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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. The natural tendency of randomly drawn samples to differ
simulation
sampling variability
regression to the mean
sample
2. Displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values
factor
case
r2
boxplot
3. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
regression to the mean
sample size
single-blind
regression line
4. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
placebo effect
statistic
bimodal
slope
5. Numerically valued attribute of a model
parameter
spread
sampling frame
standard deviation
6. An individual result of a component of a simulation
re-express data
multimodal
outcome
extrapolation
7. Doing this is equivalent to changing its units
normal model
skewed
changing center and spread
matched
8. Value calculated from data to summarize aspects of the data
shifting
statistic
median
blinding
9. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
control group
outliers
double-blind
comparing distributions
10. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units
multimodal
treatment
experimental units
data
11. 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
subset
bar chart
matching
rescaling
12. A variable in which the numbers act as numerical values; always has units
randomized block
population parameter
quantitative variable
contingency table
13. The most basic situation in a simulation in which something happens at random
distribution
data table
randomization
simulation component
14. A distribution that's roughly flat
placebo effect
re-express data
uniform
multimodal
15. The number of individuals in a sample
sample size
blinding
experimental units
random
16. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
scatterplots
symmetric
re-express data
normal percentile
17. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
parameter
standardizing
unimodal
statistically significant
18. Any attempt to force a sample to resemble specified attributes of the population
block
simpson's paradox
nonresponse bias
matching
19. When doing this - consider their shape - center - and spread
data table
stem-and-leaf display
comparing distributions
uniform
20. When averages are taken across different groups - they can appear to contradict the overall averages
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21. Tells how many standard deviations a value is from the mean; have a mean of zero and a standard deviation of one
z-score
level
sampling frame
distribution
22. Displays data that change over time
timeplot
ladder of powers
response
slope
23. A sample drawn by selecting individuals systematically from a sampling frame
dotplot
systematic sample
variable
intercept
24. Distributions with two modes
normal model
direction
bimodal
simulation component
25. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
residuals
experimental units
data table
variance
26. Graphs a dot for each case against a single axis
statistically significant
re-express data
68-95-99.7 rule
dotplot
27. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
confounded
retrospective study
quantitative variable
intercept
28. Found by summing all the data values and dividing by the count
trial
context
mean
population parameter
29. A sample that consists of the entire population
census
mode
linear model
simpson's paradox
30. Shows quantitative data values in a way that sketches the distribution of the data
block
placebo
lurking variable
stem-and-leaf display
31. A variable that names categories (whether with words or numerals)
slope
standardized value
categorical variable
marginal distribution
32. To be valid - an experiment must assign experimental units to treatment groups at random
random assignment
boxplot
lurking variable
simulation component
33. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
mean
sample
principles of experimental design
outlier
34. A numerical measure of the direction and strength of a linear association
correlation
stratified random sample
variable
pie chart
35. Consists of the individuals who are conveniently available
residuals
simple random sample
model
convenience sample
36. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
shape
tails
strength
data table
37. 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
outlier
standard deviation
quartile
undercoverage
38. Anything in a survey design that influences response
stratified random sample
regression to the mean
parameter
response bias
39. 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
regression line
randomization
histogram
cluster sample
40. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
leverage
shape
model
strength
41. Value found by subtracting the mean and dividing by the standard deviation
standard normal model
stratified random sample
standardized value
representative
42. Summarized with the standard deviation - interquartile range - and range
extrapolation
matched
spread
tails
43. When groups of experimental units are similar - it is a good idea to gather them together into these
block
matching
simulation component
principles of experimental design
44. The distribution of a variable restricting the who to consider only a smaller group of individuals
z-score
variable
conditional distribution
census
45. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
regression to the mean
matched
randomization
tails
46. 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
changing center and spread
conditional distribution
percentile
47. Gives the possible values of the variable and the relative frequency of each value
distribution
randomization
mean
influential point
48. Control - randomize - replicate - block
distribution
principles of experimental design
census
quantitative variable
49. A list of individuals from whom the sample is drawn
interquartile range
correlation
completely randomized design
sampling frame
50. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
conditional distribution
bimodal
influential point
standardizing