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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. 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
percentile
skewed
subset
interquartile range
2. Bias introduced to a sample when individuals can choose on their own whether to participate in the sample
conditional distribution
placebo effect
voluntary response bias
statistic
3. A scatterplot shows an association that is this if there is little scatter around the underlying relationship
systematic sample
slope
strength
representative
4. A list of individuals from whom the sample is drawn
simple random sample
sampling frame
placebo effect
strength
5. Value found by subtracting the mean and dividing by the standard deviation
pie chart
standardized value
stem-and-leaf display
simulation
6. When an observed difference is too large for us to believe that is is likely to have occurred naturally
simulation component
statistically significant
range
parameter
7. 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
standardized value
sample
marginal distribution
8. The best defense against bias - in which each individual is given a fair - random chance of selection
categorical variable
symmetric
randomization
population parameter
9. The distribution of a variable restricting the who to consider only a smaller group of individuals
model
conditional distribution
multimodal
lurking variable
10. 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
context
normal probability plot
standard deviation
lurking variable
11. A variable in which the numbers act as numerical values; always has units
mode
quantitative variable
units
re-express data
12. A representative subset of a population - examined in hope of learning about the population
leverage
sample
histogram
correlation
13. 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
leverage
influential point
cluster sample
form
14. Useful family of models for unimodal - symmetric distributions
convenience sample
outcome
normal model
random numbers
15. Distributions with two modes
uniform
subset
bimodal
voluntary response bias
16. An observational study in which subjects are selected and then their previous conditions or behaviors are determined
cluster sample
quartile
nonresponse bias
retrospective study
17. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
case
confounded
lurking variable
response variable
18. A sample that consists of the entire population
histogram
center
census
multistage sample
19. An observational study in which subjects are followed to observe future outcomes
normal percentile
prospective study
systematic sample
factor
20. 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
factor
census
independence
21. The specific values that the experimenter chooses for a factor
level
experimental units
changing center and spread
range
22. A distribution is this if it's not symmetric and one tail stretches out farther than the other
voluntary response bias
skewed
census
sample
23. 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
level
mean
data
extrapolation
24. Graphs a dot for each case against a single axis
scatterplots
ladder of powers
distribution
dotplot
25. A variable whose levels are controlled by the experimenter
statistically significant
sample size
factor
re-express data
26. Shows how a 'whole' divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category
pie chart
simulation
matched
bar chart
27. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
sample size
intercept
dotplot
unimodal
28. Any attempt to force a sample to resemble specified attributes of the population
simple random sample
matching
68-95-99.7 rule
simpson's paradox
29. Found by substituting the x-value in the regression equation; they're the values on the fitted line
extrapolation
predicted value
uniform
direction
30. Distributions with more than two modes
multimodal
subset
changing center and spread
lurking variable
31. 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
frequency table
randomized block
single-blind
r2
32. All experimental units have an equal chance of receiving any treatment
timeplot
completely randomized design
sampling frame
confounded
33. Anything in a survey design that influences response
case
sample size
response bias
random numbers
34. A sampling design in which entire groups are chosen at random
randomization
response
cluster sample
simple random sample
35. Design Randomization occurring within blocks
randomized block
independence
placebo
lurking variable
36. The difference between the first and third quartiles
experiment
interquartile range
marginal distribution
treatment
37. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
sample survey
units
matching
placebo effect
38. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
matched
stratified random sample
center
regression line
39. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
confounded
uniform
re-express data
influential point
40. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
stratified random sample
completely randomized design
extrapolation
nonresponse bias
41. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
units
outliers
area principle
standardizing
42. 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
lurking variable
skewed
experimental units
quartile
43. 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
correlation
standardized value
experiment
standardizing
44. A normal model with a mean of 0 and a standard deviation of 1
strength
outlier
distribution
standard normal model
45. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
independence
shape
random
context
46. Individuals on whom an experiment is performed
random
experimental units
standard deviation
subset
47. When averages are taken across different groups - they can appear to contradict the overall averages
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48. Doing this is equivalent to changing its units
range
random
changing center and spread
response variable
49. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
lurking variable
context
outcome
mode
50. When both those who could influence and evaluate the results are blinded
shifting
shape
influential point
double-blind