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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. An individual about whom or which we have data
case
percentile
control group
intercept
2. An equation of the form y-hat = b0 + b1x
representative
unimodal
interquartile range
linear model
3. When either those who could influence or evaluate the results is blinded
sampling variability
center
shape
single-blind
4. The difference between the lowest and highest values in a data set
distribution
variable
simple random sample
range
5. The number of individuals in a sample
outliers
simulation component
sample size
simpson's paradox
6. The difference between the first and third quartiles
shape
regression line
interquartile range
random assignment
7. 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
sample
undercoverage
normal probability plot
sampling variability
8. When an observed difference is too large for us to believe that is is likely to have occurred naturally
form
response variable
median
statistically significant
9. A numerical measure of the direction and strength of a linear association
population
data table
statistic
correlation
10. The best defense against bias - in which each individual is given a fair - random chance of selection
simpson's paradox
residuals
randomization
matched
11. Shows the relationship between two quantitative variables measured on the same cases
randomized block
unimodal
scatterplots
nonresponse bias
12. An event is this if we know what outcomes could happen - but not which particular values will happen
random
data
conditional distribution
model
13. The ____ we care about most is straight
confounded
least squares
leverage
form
14. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
statistically significant
lurking variable
timeplot
correlation
15. Places in order the effects that many re-expressions have on the data
undercoverage
ladder of powers
linear model
sample
16. These are hard to generate - but several websites offer an unlimited supply of equally likely random values
statistic
factor
random numbers
prospective study
17. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
symmetric
representative
simulation component
matched
18. Value found by subtracting the mean and dividing by the standard deviation
comparing distributions
sample size
tails
standardized value
19. Holds information about the same characteristic for many cases
variable
categorical variable
randomized block
regression line
20. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
confounded
bias
shifting
completely randomized design
21. The middle value with half of the data above and half below it
level
median
placebo
simpson's paradox
22. 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
response
outlier
r2
pie chart
23. A variable whose values are compared across different treatments
response
interquartile range
slope
lurking variable
24. Distributions with more than two modes
uniform
multimodal
percentile
simulation component
25. Displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values
boxplot
normal percentile
spread
independence
26. The distribution of a variable restricting the who to consider only a smaller group of individuals
conditional distribution
bimodal
least squares
sampling frame
27. A variable in which the numbers act as numerical values; always has units
observational study
sample size
timeplot
quantitative variable
28. Extreme values that don't appear to belong with the rest of the data
outliers
representative
strength
uniform
29. Individuals on whom an experiment is performed
experimental units
population
z-score
timeplot
30. Design Randomization occurring within blocks
range
standard normal model
randomized block
context
31. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
interquartile range
r2
systematic sample
regression to the mean
32. Multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant
rescaling
representative
treatment
quartile
33. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
simulation
area principle
shifting
normal percentile
34. 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
census
systematic sample
marginal distribution
boxplot
35. A representative subset of a population - examined in hope of learning about the population
frequency table
statistically significant
sample
re-express data
36. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
randomization
extrapolation
spread
independence
37. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
randomization
form
placebo effect
area principle
38. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
regression line
block
quartile
observational study
39. Bias introduced to a sample when individuals can choose on their own whether to participate in the sample
leverage
least squares
voluntary response bias
range
40. A list of individuals from whom the sample is drawn
sampling frame
block
parameter
comparing distributions
41. 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
data
symmetric
statistically significant
42. Shows quantitative data values in a way that sketches the distribution of the data
level
range
matching
stem-and-leaf display
43. An observational study in which subjects are followed to observe future outcomes
data table
prospective study
distribution
response variable
44. The ith ___ is the number that falls above i% of the data
retrospective study
variance
simulation component
percentile
45. Displays counts and - sometimes - percentages of individuals falling into named categories on two or more variables; categorizes the individuals on all variables at once - to reveal possible patterns in one variable that may be contingent on the cate
contingency table
variable
outliers
slope
46. The most basic situation in a simulation in which something happens at random
data
variable
simulation component
placebo
47. 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
level
simulation
percentile
bimodal
48. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
frequency table
normal probability plot
bimodal
form
49. All experimental units have an equal chance of receiving any treatment
linear model
completely randomized design
skewed
simpson's paradox
50. A distribution is this if it's not symmetric and one tail stretches out farther than the other
standardized value
skewed
representative
model