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Test your basic knowledge |
AP Statistics Vocab
Start Test
Study First
Subjects
:
statistics
,
ap
Instructions:
Answer 50 questions in 15 minutes.
If you are not ready to take this test, you can
study here
.
Match each statement with the correct term.
Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.
This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.
1. Data points whose x-values are far from the mean of x are said to exert ____ on a linear model; with high enough ____ - residuals can appear to be deceptively small
leverage
standardizing
population parameter
completely randomized design
2. Systematically recorded information - whether numbers or labels - together with its context
data
predicted value
double-blind
mean
3. A positive ____ or association means that - in general - as one variable increases - so does the other; when increases in one variable generally correspond to decreases in the other - the association is negative
sample size
direction
simulation component
linear model
4. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
center
subset
symmetric
response variable
5. The distribution of either variable alone in a contingency table; the counts or percentages are the totals found in the margins (last row or column) of the table
random numbers
double-blind
normal model
marginal distribution
6. Numerically valued attribute of a model
parameter
area principle
population parameter
histogram
7. Holds information about the same characteristic for many cases
variable
simulation
regression line
percentile
8. An observational study in which subjects are selected and then their previous conditions or behaviors are determined
retrospective study
normal probability plot
skewed
outlier
9. A variable whose values are compared across different treatments
histogram
response
matched
correlation
10. The natural tendency of randomly drawn samples to differ
outcome
sampling variability
data table
extrapolation
11. Bias introduced to a sample when individuals can choose on their own whether to participate in the sample
quartile
stratified random sample
voluntary response bias
ladder of powers
12. When averages are taken across different groups - they can appear to contradict the overall averages
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13. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
representative
median
shape
slope
14. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
standardizing
prospective study
representative
response bias
15. The specific values that the experimenter chooses for a factor
slope
statistically significant
standardized value
level
16. Shows how a 'whole' divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category
undercoverage
dotplot
pie chart
standardized value
17. Shows quantitative data values in a way that sketches the distribution of the data
center
form
stem-and-leaf display
case
18. A hump or local high point in the shape of the distribution of a variable; the apparent locations of these can change as the scale of a histogram is changed
matched
mode
blinding
histogram
19. An arrangement of data in which each row represents a case and each column represents a variable
lurking variable
data table
matched
timeplot
20. A variable that names categories (whether with words or numerals)
categorical variable
re-express data
matching
random numbers
21. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
boxplot
bimodal
categorical variable
stratified random sample
22. Summarized with the mean or the median
single-blind
center
scatterplots
histogram
23. Multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant
boxplot
influential point
rescaling
response
24. In a normal model - about 68% of values fall within 1 standard deviation of the mean - about 95% fall within 2 standard deviations of the mean - and about 99.7% fall within 3 standard deviations of the mean
68-95-99.7 rule
5-number summary
percentile
voluntary response bias
25. A point that does not fit the overall pattern seen in the scatterplot
normal model
outlier
marginal distribution
residuals
26. An event is this if we know what outcomes could happen - but not which particular values will happen
normal model
68-95-99.7 rule
spread
random
27. The difference between the lowest and highest values in a data set
level
trial
range
skewed
28. A distribution is this if the two halves on either side of the center look approximately like mirror images of each other
experimental units
matching
quantitative variable
symmetric
29. All experimental units have an equal chance of receiving any treatment
marginal distribution
completely randomized design
slope
symmetric
30. Summarized with the standard deviation - interquartile range - and range
model
boxplot
factor
spread
31. The ____ we care about most is straight
percentile
quartile
intercept
form
32. The best defense against bias - in which each individual is given a fair - random chance of selection
scatterplots
randomization
sample
5-number summary
33. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
standardizing
form
bimodal
5-number summary
34. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
sample
strength
standard normal model
simple random sample
35. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
stem-and-leaf display
leverage
unimodal
categorical variable
36. Consists of the minimum and maximum - the quartiles Q1 and Q3 - and the median
distribution
experimental units
double-blind
5-number summary
37. In a statistical display - each data value should be represented by the same amount of area
completely randomized design
voluntary response bias
area principle
symmetric
38. A sample drawn by selecting individuals systematically from a sampling frame
spread
case
systematic sample
population
39. A sample that consists of the entire population
census
matched
undercoverage
principles of experimental design
40. Individuals on whom an experiment is performed
model
experimental units
outcome
standardizing
41. Displays counts and - sometimes - percentages of individuals falling into named categories on two or more variables; categorizes the individuals on all variables at once - to reveal possible patterns in one variable that may be contingent on the cate
normal percentile
random assignment
contingency table
frequency table
42. Displays data that change over time
timeplot
changing center and spread
median
regression to the mean
43. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
data table
strength
sample size
intercept
44. When both those who could influence and evaluate the results are blinded
case
double-blind
sample
sample size
45. The difference between the first and third quartiles
outcome
interquartile range
form
lurking variable
46. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
placebo
experiment
blinding
trial
47. An individual result of a component of a simulation
timeplot
distribution
outcome
placebo effect
48. A sampling scheme that biases the sample in a way that gives a part of the population less representation than it has in the population
sampling variability
undercoverage
sampling frame
variance
49. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
lurking variable
cluster sample
random assignment
distribution
50. Design Randomization occurring within blocks
randomized block
predicted value
variance
random assignment