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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 both those who could influence and evaluate the results are blinded
predicted value
distribution
double-blind
leverage
2. A treatment known to have no effect - administered so that all groups experience the same conditions
symmetric
distribution
placebo
model
3. When either those who could influence or evaluate the results is blinded
linear model
data
block
single-blind
4. When an observed difference is too large for us to believe that is is likely to have occurred naturally
control group
timeplot
statistically significant
5-number summary
5. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
placebo effect
interquartile range
range
units
6. A distribution is this if it's not symmetric and one tail stretches out farther than the other
influential point
skewed
prospective study
standardized value
7. The most basic situation in a simulation in which something happens at random
sample
sample size
interquartile range
simulation component
8. All experimental units have an equal chance of receiving any treatment
standardizing
completely randomized design
predicted value
variance
9. Value found by subtracting the mean and dividing by the standard deviation
standardized value
68-95-99.7 rule
leverage
boxplot
10. 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
form
variance
observational study
r2
11. Bias introduced to a sample when a large fraction of those sampled fails to respond
outlier
blinding
nonresponse bias
response bias
12. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
intercept
standardized value
spread
randomized block
13. Any systematic failure of a sampling method to represent its population; common errors are voluntary response - undercoverage - nonresponse ____ - and response ____
symmetric
least squares
bias
prospective study
14. A scatterplot shows an association that is this if there is little scatter around the underlying relationship
strength
placebo effect
control group
confounded
15. The ith ___ is the number that falls above i% of the data
dotplot
percentile
regression line
simple random sample
16. A sampling design in which entire groups are chosen at random
r2
z-score
data table
cluster sample
17. The number of individuals in a sample
sample survey
bias
sample size
independence
18. A distribution that's roughly flat
least squares
matching
uniform
independence
19. The distribution of a variable restricting the who to consider only a smaller group of individuals
representative
conditional distribution
center
timeplot
20. 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
lurking variable
simulation
slope
standardizing
21. A numerically valued attribute of a model for a population
population parameter
outcome
cluster sample
tails
22. Shows quantitative data values in a way that sketches the distribution of the data
stem-and-leaf display
control group
range
factor
23. Doing this is equivalent to changing its units
changing center and spread
sample size
least squares
slope
24. An event is this if we know what outcomes could happen - but not which particular values will happen
experiment
randomization
outlier
random
25. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
matched
interquartile range
placebo
lurking variable
26. When groups of experimental units are similar - it is a good idea to gather them together into these
rescaling
random assignment
block
outcome
27. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
regression to the mean
sample survey
leverage
range
28. 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
sample
nonresponse bias
leverage
randomization
29. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
sample survey
median
influential point
control group
30. A variable in which the numbers act as numerical values; always has units
placebo effect
quantitative variable
confounded
regression to the mean
31. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
cluster sample
population parameter
intercept
re-express data
32. Gives the possible values of the variable and the relative frequency of each value
model
r2
distribution
influential point
33. Holds information about the same characteristic for many cases
ladder of powers
strength
variable
simple random sample
34. 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
standardizing
multimodal
population
experiment
35. Gives the possible values of the variable and the frequency or relative frequency of each value
categorical variable
distribution
double-blind
units
36. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units
categorical variable
independence
lurking variable
treatment
37. Any attempt to force a sample to resemble specified attributes of the population
matching
linear model
tails
parameter
38. An equation of the form y-hat = b0 + b1x
predicted value
linear model
observational study
multimodal
39. The sum of squared deviations from the mean - divided by the count minus one
variance
range
area principle
trial
40. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
independence
factor
simulation component
linear model
41. Shows a bar representing the count of each category in a categorical variable
direction
mode
bar chart
double-blind
42. The square root of the variance
stratified random sample
matching
direction
standard deviation
43. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
confounded
statistically significant
histogram
center
44. Displays data that change over time
standard normal model
timeplot
cluster sample
predicted value
45. A hump or local high point in the shape of the distribution of a variable; the apparent locations of these can change as the scale of a histogram is changed
percentile
mode
lurking variable
subset
46. The best defense against bias - in which each individual is given a fair - random chance of selection
matched
randomization
model
normal model
47. 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
normal probability plot
slope
trial
response variable
48. Control - randomize - replicate - block
strength
experiment
pie chart
principles of experimental design
49. The ____ we care about most is straight
form
simple random sample
68-95-99.7 rule
census
50. 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
subset
standardized value
spread
influential point