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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. When groups of experimental units are similar - it is a good idea to gather them together into these
block
sample
double-blind
pie chart
2. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
lurking variable
variable
outcome
5-number summary
3. Distributions with two modes
mean
changing center and spread
bimodal
independence
4. Values of this record the results of each trial with respect to what we were interested in
response variable
data
bimodal
random
5. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
predicted value
residuals
subset
response variable
6. A sampling design in which entire groups are chosen at random
lurking variable
independence
cluster sample
lurking variable
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
re-express data
boxplot
placebo
slope
8. An observational study in which subjects are followed to observe future outcomes
trial
prospective study
simpson's paradox
stem-and-leaf display
9. A representative subset of a population - examined in hope of learning about the population
scatterplots
sample
center
treatment
10. A quantity or amount adopted as a standard of measurement - such as dollars - hours - or grams
spread
predicted value
units
normal probability plot
11. When omitting a point from the data results in a very different regression model - the point is an ____
influential point
regression to the mean
residuals
scatterplots
12. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
sampling variability
center
median
regression to the mean
13. Displays data that change over time
timeplot
representative
skewed
random
14. When an observed difference is too large for us to believe that is is likely to have occurred naturally
population parameter
statistically significant
mean
placebo
15. Distributions with more than two modes
outlier
multimodal
independence
unimodal
16. A sample drawn by selecting individuals systematically from a sampling frame
systematic sample
strength
units
random assignment
17. Anything in a survey design that influences response
linear model
prospective study
response bias
5-number summary
18. 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
marginal distribution
lurking variable
experimental units
case
19. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
sampling frame
independence
68-95-99.7 rule
strength
20. When both those who could influence and evaluate the results are blinded
placebo
standardized value
double-blind
treatment
21. Any systematic failure of a sampling method to represent its population; common errors are voluntary response - undercoverage - nonresponse ____ - and response ____
bias
population parameter
standardized value
bar chart
22. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
changing center and spread
marginal distribution
intercept
population parameter
23. This criterion specifies the unique line that minimizes the variance of the residuals or - equivalently - the sum of the squared residuals
outliers
least squares
stem-and-leaf display
population parameter
24. Extreme values that don't appear to belong with the rest of the data
percentile
linear model
strength
outliers
25. In a statistical display - each data value should be represented by the same amount of area
experiment
area principle
principles of experimental design
bimodal
26. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
stratified random sample
shape
interquartile range
statistically significant
27. Useful family of models for unimodal - symmetric distributions
normal model
outlier
bimodal
undercoverage
28. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
dotplot
standardizing
multimodal
median
29. Shows a bar representing the count of each category in a categorical variable
frequency table
spread
bar chart
unimodal
30. When averages are taken across different groups - they can appear to contradict the overall averages
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31. 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
intercept
shape
spread
32. The entire group of individuals or instances about whom we hope to learn
data
population
distribution
stratified random sample
33. 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
quantitative variable
extrapolation
bimodal
least squares
34. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
lurking variable
simulation component
matching
outlier
35. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
rescaling
pie chart
simple random sample
blinding
36. 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
case
subset
response variable
quartile
37. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
normal percentile
simulation
control group
uniform
38. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
multistage sample
tails
single-blind
blinding
39. An event is this if we know what outcomes could happen - but not which particular values will happen
68-95-99.7 rule
random
r2
rescaling
40. To be valid - an experiment must assign experimental units to treatment groups at random
random assignment
variance
percentile
randomization
41. A normal model with a mean of 0 and a standard deviation of 1
statistically significant
histogram
simpson's paradox
standard normal model
42. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units
tails
data
treatment
matched
43. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
response
simple random sample
blinding
categorical variable
44. The natural tendency of randomly drawn samples to differ
undercoverage
tails
data table
sampling variability
45. Displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values
influential point
boxplot
scatterplots
level
46. Consists of the minimum and maximum - the quartiles Q1 and Q3 - and the median
outcome
bar chart
5-number summary
response bias
47. An equation or formula that simplifies and represents reality
model
convenience sample
random assignment
matched
48. 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
r2
simulation component
changing center and spread
frequency table
49. Shows quantitative data values in a way that sketches the distribution of the data
subset
outliers
stem-and-leaf display
random assignment
50. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
shape
68-95-99.7 rule
strength
regression to the mean