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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 parts of a distribution that typically trail off on either side; they can be characterized as long or short
statistic
tails
rescaling
conditional distribution
2. Found by substituting the x-value in the regression equation; they're the values on the fitted line
representative
predicted value
outlier
standardizing
3. The ith ___ is the number that falls above i% of the data
double-blind
percentile
spread
ladder of powers
4. When an observed difference is too large for us to believe that is is likely to have occurred naturally
statistically significant
sample
linear model
independence
5. 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
retrospective study
68-95-99.7 rule
systematic sample
skewed
6. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
leverage
representative
normal probability plot
lurking variable
7. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
random numbers
68-95-99.7 rule
shape
percentile
8. Systematically recorded information - whether numbers or labels - together with its context
data
nonresponse bias
shifting
contingency table
9. 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
histogram
least squares
double-blind
10. A study based on data in which no manipulation of factors has been employed
observational study
influential point
rescaling
lurking variable
11. Displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values
shifting
boxplot
regression line
response variable
12. Summarized with the mean or the median
treatment
context
center
standard normal model
13. 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
predicted value
normal probability plot
r2
rescaling
14. A sampling design in which entire groups are chosen at random
range
cluster sample
regression to the mean
marginal distribution
15. A quantity or amount adopted as a standard of measurement - such as dollars - hours - or grams
subset
units
case
simulation component
16. When groups of experimental units are similar - it is a good idea to gather them together into these
double-blind
census
block
undercoverage
17. Places in order the effects that many re-expressions have on the data
z-score
comparing distributions
r2
ladder of powers
18. Found by summing all the data values and dividing by the count
variable
matching
mean
spread
19. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
frequency table
r2
units
uniform
20. Design Randomization occurring within blocks
randomized block
5-number summary
level
percentile
21. An observational study in which subjects are selected and then their previous conditions or behaviors are determined
dotplot
sample
outlier
retrospective study
22. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
nonresponse bias
outlier
quartile
distribution
23. A variable in which the numbers act as numerical values; always has units
quantitative variable
simulation component
direction
simulation
24. The square root of the variance
variance
conditional distribution
standard deviation
ladder of powers
25. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
confounded
retrospective study
predicted value
sample survey
26. A numerically valued attribute of a model for a population
bimodal
random
68-95-99.7 rule
population parameter
27. Graphs a dot for each case against a single axis
systematic sample
block
retrospective study
dotplot
28. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
outliers
center
representative
timeplot
29. Displays data that change over time
ladder of powers
stem-and-leaf display
timeplot
simple random sample
30. A distribution is this if it's not symmetric and one tail stretches out farther than the other
skewed
randomized block
correlation
simple random sample
31. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
placebo effect
normal probability plot
simulation component
random
32. The difference between the lowest and highest values in a data set
treatment
range
standard normal model
bar chart
33. A sample that consists of the entire population
multimodal
lurking variable
quantitative variable
census
34. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
regression line
response variable
residuals
spread
35. The most basic situation in a simulation in which something happens at random
normal percentile
simulation component
observational study
principles of experimental design
36. A normal model with a mean of 0 and a standard deviation of 1
mean
standard normal model
experiment
randomized block
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
histogram
direction
quartile
pie chart
38. Gives the possible values of the variable and the relative frequency of each value
distribution
completely randomized design
data table
convenience sample
39. Shows quantitative data values in a way that sketches the distribution of the data
percentile
distribution
outcome
stem-and-leaf display
40. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
undercoverage
form
placebo
regression line
41. 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
response
undercoverage
convenience sample
r2
42. The sum of squared deviations from the mean - divided by the count minus one
variance
experiment
scatterplots
principles of experimental design
43. A treatment known to have no effect - administered so that all groups experience the same conditions
prospective study
68-95-99.7 rule
placebo
double-blind
44. Any attempt to force a sample to resemble specified attributes of the population
matching
nonresponse bias
context
interquartile range
45. Any systematic failure of a sampling method to represent its population; common errors are voluntary response - undercoverage - nonresponse ____ - and response ____
bias
direction
slope
center
46. Sampling schemes that combine several sampling methods
bar chart
multistage sample
matching
single-blind
47. A variable whose values are compared across different treatments
standardized value
population
68-95-99.7 rule
response
48. A distribution that's roughly flat
experiment
model
marginal distribution
uniform
49. An equation of the form y-hat = b0 + b1x
regression line
area principle
shifting
linear model
50. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
sampling variability
multistage sample
statistically significant
normal percentile