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Test your basic knowledge |
AP Statistics Vocab
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Subjects
:
statistics
,
ap
Instructions:
Answer 50 questions in 15 minutes.
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.
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. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
simulation
residuals
normal probability plot
area principle
2. When omitting a point from the data results in a very different regression model - the point is an ____
interquartile range
confounded
intercept
influential point
3. Summarized with the standard deviation - interquartile range - and range
least squares
spread
5-number summary
simple random sample
4. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
ladder of powers
re-express data
confounded
area principle
5. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
control group
stratified random sample
sample survey
simulation component
6. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
simple random sample
ladder of powers
randomization
correlation
7. Ideally tells who was measured - what was measured - how the data were collected - where the data were collected - and when and why the study was performed
form
stem-and-leaf display
block
context
8. 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
z-score
unimodal
direction
nonresponse bias
9. Adding a constant to each data value adds the same constant to the mean - the median - and the quartiles - but does not change the standard deviation or IQR
bimodal
units
lurking variable
shifting
10. Summarized with the mean or the median
data
population
center
stem-and-leaf display
11. Multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant
single-blind
conditional distribution
context
rescaling
12. A sample that consists of the entire population
census
retrospective study
shape
spread
13. When doing this - consider their shape - center - and spread
comparing distributions
center
least squares
changing center and spread
14. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
prospective study
z-score
tails
68-95-99.7 rule
15. 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
marginal distribution
blinding
systematic sample
slope
16. The square of the correlation between y and x; gives the fraction of the variability of y accounted for by the least squares linear regression on x; an overall measure of how successful the regression is in linearly relating y to x
residuals
r2
symmetric
uniform
17. The ____ we care about most is straight
form
rescaling
cluster sample
normal probability plot
18. Places in order the effects that many re-expressions have on the data
correlation
sampling variability
block
ladder of powers
19. Gives the possible values of the variable and the relative frequency of each value
single-blind
pie chart
area principle
distribution
20. The square root of the variance
ladder of powers
standard deviation
histogram
random numbers
21. A representative subset of a population - examined in hope of learning about the population
multistage sample
sample
regression to the mean
case
22. An equation of the form y-hat = b0 + b1x
standard normal model
normal percentile
linear model
median
23. Although linear models provide an easy way to predict values of y for a given value of x - it is unsafe to predict for values of x far from the ones used to find the linear model equation; predictions should not be trusted
shape
experiment
extrapolation
normal percentile
24. These are hard to generate - but several websites offer an unlimited supply of equally likely random values
marginal distribution
statistic
random numbers
distribution
25. Found by summing all the data values and dividing by the count
area principle
placebo effect
mean
ladder of powers
26. A sample drawn by selecting individuals systematically from a sampling frame
sample survey
principles of experimental design
pie chart
systematic sample
27. Displays data that change over time
predicted value
level
residuals
timeplot
28. Sampling schemes that combine several sampling methods
multistage sample
randomization
changing center and spread
center
29. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
standardizing
direction
form
normal model
30. Value calculated from data to summarize aspects of the data
population
skewed
statistic
direction
31. An individual result of a component of a simulation
outcome
standardizing
cluster sample
interquartile range
32. A distribution that's roughly flat
uniform
standard normal model
data
treatment
33. An arrangement of data in which each row represents a case and each column represents a variable
experimental units
linear model
data table
single-blind
34. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
center
sampling variability
representative
population
35. A variable whose values are compared across different treatments
response
control group
comparing distributions
context
36. Individuals on whom an experiment is performed
symmetric
random assignment
multimodal
experimental units
37. A sampling design in which entire groups are chosen at random
frequency table
principles of experimental design
z-score
cluster sample
38. 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
experiment
completely randomized design
sample size
response bias
39. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
distribution
unimodal
regression to the mean
parameter
40. The difference between the first and third quartiles
mean
parameter
interquartile range
bias
41. Bias introduced to a sample when individuals can choose on their own whether to participate in the sample
regression to the mean
shifting
voluntary response bias
population parameter
42. The natural tendency of randomly drawn samples to differ
bar chart
convenience sample
dotplot
sampling variability
43. When either those who could influence or evaluate the results is blinded
normal model
lurking variable
single-blind
principles of experimental design
44. An event is this if we know what outcomes could happen - but not which particular values will happen
sample
distribution
spread
random
45. When groups of experimental units are similar - it is a good idea to gather them together into these
sample size
normal probability plot
block
factor
46. Doing this is equivalent to changing its units
model
units
changing center and spread
uniform
47. Distributions with two modes
uniform
correlation
randomized block
bimodal
48. When both those who could influence and evaluate the results are blinded
form
outliers
simpson's paradox
double-blind
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
random
re-express data
contingency table
undercoverage
50. Anything in a survey design that influences response
form
response bias
boxplot
block
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