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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. Places in order the effects that many re-expressions have on the data
sampling frame
completely randomized design
ladder of powers
conditional distribution
2. A normal model with a mean of 0 and a standard deviation of 1
sample size
standard normal model
uniform
normal model
3. 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
bias
experiment
blinding
placebo
4. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
blinding
histogram
direction
stratified random sample
5. A variable in which the numbers act as numerical values; always has units
quantitative variable
correlation
representative
68-95-99.7 rule
6. The sum of squared deviations from the mean - divided by the count minus one
data table
variance
standard deviation
randomized block
7. 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
sample
unimodal
outlier
8. When groups of experimental units are similar - it is a good idea to gather them together into these
tails
retrospective study
random
block
9. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
experiment
distribution
level
lurking variable
10. An event is this if we know what outcomes could happen - but not which particular values will happen
standardized value
5-number summary
representative
random
11. A sampling design in which entire groups are chosen at random
ladder of powers
cluster sample
histogram
intercept
12. A sample drawn by selecting individuals systematically from a sampling frame
variance
center
systematic sample
cluster sample
13. Doing this is equivalent to changing its units
quartile
sample
strength
changing center and spread
14. Any attempt to force a sample to resemble specified attributes of the population
spread
matching
regression line
representative
15. An individual result of a component of a simulation
treatment
timeplot
lurking variable
outcome
16. Design Randomization occurring within blocks
treatment
randomized block
simpson's paradox
observational study
17. All experimental units have an equal chance of receiving any treatment
principles of experimental design
ladder of powers
placebo effect
completely randomized design
18. Distributions with more than two modes
multimodal
dotplot
direction
pie chart
19. When averages are taken across different groups - they can appear to contradict the overall averages
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20. Found by summing all the data values and dividing by the count
68-95-99.7 rule
categorical variable
mean
randomization
21. Summarized with the mean or the median
linear model
center
variable
level
22. The ____ we care about most is straight
response
model
form
outcome
23. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
center
scatterplots
variance
response bias
24. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
standardized value
control group
block
random assignment
25. Displays data that change over time
scatterplots
outcome
timeplot
stratified random sample
26. The most basic situation in a simulation in which something happens at random
timeplot
factor
simulation component
slope
27. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
least squares
tails
bias
normal model
28. Bias introduced to a sample when a large fraction of those sampled fails to respond
variance
ladder of powers
uniform
nonresponse bias
29. When omitting a point from the data results in a very different regression model - the point is an ____
independence
frequency table
influential point
outlier
30. A variable whose values are compared across different treatments
independence
response
single-blind
subset
31. 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
shifting
lurking variable
confounded
32. 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 probability plot
simulation component
residuals
least squares
33. 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
simulation
census
boxplot
mode
34. 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
scatterplots
regression to the mean
spread
35. The ith ___ is the number that falls above i% of the data
percentile
frequency table
matched
slope
36. The middle value with half of the data above and half below it
convenience sample
pie chart
direction
median
37. Sampling schemes that combine several sampling methods
simpson's paradox
intercept
multistage sample
random assignment
38. 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
strength
variance
sample size
68-95-99.7 rule
39. Individuals on whom an experiment is performed
case
simulation component
experimental units
spread
40. The difference between the first and third quartiles
statistically significant
principles of experimental design
interquartile range
linear model
41. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
slope
data table
regression line
histogram
42. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
spread
stratified random sample
confounded
census
43. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
regression line
simple random sample
random numbers
normal model
44. Extreme values that don't appear to belong with the rest of the data
simpson's paradox
changing center and spread
observational study
outliers
45. 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
standardizing
spread
data
simulation
46. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units
data table
simulation
unimodal
treatment
47. A study based on data in which no manipulation of factors has been employed
standardized value
quartile
observational study
sample size
48. When an observed difference is too large for us to believe that is is likely to have occurred naturally
response
statistically significant
center
sampling frame
49. A distribution that's roughly flat
uniform
data
simpson's paradox
scatterplots
50. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
independence
range
histogram
quantitative variable