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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 square root of the variance
predicted value
uniform
context
standard deviation
2. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
strength
representative
observational study
population parameter
3. Anything in a survey design that influences response
completely randomized design
response bias
pie chart
undercoverage
4. 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
68-95-99.7 rule
r2
multimodal
rescaling
5. A numerical summary of how tightly the values are clustered around the 'center'
spread
cluster sample
response variable
control group
6. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
unimodal
range
regression line
dotplot
7. 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
68-95-99.7 rule
systematic sample
simpson's paradox
sample survey
8. Value found by subtracting the mean and dividing by the standard deviation
correlation
regression line
z-score
standardized value
9. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
frequency table
completely randomized design
simpson's paradox
census
10. A variable in which the numbers act as numerical values; always has units
quantitative variable
subset
control group
case
11. A numerically valued attribute of a model for a population
response
shape
simulation component
population parameter
12. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
voluntary response bias
population
standardizing
population parameter
13. When averages are taken across different groups - they can appear to contradict the overall averages
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14. A variable whose levels are controlled by the experimenter
response variable
cluster sample
factor
multistage sample
15. Useful family of models for unimodal - symmetric distributions
outcome
normal model
sampling variability
variable
16. The most basic situation in a simulation in which something happens at random
simulation component
matched
level
stem-and-leaf display
17. An arrangement of data in which each row represents a case and each column represents a variable
response bias
standardizing
data table
factor
18. In a retrospective or prospective study Subjects who are similar in ways not under study may be ____ and then compared with each other on the variables of interest
outcome
variable
multimodal
matched
19. The sum of squared deviations from the mean - divided by the count minus one
multimodal
variance
bimodal
dotplot
20. A sample that consists of the entire population
subset
census
single-blind
shape
21. Summarized with the mean or the median
standardized value
mean
center
marginal distribution
22. The ____ we care about most is straight
experimental units
form
skewed
retrospective study
23. An observational study in which subjects are followed to observe future outcomes
level
prospective study
double-blind
control group
24. 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
slope
statistically significant
simulation component
25. 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
model
influential point
data
context
26. The specific values that the experimenter chooses for a factor
cluster sample
level
parameter
spread
27. A sampling design in which entire groups are chosen at random
context
form
ladder of powers
cluster sample
28. The entire group of individuals or instances about whom we hope to learn
population
conditional distribution
units
center
29. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
outlier
residuals
bar chart
placebo effect
30. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
rescaling
double-blind
independence
leverage
31. When both those who could influence and evaluate the results are blinded
retrospective study
double-blind
data
population parameter
32. 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
simple random sample
shape
subset
standard normal model
33. 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
undercoverage
dotplot
simulation component
systematic sample
34. 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
distribution
range
re-express data
mode
35. Numerically valued attribute of a model
random assignment
form
parameter
lurking variable
36. Bias introduced to a sample when individuals can choose on their own whether to participate in the sample
changing center and spread
voluntary response bias
tails
frequency table
37. Sampling schemes that combine several sampling methods
changing center and spread
multistage sample
percentile
sample
38. The sequence of several components representing events that we are pretending will take place
trial
outliers
68-95-99.7 rule
simpson's paradox
39. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
regression line
skewed
normal probability plot
z-score
40. Found by substituting the x-value in the regression equation; they're the values on the fitted line
undercoverage
predicted value
placebo
census
41. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
outlier
subset
normal probability plot
uniform
42. The difference between the lowest and highest values in a data set
regression to the mean
range
68-95-99.7 rule
response
43. An individual result of a component of a simulation
nonresponse bias
form
68-95-99.7 rule
outcome
44. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
center
scatterplots
population parameter
simple random sample
45. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
placebo effect
spread
control group
subset
46. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
placebo effect
rescaling
comparing distributions
simulation
47. When omitting a point from the data results in a very different regression model - the point is an ____
pie chart
randomization
influential point
matching
48. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
rescaling
level
data
lurking variable
49. Shows a bar representing the count of each category in a categorical variable
response bias
direction
bar chart
systematic sample
50. The natural tendency of randomly drawn samples to differ
unimodal
multimodal
influential point
sampling variability