the center, the upper and lower ends (tails) and the shoulders (between the center of the tails) of a distribution

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21
Q

mesokurtic

A

a normal distribution, with tails normally proportioned (neither too thick nor too thin) and with center normally shaped (neither too many nor too few scores concentrated there)

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22
Q

platykurtic

A

flatter-shaped distribution where scores are concentrated in the shoulders (pulled in from the tails and down from the center)

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23
Q

leptokurtic

A

distribution with higher-than-normal center peak and thicker-than-normal tails

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24
Q

sigma

A

standard notation for sum (adds up to)

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25
Q

measures of central tendency

A

different statistics that measure the “center” of the distribution

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26
Q

measures of location

A

reflect where on the scale the distribution is centered

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27
Q

mode

A

Mo-most common score, advantage: represents the largest number of people & unaffected by extreme scores

28
Q

median

A

Mdn-corresponds to the point at or below which 50% of the scores fall when the data are arranged in numerical order, advantage: unaffected by extreme scores

29
Q

median location

A

(N+1)/2

30
Q

mean

A

X bar, sum of scores divided by # of scores, disadvantage: influenced by extreme scores, value may not actually exist in the data; advantage: can be manipulated algebraically, estimates the population well

31
Q

relation of measures of central tendency to one another

A

whenever the distribution is normal (unimodal and symmetric), the mean, median, and mode will all be close to one another

32
Q

trimmed means

A

means calculated on data for which we have discarded a certain percentage of the data at each end of the distribution, to weaken the effects extreme scores have on the mean and to use a population estimate with a small standard of error

33
Q

dispersion

A

variability around the central measure of tendency (usually around the mean)

34
Q

range

A

measure of distance from lowest to highest score, relies on the extremes and so may be a distorted picture of the variability

35
Q

interquartile range

A

discards upper and lower 25%ages of scores, leaving middle half to make up the range (Q3-Q1), can discard too much of the data to be good representative of a sample

36
Q

first quartile

A

point that cuts off the lowest 25% of a distribution, Q1

37
Q

third quartile

A

point that cuts off the upper 25% of a distribution, Q3

38
Q

second quartile

A

median of a distribution, Q2

39
Q

Winsorized sample

A

using trimmed samples to estimate variability, dropping a %age of the highest and lowest scores and replacing them with copies of the highest and lowest remaining scores

40
Q

absolute value

A

positive expression of an integer (for example, the absolute value of -3 is 3.)

41
Q

mean absolute deviation

A

turning all numbers into their absolute values (eliminating negative numbers) prior to finding the mean to determine deviation from the mean

42
Q

standard deviation

A

sum of all (X-Xbar), squared
divided by
N-1

43
Q

sample variance

A

(s squared), part of the whole population

44
Q

population variance

A

(sigma squared), whole population

45
Q

coefficient of variation

A

CV=(standard deviation / mean) X 100, to express the answer as a percentage; to determine which of two groups/tests is better

46
Q

statistics

A

characteristics of samples, designated by Roman letters

47
Q

parameters

A

characteristics of populations, designated by Greek lettes

48
Q

population mean

A

symbolized by the Greek mu

49
Q

expected value

A

long-range average of many samples

50
Q

unbiased estimate

A

estimator whose expected value equals the parameter to be estimated

51
Q

degrees of freedom

A

df: losing one degree of freedom (dividing by N-1 instead of just by N) because mu is not known and must be estimated from the sample mean

52
Q

boxplot

A

aka box-and-whisker plot-method of looking at data, designed by Tukey, includes a scale that covers the whole range of obtained values, a rectangular box drawn from Q1 to Q3 with a vertical line representing the median, and lines called whiskers from the quartiles out to the adjacent values

53
Q

quartile location

A

taking the 1st and 3rd quartiles, (median location +1)/2

54
Q

inner fences

A

point that falls 1.5 times the interquartile range below or above the appropriate quartile

55
Q

adjacent values

A

those actual values in the data that are no more extreme (no farther from the median) than the inner fences

56
Q

deciles

A

like quartiles, but divide the distribution into 10ths rather than quarters

57
Q

percentiles

A

divide the distribution into hundredths

58
Q

quantiles/fractiles

A

dividing data into chunks for statistical purposes, like percentiles

59
Q

linear transformations

A

multiplying a value by a constant and adding a constant to express the same value in a new way (like converting Celsius degrees into Fahrenheit degrees)

60
Q

nonlinear transformations

A

using exponents, logarithms, trigonometric functions, etc. to transform values into another expression, usually involve a change in shape of a distribution

61
Q

centering

A

subtracting sample mean from all of the observations, rendering the new mean 0.00 but not affecting the standard deviation or the variance

62
Q

reflection

A

preventing subjects from simply checking the same point on the scale all the way down without thinking by reversing the phrasing of the questions (half could be positive, like “strongly agree”, and half could be negative, like “strongly disagree”), accomplished by a linear transformation

63
Q

deviation scores

A

employed to rescale data, subtracting mean from each observation

64
Q

standard scores

A

creating deviation scores and then dividing them by the standard deviation

65
Q

standardization

A

creating standard scores from raw scores

Statistical Methods (5 decks)

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