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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 process - intervention - or other controlled circumstance applied to randomly assigned experimental units
context
population
units
treatment
2. 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
bias
subset
mean
response bias
3. A representative subset of a population - examined in hope of learning about the population
completely randomized design
area principle
random
sample
4. A numerical measure of the direction and strength of a linear association
randomized block
correlation
stem-and-leaf display
nonresponse bias
5. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
center
representative
context
68-95-99.7 rule
6. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
least squares
symmetric
retrospective study
normal percentile
7. A variable that names categories (whether with words or numerals)
categorical variable
multimodal
regression line
randomization
8. A positive ____ or association means that - in general - as one variable increases - so does the other; when increases in one variable generally correspond to decreases in the other - the association is negative
quantitative variable
direction
level
area principle
9. Useful family of models for unimodal - symmetric distributions
lurking variable
population
normal model
predicted value
10. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
quantitative variable
convenience sample
comparing distributions
re-express data
11. Extreme values that don't appear to belong with the rest of the data
changing center and spread
outliers
lurking variable
form
12. An observational study in which subjects are selected and then their previous conditions or behaviors are determined
scatterplots
5-number summary
retrospective study
simpson's paradox
13. A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population
linear model
contingency table
normal percentile
sample survey
14. 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
standardizing
data
median
normal probability plot
15. 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
extrapolation
completely randomized design
intercept
normal probability plot
16. Value calculated from data to summarize aspects of the data
statistic
least squares
rescaling
confounded
17. Graphs a dot for each case against a single axis
distribution
matched
dotplot
sample size
18. 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
histogram
marginal distribution
systematic sample
standard normal model
19. Design Randomization occurring within blocks
trial
standard deviation
outliers
randomized block
20. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
frequency table
outliers
population parameter
linear model
21. An event is this if we know what outcomes could happen - but not which particular values will happen
data
unimodal
variable
random
22. A sample that consists of the entire population
census
spread
variance
normal percentile
23. A distribution that's roughly flat
undercoverage
spread
intercept
uniform
24. 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
experiment
random
residuals
randomization
25. Gives the possible values of the variable and the relative frequency of each value
sample survey
standard deviation
distribution
simpson's paradox
26. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
simple random sample
experiment
shape
variance
27. Summarized with the standard deviation - interquartile range - and range
unimodal
regression line
spread
simple random sample
28. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
response variable
voluntary response bias
residuals
level
29. An equation or formula that simplifies and represents reality
lurking variable
quantitative variable
control group
model
30. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
marginal distribution
random assignment
placebo effect
spread
31. The best defense against bias - in which each individual is given a fair - random chance of selection
sampling frame
changing center and spread
normal model
randomization
32. A sampling design in which entire groups are chosen at random
lurking variable
prospective study
bimodal
cluster sample
33. The sum of squared deviations from the mean - divided by the count minus one
r2
spread
variance
form
34. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
standardizing
median
sampling variability
marginal distribution
35. Numerically valued attribute of a model
response
parameter
residuals
data
36. When omitting a point from the data results in a very different regression model - the point is an ____
outlier
outliers
influential point
convenience sample
37. When both those who could influence and evaluate the results are blinded
matched
context
double-blind
standardizing
38. 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
standardizing
undercoverage
subset
context
39. A numerically valued attribute of a model for a population
center
shape
population parameter
matched
40. Summarized with the mean or the median
form
center
statistically significant
stem-and-leaf display
41. 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
matching
r2
cluster sample
level
42. Control - randomize - replicate - block
random
sample size
principles of experimental design
shape
43. Gives the possible values of the variable and the frequency or relative frequency of each value
boxplot
slope
distribution
simulation component
44. When either those who could influence or evaluate the results is blinded
residuals
comparing distributions
random assignment
single-blind
45. A numerical summary of how tightly the values are clustered around the 'center'
voluntary response bias
spread
outlier
bias
46. An individual about whom or which we have data
extrapolation
case
outliers
simple random sample
47. Value found by subtracting the mean and dividing by the standard deviation
observational study
shape
standardized value
re-express data
48. An equation of the form y-hat = b0 + b1x
linear model
sampling frame
sample
cluster sample
49. Displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values
shifting
boxplot
spread
quantitative variable
50. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
retrospective study
center
control group
representative