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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. Shows quantitative data values in a way that sketches the distribution of the data
normal probability plot
multimodal
rescaling
stem-and-leaf display
2. The number of individuals in a sample
level
sample size
population parameter
timeplot
3. Consists of the individuals who are conveniently available
stratified random sample
symmetric
convenience sample
completely randomized design
4. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
spread
sample
placebo effect
timeplot
5. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
extrapolation
response variable
unimodal
matched
6. An event is this if we know what outcomes could happen - but not which particular values will happen
randomization
shape
random
z-score
7. Uses adjacent bars to show the distribution of vales in a quantitative variable; each bar represents the frequency (or relative frequency) of values falling in an interval of values
randomization
influential point
histogram
randomized block
8. Gives a value in 'y-units per x-unit'; changes of one unit in x are associated with changes of b1 units in predicted values of y
slope
predicted value
sampling variability
residuals
9. A list of individuals from whom the sample is drawn
sampling frame
placebo effect
ladder of powers
scatterplots
10. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
outlier
principles of experimental design
standardized value
outliers
11. The square root of the variance
normal percentile
case
standard deviation
mode
12. A display to help assess whether a distribution of data is approximately normal; if it is nearly straight - the data satisfy the nearly normal condition
normal model
frequency table
normal probability plot
standard normal model
13. The natural tendency of randomly drawn samples to differ
sampling variability
uniform
least squares
standardized value
14. A sampling design in which entire groups are chosen at random
response
mean
range
cluster sample
15. A sample drawn by selecting individuals systematically from a sampling frame
shape
census
systematic sample
mode
16. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
regression line
convenience sample
r2
conditional distribution
17. A scatterplot shows an association that is this if there is little scatter around the underlying relationship
response
systematic sample
strength
range
18. Distributions with more than two modes
experimental units
nonresponse bias
multimodal
mode
19. A representative subset of a population - examined in hope of learning about the population
sample
variable
distribution
response variable
20. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
marginal distribution
stratified random sample
tails
lurking variable
21. A variable whose values are compared across different treatments
response
lurking variable
spread
simulation component
22. The lower of this is the value with a quarter of the data below it; the upper of this has a quarter of the data above it
quartile
5-number summary
placebo
normal percentile
23. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
categorical variable
stem-and-leaf display
representative
observational study
24. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
trial
sample survey
normal model
residuals
25. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
statistically significant
double-blind
population
center
26. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
bimodal
intercept
sampling variability
subset
27. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
pie chart
placebo effect
standardizing
population
28. If data consist of two or more groups that have been thrown together - it is usually best to fit different linear models to each group than to try to fit a single model to all of the data
simulation component
placebo effect
systematic sample
subset
29. Distributions with two modes
bimodal
quartile
categorical variable
ladder of powers
30. A treatment known to have no effect - administered so that all groups experience the same conditions
variable
representative
placebo
simulation component
31. When either those who could influence or evaluate the results is blinded
population
single-blind
percentile
center
32. Any attempt to force a sample to resemble specified attributes of the population
frequency table
conditional distribution
matching
systematic sample
33. A numerical measure of the direction and strength of a linear association
outcome
timeplot
response
correlation
34. A variable in which the numbers act as numerical values; always has units
bimodal
marginal distribution
strength
quantitative variable
35. Manipulates factor levels to create treatments - randomly assigns subjects to these treatment levels - and then compares the responses of the subject groups across treatment levels
experimental units
outlier
data table
experiment
36. Extreme values that don't appear to belong with the rest of the data
outcome
standardizing
experimental units
outliers
37. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
stratified random sample
uniform
parameter
sample survey
38. Holds information about the same characteristic for many cases
sample survey
variable
context
block
39. A sample that consists of the entire population
correlation
outliers
bias
census
40. Shows the relationship between two quantitative variables measured on the same cases
cluster sample
extrapolation
linear model
scatterplots
41. These are hard to generate - but several websites offer an unlimited supply of equally likely random values
retrospective study
principles of experimental design
random numbers
timeplot
42. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
independence
placebo
symmetric
blinding
43. Places in order the effects that many re-expressions have on the data
ladder of powers
retrospective study
parameter
outcome
44. Value found by subtracting the mean and dividing by the standard deviation
standardized value
matching
interquartile range
retrospective study
45. A normal model with a mean of 0 and a standard deviation of 1
control group
standard normal model
frequency table
census
46. The specific values that the experimenter chooses for a factor
level
sample
normal percentile
simpson's paradox
47. Tells how many standard deviations a value is from the mean; have a mean of zero and a standard deviation of one
level
stem-and-leaf display
confounded
z-score
48. Models random events by using random numbers to specify event outcomes with relative frequencies that correspond to the true real-world relative frequencies we are trying to model
undercoverage
block
shifting
simulation
49. 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
leverage
contingency table
undercoverage
block
50. In a statistical display - each data value should be represented by the same amount of area
bimodal
leverage
area principle
variable