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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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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. Shows how a 'whole' divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category
pie chart
observational study
double-blind
control group
2. The sequence of several components representing events that we are pretending will take place
spread
ladder of powers
trial
voluntary response bias
3. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
stratified random sample
frequency table
shifting
distribution
4. Numerically valued attribute of a model
blinding
least squares
percentile
parameter
5. Holds information about the same characteristic for many cases
confounded
units
standardized value
variable
6. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
unimodal
uniform
independence
regression to the mean
7. An equation of the form y-hat = b0 + b1x
predicted value
ladder of powers
linear model
undercoverage
8. Doing this is equivalent to changing its units
units
changing center and spread
population parameter
prospective study
9. Gives the possible values of the variable and the frequency or relative frequency of each value
distribution
marginal distribution
sampling variability
simpson's paradox
10. An observational study in which subjects are followed to observe future outcomes
matched
level
prospective study
contingency table
11. Consists of the individuals who are conveniently available
convenience sample
population
linear model
standardizing
12. An event is this if we know what outcomes could happen - but not which particular values will happen
leverage
statistic
dotplot
random
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
leverage
symmetric
simpson's paradox
histogram
14. 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
model
multimodal
completely randomized design
15. A distribution is this if it's not symmetric and one tail stretches out farther than the other
skewed
symmetric
random
control group
16. 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
normal model
68-95-99.7 rule
predicted value
5-number summary
17. A variable whose levels are controlled by the experimenter
factor
bar chart
center
symmetric
18. 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
mean
units
spread
slope
19. Found by summing all the data values and dividing by the count
frequency table
mean
leverage
sampling frame
20. A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population
normal model
single-blind
systematic sample
sample survey
21. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
range
representative
standardized value
independence
22. An individual result of a component of a simulation
center
data table
outcome
bias
23. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
median
intercept
leverage
bias
24. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
ladder of powers
placebo effect
population
multistage sample
25. These are hard to generate - but several websites offer an unlimited supply of equally likely random values
extrapolation
random numbers
median
stratified random sample
26. Bias introduced to a sample when individuals can choose on their own whether to participate in the sample
simpson's paradox
population parameter
variance
voluntary response bias
27. Shows quantitative data values in a way that sketches the distribution of the data
center
block
influential point
stem-and-leaf display
28. 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
principles of experimental design
dotplot
68-95-99.7 rule
contingency table
29. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
influential point
standard deviation
histogram
representative
30. A variable that names categories (whether with words or numerals)
experiment
categorical variable
unimodal
timeplot
31. A sample that consists of the entire population
center
level
standardizing
census
32. A sampling design in which entire groups are chosen at random
cluster sample
tails
center
standard normal model
33. The difference between the lowest and highest values in a data set
simpson's paradox
range
uniform
regression to the mean
34. A scatterplot shows an association that is this if there is little scatter around the underlying relationship
strength
random numbers
confounded
pie chart
35. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
rescaling
block
treatment
outlier
36. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
data
re-express data
stem-and-leaf display
symmetric
37. Extreme values that don't appear to belong with the rest of the data
contingency table
least squares
outliers
systematic sample
38. This criterion specifies the unique line that minimizes the variance of the residuals or - equivalently - the sum of the squared residuals
quantitative variable
least squares
contingency table
ladder of powers
39. Bias introduced to a sample when a large fraction of those sampled fails to respond
simpson's paradox
nonresponse bias
correlation
center
40. To be valid - an experiment must assign experimental units to treatment groups at random
form
distribution
representative
random assignment
41. Shows a bar representing the count of each category in a categorical variable
bar chart
outcome
5-number summary
lurking variable
42. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
randomization
mean
lurking variable
census
43. Value found by subtracting the mean and dividing by the standard deviation
5-number summary
standardized value
case
quartile
44. 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
tails
normal probability plot
contingency table
variance
45. Shows the relationship between two quantitative variables measured on the same cases
double-blind
case
matching
scatterplots
46. Multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant
simulation
experiment
rescaling
retrospective study
47. Distributions with two modes
area principle
bimodal
lurking variable
standard normal model
48. A numerically valued attribute of a model for a population
quartile
scatterplots
population parameter
percentile
49. A normal model with a mean of 0 and a standard deviation of 1
mean
standard normal model
histogram
form
50. All experimental units have an equal chance of receiving any treatment
changing center and spread
statistic
completely randomized design
lurking variable
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