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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. Places in order the effects that many re-expressions have on the data
5-number summary
ladder of powers
sampling variability
conditional distribution
2. When groups of experimental units are similar - it is a good idea to gather them together into these
predicted value
prospective study
voluntary response bias
block
3. A study based on data in which no manipulation of factors has been employed
nonresponse bias
matching
center
observational study
4. In a normal model - about 68% of values fall within 1 standard deviation of the mean - about 95% fall within 2 standard deviations of the mean - and about 99.7% fall within 3 standard deviations of the mean
parameter
quantitative variable
randomization
68-95-99.7 rule
5. A quantity or amount adopted as a standard of measurement - such as dollars - hours - or grams
influential point
units
conditional distribution
histogram
6. The specific values that the experimenter chooses for a factor
leverage
level
randomized block
extrapolation
7. A numerical summary of how tightly the values are clustered around the 'center'
standardizing
experiment
spread
representative
8. A scatterplot shows an association that is this if there is little scatter around the underlying relationship
skewed
variable
variance
strength
9. Shows how a 'whole' divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category
tails
pie chart
matched
response
10. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
confounded
census
matching
randomized block
11. Any systematic failure of a sampling method to represent its population; common errors are voluntary response - undercoverage - nonresponse ____ - and response ____
bias
lurking variable
r2
distribution
12. Distributions with two modes
bimodal
marginal distribution
response variable
interquartile range
13. 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
outlier
block
completely randomized design
14. Values of this record the results of each trial with respect to what we were interested in
response variable
matched
response bias
normal probability plot
15. Doing this is equivalent to changing its units
direction
census
shifting
changing center and spread
16. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
spread
experiment
double-blind
re-express data
17. Individuals on whom an experiment is performed
response bias
linear model
contingency table
experimental units
18. Holds information about the same characteristic for many cases
independence
variable
range
distribution
19. The best defense against bias - in which each individual is given a fair - random chance of selection
block
randomization
bimodal
variance
20. Multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant
simple random sample
normal percentile
rescaling
response variable
21. When either those who could influence or evaluate the results is blinded
single-blind
trial
blinding
regression to the mean
22. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
r2
randomization
independence
statistic
23. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
matching
double-blind
normal percentile
center
24. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
stratified random sample
bimodal
bar chart
context
25. Summarized with the standard deviation - interquartile range - and range
spread
regression to the mean
frequency table
simpson's paradox
26. When omitting a point from the data results in a very different regression model - the point is an ____
influential point
standard deviation
sample survey
observational study
27. A variable whose values are compared across different treatments
interquartile range
outliers
uniform
response
28. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
distribution
intercept
tails
systematic sample
29. The ____ we care about most is straight
bimodal
double-blind
prospective study
form
30. 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
marginal distribution
case
placebo effect
histogram
31. Consists of the minimum and maximum - the quartiles Q1 and Q3 - and the median
5-number summary
bar chart
predicted value
residuals
32. Anything in a survey design that influences response
outliers
re-express data
response bias
normal probability plot
33. 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
shape
experiment
normal probability plot
population parameter
34. When doing this - consider their shape - center - and spread
trial
skewed
comparing distributions
r2
35. The entire group of individuals or instances about whom we hope to learn
percentile
nonresponse bias
population
extrapolation
36. An individual about whom or which we have data
spread
blinding
sample
case
37. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
sampling variability
spread
parameter
control group
38. 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
sampling frame
categorical variable
prospective study
quartile
39. All experimental units have an equal chance of receiving any treatment
experiment
r2
completely randomized design
units
40. Found by substituting the x-value in the regression equation; they're the values on the fitted line
residuals
correlation
experiment
predicted value
41. A normal model with a mean of 0 and a standard deviation of 1
factor
bias
standard normal model
placebo
42. Shows quantitative data values in a way that sketches the distribution of the data
outcome
distribution
conditional distribution
stem-and-leaf display
43. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
simpson's paradox
standardizing
residuals
nonresponse bias
44. The difference between the first and third quartiles
treatment
sampling frame
interquartile range
matched
45. An individual result of a component of a simulation
outcome
bimodal
principles of experimental design
standardizing
46. 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
area principle
marginal distribution
random
simulation component
47. Displays data that change over time
residuals
timeplot
response variable
center
48. The difference between the lowest and highest values in a data set
range
categorical variable
subset
context
49. Found by summing all the data values and dividing by the count
mean
matching
parameter
mode
50. 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
median
model
r2
outliers