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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 number of individuals in a sample
sample size
blinding
standardizing
sampling variability
2. The square root of the variance
standard deviation
factor
context
pie chart
3. A variable in which the numbers act as numerical values; always has units
sampling frame
sample
range
quantitative variable
4. 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
standardizing
histogram
data table
normal percentile
5. Any systematic failure of a sampling method to represent its population; common errors are voluntary response - undercoverage - nonresponse ____ - and response ____
slope
categorical variable
standard normal model
bias
6. Value calculated from data to summarize aspects of the data
statistic
placebo
experiment
model
7. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
stratified random sample
independence
contingency table
percentile
8. Distributions with two modes
direction
68-95-99.7 rule
pie chart
bimodal
9. In a retrospective or prospective study Subjects who are similar in ways not under study may be ____ and then compared with each other on the variables of interest
least squares
normal model
matched
placebo effect
10. 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
skewed
symmetric
completely randomized design
subset
11. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
experimental units
regression line
least squares
population
12. A list of individuals from whom the sample is drawn
treatment
sampling frame
lurking variable
subset
13. The most basic situation in a simulation in which something happens at random
variable
simulation component
strength
range
14. Numerically valued attribute of a model
parameter
systematic sample
response variable
matching
15. Places in order the effects that many re-expressions have on the data
ladder of powers
cluster sample
placebo
tails
16. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units
standard normal model
predicted value
treatment
categorical variable
17. Anything in a survey design that influences response
r2
response bias
treatment
matched
18. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
scatterplots
re-express data
variable
simulation component
19. The best defense against bias - in which each individual is given a fair - random chance of selection
lurking variable
conditional distribution
randomization
level
20. 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
shifting
parameter
direction
normal model
21. Design Randomization occurring within blocks
randomized block
representative
response variable
subset
22. Values of this record the results of each trial with respect to what we were interested in
marginal distribution
response variable
multimodal
standardized value
23. Summarized with the mean or the median
parameter
outlier
correlation
center
24. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
pie chart
spread
unimodal
68-95-99.7 rule
25. An event is this if we know what outcomes could happen - but not which particular values will happen
random
normal model
multimodal
sample survey
26. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
conditional distribution
lurking variable
convenience sample
subset
27. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
categorical variable
intercept
principles of experimental design
dotplot
28. Shows a bar representing the count of each category in a categorical variable
standard normal model
completely randomized design
bar chart
control group
29. Consists of the individuals who are conveniently available
distribution
convenience sample
ladder of powers
68-95-99.7 rule
30. Extreme values that don't appear to belong with the rest of the data
direction
outliers
skewed
stratified random sample
31. 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
influential point
cluster sample
standard normal model
leverage
32. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
center
random numbers
boxplot
standardizing
33. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
model
census
lurking variable
units
34. A treatment known to have no effect - administered so that all groups experience the same conditions
placebo
lurking variable
simpson's paradox
influential point
35. A normal model with a mean of 0 and a standard deviation of 1
z-score
standard normal model
stem-and-leaf display
outlier
36. 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
area principle
frequency table
interquartile range
undercoverage
37. Found by summing all the data values and dividing by the count
cluster sample
mean
random numbers
multistage sample
38. An equation of the form y-hat = b0 + b1x
linear model
spread
nonresponse bias
direction
39. A variable whose values are compared across different treatments
slope
regression to the mean
response
regression line
40. A quantity or amount adopted as a standard of measurement - such as dollars - hours - or grams
quantitative variable
units
standardizing
outliers
41. An observational study in which subjects are selected and then their previous conditions or behaviors are determined
area principle
retrospective study
units
voluntary response bias
42. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
bar chart
residuals
factor
placebo
43. The specific values that the experimenter chooses for a factor
level
control group
statistic
regression line
44. Ideally tells who was measured - what was measured - how the data were collected - where the data were collected - and when and why the study was performed
context
comparing distributions
contingency table
normal model
45. A study based on data in which no manipulation of factors has been employed
level
observational study
context
outliers
46. A numerical summary of how tightly the values are clustered around the 'center'
observational study
tails
spread
units
47. The natural tendency of randomly drawn samples to differ
sample
skewed
r2
sampling variability
48. Shows the relationship between two quantitative variables measured on the same cases
scatterplots
prospective study
standardized value
variance
49. Displays data that change over time
timeplot
statistic
blinding
mode
50. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
center
boxplot
observational study
treatment