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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. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
blinding
matching
systematic sample
variance
2. An observational study in which subjects are selected and then their previous conditions or behaviors are determined
control group
retrospective study
influential point
simpson's paradox
3. 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
lurking variable
extrapolation
outlier
variable
4. These are hard to generate - but several websites offer an unlimited supply of equally likely random values
outlier
bar chart
random numbers
matching
5. An equation of the form y-hat = b0 + b1x
extrapolation
linear model
histogram
influential point
6. Extreme values that don't appear to belong with the rest of the data
outliers
68-95-99.7 rule
extrapolation
cluster sample
7. Found by substituting the x-value in the regression equation; they're the values on the fitted line
predicted value
unimodal
symmetric
area principle
8. The number of individuals in a sample
sample size
stem-and-leaf display
case
subset
9. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
nonresponse bias
shape
center
interquartile range
10. Distributions with more than two modes
bimodal
confounded
shifting
multimodal
11. Design Randomization occurring within blocks
response
subset
ladder of powers
randomized block
12. Found by summing all the data values and dividing by the count
representative
mean
changing center and spread
convenience sample
13. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
units
lurking variable
tails
completely randomized design
14. A numerically valued attribute of a model for a population
bar chart
retrospective study
population parameter
bias
15. The square root of the variance
predicted value
response
experiment
standard deviation
16. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
spread
unimodal
stem-and-leaf display
uniform
17. Consists of the individuals who are conveniently available
convenience sample
normal percentile
least squares
standardized value
18. 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
control group
range
timeplot
r2
19. A numerical summary of how tightly the values are clustered around the 'center'
outliers
completely randomized design
spread
shape
20. Displays data that change over time
standard deviation
strength
timeplot
conditional distribution
21. An arrangement of data in which each row represents a case and each column represents a variable
data table
leverage
matched
placebo effect
22. Bias introduced to a sample when a large fraction of those sampled fails to respond
sample size
nonresponse bias
census
pie chart
23. Shows how a 'whole' divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category
dotplot
range
unimodal
pie chart
24. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
extrapolation
form
representative
prospective study
25. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
outlier
5-number summary
center
stratified random sample
26. 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
block
quartile
spread
systematic sample
27. An event is this if we know what outcomes could happen - but not which particular values will happen
outlier
contingency table
random
sample
28. A variable that names categories (whether with words or numerals)
convenience sample
spread
categorical variable
bimodal
29. 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
boxplot
sample size
simple random sample
30. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
multimodal
distribution
contingency table
center
31. A sample drawn by selecting individuals systematically from a sampling frame
bias
intercept
systematic sample
z-score
32. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
standard deviation
outlier
observational study
sampling variability
33. Doing this is equivalent to changing its units
histogram
changing center and spread
random
slope
34. Summarized with the standard deviation - interquartile range - and range
single-blind
simulation component
standardized value
spread
35. A normal model with a mean of 0 and a standard deviation of 1
standard normal model
slope
block
randomization
36. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
random assignment
intercept
census
confounded
37. 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
re-express data
placebo effect
standardized value
subset
38. When both those who could influence and evaluate the results are blinded
standardizing
outlier
simple random sample
double-blind
39. When an observed difference is too large for us to believe that is is likely to have occurred naturally
statistically significant
uniform
frequency table
response variable
40. The best defense against bias - in which each individual is given a fair - random chance of selection
lurking variable
population
randomization
regression to the mean
41. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
confounded
control group
factor
z-score
42. 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
residuals
form
conditional distribution
undercoverage
43. Any systematic failure of a sampling method to represent its population; common errors are voluntary response - undercoverage - nonresponse ____ - and response ____
sample size
bias
random assignment
quantitative variable
44. All experimental units have an equal chance of receiving any treatment
changing center and spread
unimodal
normal model
completely randomized design
45. Gives the possible values of the variable and the frequency or relative frequency of each value
symmetric
level
distribution
mean
46. When omitting a point from the data results in a very different regression model - the point is an ____
correlation
influential point
quartile
response bias
47. A point that does not fit the overall pattern seen in the scatterplot
frequency table
distribution
model
outlier
48. The most basic situation in a simulation in which something happens at random
simpson's paradox
undercoverage
uniform
simulation component
49. When averages are taken across different groups - they can appear to contradict the overall averages
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50. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
re-express data
simple random sample
normal model
blinding