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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. Any attempt to force a sample to resemble specified attributes of the population
bimodal
matching
randomized block
area principle
2. 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
marginal distribution
random assignment
shifting
form
3. 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
percentile
slope
center
nonresponse bias
4. A sample drawn by selecting individuals systematically from a sampling frame
outcome
matched
systematic sample
center
5. 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
trial
systematic sample
retrospective study
extrapolation
6. Control - randomize - replicate - block
placebo effect
area principle
principles of experimental design
tails
7. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
statistically significant
shifting
parameter
shape
8. Graphs a dot for each case against a single axis
extrapolation
dotplot
median
predicted value
9. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
extrapolation
center
data table
population parameter
10. A distribution is this if it's not symmetric and one tail stretches out farther than the other
trial
lurking variable
skewed
shifting
11. When omitting a point from the data results in a very different regression model - the point is an ____
standardizing
sampling variability
contingency table
influential point
12. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
nonresponse bias
normal probability plot
confounded
random numbers
13. Holds information about the same characteristic for many cases
multistage sample
bias
variable
treatment
14. A variable whose levels are controlled by the experimenter
area principle
normal probability plot
factor
interquartile range
15. To be valid - an experiment must assign experimental units to treatment groups at random
5-number summary
systematic sample
census
random assignment
16. Adding a constant to each data value adds the same constant to the mean - the median - and the quartiles - but does not change the standard deviation or IQR
shifting
spread
residuals
bar chart
17. 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
categorical variable
regression to the mean
simulation
random numbers
18. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
principles of experimental design
distribution
context
independence
19. 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
double-blind
voluntary response bias
marginal distribution
r2
20. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
residuals
bar chart
data table
center
21. A sample that consists of the entire population
sample survey
blinding
random numbers
census
22. Doing this is equivalent to changing its units
changing center and spread
slope
range
multimodal
23. A variable that names categories (whether with words or numerals)
matching
categorical variable
statistically significant
systematic sample
24. 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
experiment
histogram
outlier
spread
25. A variable whose values are compared across different treatments
representative
strength
treatment
response
26. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
treatment
rescaling
outliers
tails
27. 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
undercoverage
correlation
regression to the mean
model
28. 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
outlier
categorical variable
stratified random sample
quartile
29. Consists of the minimum and maximum - the quartiles Q1 and Q3 - and the median
treatment
5-number summary
comparing distributions
unimodal
30. Bias introduced to a sample when a large fraction of those sampled fails to respond
nonresponse bias
mode
slope
confounded
31. A numerical summary of how tightly the values are clustered around the 'center'
shape
spread
parameter
bimodal
32. A scatterplot shows an association that is this if there is little scatter around the underlying relationship
spread
strength
shifting
comparing distributions
33. Distributions with more than two modes
multimodal
control group
confounded
simulation component
34. The middle value with half of the data above and half below it
factor
median
random numbers
cluster sample
35. The entire group of individuals or instances about whom we hope to learn
standardizing
normal probability plot
population
r2
36. An event is this if we know what outcomes could happen - but not which particular values will happen
standard normal model
shape
random
boxplot
37. 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
principles of experimental design
68-95-99.7 rule
lurking variable
trial
38. The distribution of a variable restricting the who to consider only a smaller group of individuals
uniform
conditional distribution
experimental units
blinding
39. The natural tendency of randomly drawn samples to differ
normal percentile
rescaling
sampling variability
standardizing
40. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
least squares
form
re-express data
representative
41. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
mode
placebo effect
representative
comparing distributions
42. A distribution is this if the two halves on either side of the center look approximately like mirror images of each other
standard normal model
independence
symmetric
prospective study
43. A numerical measure of the direction and strength of a linear association
correlation
uniform
quantitative variable
outcome
44. A quantity or amount adopted as a standard of measurement - such as dollars - hours - or grams
population parameter
units
5-number summary
data table
45. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
parameter
intercept
spread
lurking variable
46. When either those who could influence or evaluate the results is blinded
uniform
single-blind
response bias
shape
47. 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
correlation
histogram
spread
subset
48. A numerically valued attribute of a model for a population
z-score
outliers
population parameter
response variable
49. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
intercept
lurking variable
scatterplots
regression line
50. These are hard to generate - but several websites offer an unlimited supply of equally likely random values
random numbers
simulation
standardized value
block