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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. Shows a bar representing the count of each category in a categorical variable
bar chart
standardizing
normal model
pie chart
2. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
case
multistage sample
median
frequency table
3. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
treatment
undercoverage
shifting
outlier
4. Extreme values that don't appear to belong with the rest of the data
data table
outliers
regression to the mean
standard deviation
5. The difference between the first and third quartiles
interquartile range
prospective study
boxplot
distribution
6. A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population
sample survey
sample size
cluster sample
randomization
7. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
standard deviation
re-express data
response bias
68-95-99.7 rule
8. Shows quantitative data values in a way that sketches the distribution of the data
stem-and-leaf display
regression line
marginal distribution
standardized value
9. The entire group of individuals or instances about whom we hope to learn
random
spread
population
quantitative variable
10. An observational study in which subjects are selected and then their previous conditions or behaviors are determined
intercept
5-number summary
standardized value
retrospective study
11. When groups of experimental units are similar - it is a good idea to gather them together into these
distribution
timeplot
block
histogram
12. 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
shifting
stem-and-leaf display
linear model
simple random sample
13. Displays data that change over time
systematic sample
retrospective study
timeplot
quantitative variable
14. Anything in a survey design that influences response
standard deviation
pie chart
cluster sample
response bias
15. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
conditional distribution
simple random sample
sampling variability
independence
16. A sample that consists of the entire population
variance
percentile
undercoverage
census
17. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
cluster sample
standard deviation
linear model
control group
18. 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
confounded
simulation
5-number summary
strength
19. A sample drawn by selecting individuals systematically from a sampling frame
mode
residuals
matching
systematic sample
20. 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
random
marginal distribution
response
distribution
21. Value found by subtracting the mean and dividing by the standard deviation
census
standardized value
correlation
form
22. An arrangement of data in which each row represents a case and each column represents a variable
data table
boxplot
residuals
simulation component
23. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
68-95-99.7 rule
placebo effect
statistic
marginal distribution
24. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
regression to the mean
boxplot
bias
lurking variable
25. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
direction
bias
blinding
variance
26. Doing this is equivalent to changing its units
changing center and spread
observational study
strength
center
27. Data points whose x-values are far from the mean of x are said to exert ____ on a linear model; with high enough ____ - residuals can appear to be deceptively small
population
sample survey
representative
leverage
28. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
response bias
simpson's paradox
area principle
tails
29. Holds information about the same characteristic for many cases
random numbers
quartile
variable
response
30. The distribution of a variable restricting the who to consider only a smaller group of individuals
symmetric
re-express data
conditional distribution
mean
31. An individual result of a component of a simulation
experimental units
outcome
principles of experimental design
stratified random sample
32. A normal model with a mean of 0 and a standard deviation of 1
voluntary response bias
form
randomization
standard normal model
33. Design Randomization occurring within blocks
prospective study
dotplot
randomized block
response
34. A list of individuals from whom the sample is drawn
data
outliers
sampling frame
simulation component
35. The best defense against bias - in which each individual is given a fair - random chance of selection
randomization
median
response bias
blinding
36. A variable that names categories (whether with words or numerals)
statistically significant
center
categorical variable
outliers
37. When both those who could influence and evaluate the results are blinded
double-blind
r2
residuals
nonresponse bias
38. 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
trial
matched
timeplot
quartile
39. 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
quantitative variable
standardizing
random assignment
68-95-99.7 rule
40. 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
unimodal
parameter
histogram
interquartile range
41. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
response variable
independence
mode
shape
42. The ____ we care about most is straight
form
scatterplots
conditional distribution
shifting
43. When doing this - consider their shape - center - and spread
single-blind
comparing distributions
leverage
independence
44. Summarized with the standard deviation - interquartile range - and range
68-95-99.7 rule
histogram
bimodal
spread
45. A distribution is this if it's not symmetric and one tail stretches out farther than the other
ladder of powers
skewed
mean
standard deviation
46. The ith ___ is the number that falls above i% of the data
data table
r2
percentile
randomization
47. These are hard to generate - but several websites offer an unlimited supply of equally likely random values
random numbers
normal model
sample survey
mean
48. A variable whose levels are controlled by the experimenter
predicted value
random numbers
parameter
factor
49. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
sample size
blinding
standardizing
placebo
50. A numerically valued attribute of a model for a population
blinding
population parameter
response
interquartile range