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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. 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
nonresponse bias
population
prospective study
normal probability plot
2. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
simpson's paradox
blinding
least squares
case
3. Places in order the effects that many re-expressions have on the data
ladder of powers
context
single-blind
level
4. Shows a bar representing the count of each category in a categorical variable
bar chart
stratified random sample
conditional distribution
model
5. Any systematic failure of a sampling method to represent its population; common errors are voluntary response - undercoverage - nonresponse ____ - and response ____
bias
normal model
matching
z-score
6. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
z-score
population parameter
normal percentile
response variable
7. Summarized with the standard deviation - interquartile range - and range
r2
residuals
lurking variable
spread
8. The ith ___ is the number that falls above i% of the data
distribution
percentile
undercoverage
population
9. The difference between the first and third quartiles
interquartile range
nonresponse bias
slope
categorical variable
10. 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
r2
extrapolation
statistically significant
68-95-99.7 rule
11. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
comparing distributions
quartile
standardizing
outlier
12. Gives the possible values of the variable and the relative frequency of each value
distribution
68-95-99.7 rule
re-express data
center
13. Consists of the minimum and maximum - the quartiles Q1 and Q3 - and the median
5-number summary
control group
re-express data
census
14. A normal model with a mean of 0 and a standard deviation of 1
rescaling
placebo effect
standard normal model
case
15. When doing this - consider their shape - center - and spread
categorical variable
boxplot
comparing distributions
correlation
16. The number of individuals in a sample
statistically significant
sample size
changing center and spread
matching
17. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
quantitative variable
outlier
outcome
intercept
18. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
tails
regression to the mean
population
random numbers
19. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
frequency table
center
comparing distributions
standard normal model
20. To be valid - an experiment must assign experimental units to treatment groups at random
retrospective study
skewed
random assignment
simple random sample
21. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
independence
bias
trial
normal percentile
22. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
regression line
frequency table
dotplot
unimodal
23. A point that does not fit the overall pattern seen in the scatterplot
outlier
context
distribution
prospective study
24. A numerical summary of how tightly the values are clustered around the 'center'
spread
lurking variable
regression line
standardizing
25. When omitting a point from the data results in a very different regression model - the point is an ____
data table
level
influential point
median
26. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
residuals
lurking variable
multistage sample
response variable
27. A study based on data in which no manipulation of factors has been employed
outlier
placebo
matched
observational study
28. A representative subset of a population - examined in hope of learning about the population
block
influential point
random numbers
sample
29. Displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values
regression line
prospective study
boxplot
sampling variability
30. The difference between the lowest and highest values in a data set
leverage
range
lurking variable
extrapolation
31. Shows quantitative data values in a way that sketches the distribution of the data
contingency table
multistage sample
distribution
stem-and-leaf display
32. A variable that names categories (whether with words or numerals)
shifting
categorical variable
quartile
case
33. A sample is this if the statistics computed from it accurately reflect the corresponding population parameters
residuals
representative
dotplot
shape
34. Shows how a 'whole' divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category
normal percentile
pie chart
distribution
confounded
35. Holds information about the same characteristic for many cases
matching
case
undercoverage
variable
36. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
area principle
case
standardizing
lurking variable
37. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
symmetric
trial
stratified random sample
predicted value
38. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
changing center and spread
outlier
residuals
regression to the mean
39. Any attempt to force a sample to resemble specified attributes of the population
matching
range
conditional distribution
re-express data
40. Sampling schemes that combine several sampling methods
multistage sample
randomized block
simple random sample
predicted value
41. Graphs a dot for each case against a single axis
random
dotplot
boxplot
convenience sample
42. An equation of the form y-hat = b0 + b1x
quantitative variable
residuals
linear model
center
43. This criterion specifies the unique line that minimizes the variance of the residuals or - equivalently - the sum of the squared residuals
least squares
data table
placebo
simulation component
44. Individuals on whom an experiment is performed
statistically significant
form
timeplot
experimental units
45. Values of this record the results of each trial with respect to what we were interested in
treatment
response variable
predicted value
shifting
46. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
treatment
double-blind
unimodal
placebo effect
47. A variable in which the numbers act as numerical values; always has units
mode
distribution
units
quantitative variable
48. A sample drawn by selecting individuals systematically from a sampling frame
systematic sample
re-express data
regression to the mean
shifting
49. Useful family of models for unimodal - symmetric distributions
normal percentile
simpson's paradox
normal model
parameter
50. Value found by subtracting the mean and dividing by the standard deviation
stratified random sample
double-blind
standardized value
principles of experimental design