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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. Control - randomize - replicate - block
spread
treatment
random assignment
principles of experimental design
2. Design Randomization occurring within blocks
experiment
simulation
center
randomized block
3. When either those who could influence or evaluate the results is blinded
single-blind
lurking variable
simple random sample
marginal distribution
4. A numerical measure of the direction and strength of a linear association
undercoverage
randomized block
timeplot
correlation
5. Bias introduced to a sample when individuals can choose on their own whether to participate in the sample
voluntary response bias
r2
median
census
6. When an observed difference is too large for us to believe that is is likely to have occurred naturally
statistically significant
case
distribution
census
7. The ____ we care about most is straight
distribution
form
bimodal
standardized value
8. The sequence of several components representing events that we are pretending will take place
distribution
correlation
trial
prospective study
9. Useful family of models for unimodal - symmetric distributions
confounded
extrapolation
standard normal model
normal model
10. The sum of squared deviations from the mean - divided by the count minus one
parameter
shape
sample size
variance
11. Summarized with the mean or the median
statistic
standard normal model
center
marginal distribution
12. When omitting a point from the data results in a very different regression model - the point is an ____
stem-and-leaf display
standard deviation
percentile
influential point
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
spread
extrapolation
skewed
leverage
14. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
cluster sample
representative
matched
random assignment
15. Tells how many standard deviations a value is from the mean; have a mean of zero and a standard deviation of one
population parameter
z-score
randomized block
bimodal
16. A list of individuals from whom the sample is drawn
data table
sampling frame
68-95-99.7 rule
spread
17. A distribution is this if it's not symmetric and one tail stretches out farther than the other
skewed
sample size
stem-and-leaf display
context
18. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
matched
spread
residuals
changing center and spread
19. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
rescaling
intercept
regression to the mean
linear model
20. The ith ___ is the number that falls above i% of the data
percentile
regression to the mean
experimental units
response variable
21. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
double-blind
sampling frame
center
regression to the mean
22. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
census
placebo effect
prospective study
lurking variable
23. A numerical summary of how tightly the values are clustered around the 'center'
spread
sampling variability
nonresponse bias
unimodal
24. This criterion specifies the unique line that minimizes the variance of the residuals or - equivalently - the sum of the squared residuals
simple random sample
principles of experimental design
matched
least squares
25. The difference between the lowest and highest values in a data set
range
model
placebo
timeplot
26. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
shifting
response bias
randomization
tails
27. Found by summing all the data values and dividing by the count
variance
population parameter
mean
histogram
28. Shows quantitative data values in a way that sketches the distribution of the data
stem-and-leaf display
5-number summary
data
symmetric
29. A variable whose values are compared across different treatments
blinding
random assignment
experiment
response
30. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
confounded
changing center and spread
census
range
31. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
cluster sample
simpson's paradox
quantitative variable
normal percentile
32. Extreme values that don't appear to belong with the rest of the data
mode
trial
voluntary response bias
outliers
33. In a statistical display - each data value should be represented by the same amount of area
multistage sample
area principle
sample survey
leverage
34. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
bar chart
dotplot
sampling frame
independence
35. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
lurking variable
random numbers
case
statistically significant
36. Gives the possible values of the variable and the frequency or relative frequency of each value
sample
convenience sample
distribution
residuals
37. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
simulation component
spread
sampling variability
control group
38. An individual result of a component of a simulation
leverage
outcome
variance
influential point
39. The best defense against bias - in which each individual is given a fair - random chance of selection
standard deviation
randomization
influential point
68-95-99.7 rule
40. An equation or formula that simplifies and represents reality
model
sampling frame
regression to the mean
strength
41. The specific values that the experimenter chooses for a factor
ladder of powers
level
standard deviation
quantitative variable
42. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
shape
median
simulation
simpson's paradox
43. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
simulation component
units
placebo effect
statistic
44. When groups of experimental units are similar - it is a good idea to gather them together into these
extrapolation
experimental units
block
normal model
45. A point that does not fit the overall pattern seen in the scatterplot
symmetric
undercoverage
outlier
spread
46. A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population
direction
sample survey
factor
bimodal
47. 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
histogram
quantitative variable
response variable
48. When doing this - consider their shape - center - and spread
model
comparing distributions
independence
statistic
49. Anything in a survey design that influences response
convenience sample
tails
response bias
sample
50. Sampling schemes that combine several sampling methods
regression to the mean
form
multistage sample
statistic