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Test your basic knowledge |
AP Statistics Vocab
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Subjects
:
statistics
,
ap
Instructions:
Answer 50 questions in 15 minutes.
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.
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 specific values that the experimenter chooses for a factor
interquartile range
level
random numbers
blinding
2. Places in order the effects that many re-expressions have on the data
units
r2
ladder of powers
statistic
3. When averages are taken across different groups - they can appear to contradict the overall averages
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4. Control - randomize - replicate - block
r2
data table
principles of experimental design
simpson's paradox
5. A variable whose values are compared across different treatments
population parameter
block
response
bar chart
6. 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
least squares
simple random sample
lurking variable
7. 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
sample survey
context
frequency table
standard normal model
8. 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
outlier
r2
standard deviation
9. Gives the possible values of the variable and the frequency or relative frequency of each value
convenience sample
median
distribution
response variable
10. An equation or formula that simplifies and represents reality
multimodal
normal probability plot
population parameter
model
11. A variable in which the numbers act as numerical values; always has units
quantitative variable
experimental units
center
nonresponse bias
12. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
multimodal
tails
simpson's paradox
variable
13. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
stratified random sample
multistage sample
lurking variable
percentile
14. Gives a value in 'y-units per x-unit'; changes of one unit in x are associated with changes of b1 units in predicted values of y
stratified random sample
slope
normal probability plot
contingency table
15. The number of individuals in a sample
matched
statistically significant
bias
sample size
16. 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
response variable
lurking variable
uniform
undercoverage
17. A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population
sample survey
completely randomized design
rescaling
variance
18. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
blinding
ladder of powers
control group
sample size
19. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
re-express data
lurking variable
placebo effect
statistic
20. Any systematic failure of a sampling method to represent its population; common errors are voluntary response - undercoverage - nonresponse ____ - and response ____
matching
bias
z-score
statistic
21. When doing this - consider their shape - center - and spread
simpson's paradox
residuals
comparing distributions
sample survey
22. Anything in a survey design that influences response
context
categorical variable
standardized value
response bias
23. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
changing center and spread
leverage
lurking variable
shape
24. Individuals on whom an experiment is performed
experiment
re-express data
intercept
experimental units
25. 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
stratified random sample
center
tails
26. An individual result of a component of a simulation
linear model
response
outcome
center
27. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
linear model
blinding
factor
normal model
28. Holds information about the same characteristic for many cases
model
variable
simple random sample
rescaling
29. Found by summing all the data values and dividing by the count
tails
context
influential point
mean
30. The sequence of several components representing events that we are pretending will take place
trial
principles of experimental design
outcome
placebo effect
31. A treatment known to have no effect - administered so that all groups experience the same conditions
normal model
spread
placebo
blinding
32. A numerical measure of the direction and strength of a linear association
distribution
simple random sample
correlation
statistic
33. 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
r2
strength
contingency table
lurking variable
34. To be valid - an experiment must assign experimental units to treatment groups at random
re-express data
contingency table
random assignment
randomized block
35. Useful family of models for unimodal - symmetric distributions
convenience sample
normal model
randomization
normal probability plot
36. 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
matched
contingency table
response variable
stratified random sample
37. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
prospective study
factor
histogram
placebo effect
38. The square root of the variance
categorical variable
standard deviation
factor
trial
39. Design Randomization occurring within blocks
randomized block
intercept
quantitative variable
5-number summary
40. Multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant
regression to the mean
outlier
units
rescaling
41. A normal model with a mean of 0 and a standard deviation of 1
standard normal model
quartile
outlier
data table
42. Consists of the minimum and maximum - the quartiles Q1 and Q3 - and the median
uniform
outcome
experiment
5-number summary
43. 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
uniform
intercept
mean
histogram
44. Distributions with two modes
simulation
comparing distributions
bimodal
z-score
45. A distribution is this if it's not symmetric and one tail stretches out farther than the other
shape
factor
systematic sample
skewed
46. A distribution is this if the two halves on either side of the center look approximately like mirror images of each other
shape
sample survey
scatterplots
symmetric
47. Consists of the individuals who are conveniently available
population parameter
interquartile range
simple random sample
convenience sample
48. This criterion specifies the unique line that minimizes the variance of the residuals or - equivalently - the sum of the squared residuals
skewed
independence
population parameter
least squares
49. The ith ___ is the number that falls above i% of the data
percentile
simulation component
observational study
context
50. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units
ladder of powers
simple random sample
treatment
spread
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