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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. A quantity or amount adopted as a standard of measurement - such as dollars - hours - or grams
systematic sample
extrapolation
units
response bias
2. When both those who could influence and evaluate the results are blinded
randomized block
double-blind
tails
scatterplots
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
census
re-express data
response variable
extrapolation
4. The ith ___ is the number that falls above i% of the data
residuals
sample
categorical variable
percentile
5. The specific values that the experimenter chooses for a factor
z-score
correlation
level
data table
6. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units
treatment
direction
experiment
prospective study
7. Summarized with the standard deviation - interquartile range - and range
spread
quantitative variable
simple random sample
outlier
8. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
model
distribution
lurking variable
representative
9. An individual result of a component of a simulation
outcome
quantitative variable
form
r2
10. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
tails
regression line
residuals
randomization
11. To be valid - an experiment must assign experimental units to treatment groups at random
random assignment
normal probability plot
intercept
68-95-99.7 rule
12. Any attempt to force a sample to resemble specified attributes of the population
comparing distributions
matching
sampling variability
cluster sample
13. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
placebo effect
bias
undercoverage
population parameter
14. Control - randomize - replicate - block
experimental units
r2
principles of experimental design
experiment
15. Shows a bar representing the count of each category in a categorical variable
leverage
bar chart
double-blind
strength
16. Graphs a dot for each case against a single axis
dotplot
variance
slope
changing center and spread
17. The entire group of individuals or instances about whom we hope to learn
block
experimental units
population
mode
18. Shows the relationship between two quantitative variables measured on the same cases
convenience sample
normal model
interquartile range
scatterplots
19. Values of this record the results of each trial with respect to what we were interested in
response variable
level
stratified random sample
percentile
20. When either those who could influence or evaluate the results is blinded
pie chart
spread
single-blind
independence
21. The most basic situation in a simulation in which something happens at random
area principle
intercept
simulation component
sampling frame
22. A distribution is this if it's not symmetric and one tail stretches out farther than the other
direction
skewed
level
response bias
23. Value calculated from data to summarize aspects of the data
standardizing
voluntary response bias
statistic
experimental units
24. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
nonresponse bias
completely randomized design
residuals
outlier
25. Displays counts and - sometimes - percentages of individuals falling into named categories on two or more variables; categorizes the individuals on all variables at once - to reveal possible patterns in one variable that may be contingent on the cate
contingency table
standard normal model
model
sample size
26. A distribution that's roughly flat
lurking variable
experimental units
random assignment
uniform
27. Bias introduced to a sample when a large fraction of those sampled fails to respond
nonresponse bias
placebo
conditional distribution
r2
28. 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
stratified random sample
normal probability plot
mean
simpson's paradox
29. Gives a value in 'y-units per x-unit'; changes of one unit in x are associated with changes of b1 units in predicted values of y
predicted value
linear model
pie chart
slope
30. A variable in which the numbers act as numerical values; always has units
uniform
quantitative variable
interquartile range
experimental units
31. 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
random numbers
scatterplots
experiment
percentile
32. All experimental units have an equal chance of receiving any treatment
simple random sample
spread
completely randomized design
quartile
33. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
blinding
contingency table
representative
distribution
34. A sampling design in which entire groups are chosen at random
cluster sample
prospective study
random numbers
standard normal model
35. A representative subset of a population - examined in hope of learning about the population
systematic sample
cluster sample
independence
sample
36. An event is this if we know what outcomes could happen - but not which particular values will happen
correlation
stratified random sample
undercoverage
random
37. The difference between the lowest and highest values in a data set
statistic
range
re-express data
random numbers
38. Displays data that change over time
census
timeplot
form
comparing distributions
39. A numerical summary of how tightly the values are clustered around the 'center'
predicted value
area principle
rescaling
spread
40. A variable whose values are compared across different treatments
standard normal model
response
center
random numbers
41. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
categorical variable
variance
regression line
statistic
42. The middle value with half of the data above and half below it
census
simpson's paradox
median
model
43. Numerically valued attribute of a model
statistically significant
stem-and-leaf display
parameter
frequency table
44. 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
quartile
subset
standard deviation
double-blind
45. In a statistical display - each data value should be represented by the same amount of area
residuals
matched
conditional distribution
area principle
46. A variable that names categories (whether with words or numerals)
categorical variable
unimodal
model
comparing distributions
47. Found by substituting the x-value in the regression equation; they're the values on the fitted line
correlation
categorical variable
predicted value
lurking variable
48. A scatterplot shows an association that is this if there is little scatter around the underlying relationship
census
strength
units
68-95-99.7 rule
49. When averages are taken across different groups - they can appear to contradict the overall averages
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50. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
treatment
block
standard normal model
frequency table