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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. The sequence of several components representing events that we are pretending will take place
parameter
single-blind
form
trial
2. When both those who could influence and evaluate the results are blinded
residuals
population
intercept
double-blind
3. A hump or local high point in the shape of the distribution of a variable; the apparent locations of these can change as the scale of a histogram is changed
data table
treatment
mode
cluster sample
4. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
random assignment
placebo effect
matching
area principle
5. The best defense against bias - in which each individual is given a fair - random chance of selection
randomization
convenience sample
observational study
direction
6. The ____ we care about most is straight
response variable
form
statistic
outlier
7. A numerical measure of the direction and strength of a linear association
correlation
context
random assignment
factor
8. In a statistical display - each data value should be represented by the same amount of area
prospective study
area principle
census
standard deviation
9. 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
histogram
level
dotplot
stratified random sample
10. Useful family of models for unimodal - symmetric distributions
range
residuals
strength
normal model
11. Numerically valued attribute of a model
placebo effect
randomization
parameter
control group
12. Multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant
nonresponse bias
rescaling
random
shape
13. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
unimodal
shape
least squares
data table
14. The natural tendency of randomly drawn samples to differ
area principle
sampling variability
sampling frame
undercoverage
15. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
skewed
systematic sample
outlier
normal probability plot
16. When an observed difference is too large for us to believe that is is likely to have occurred naturally
statistically significant
pie chart
sample
standard normal model
17. An equation of the form y-hat = b0 + b1x
treatment
distribution
linear model
mode
18. A list of individuals from whom the sample is drawn
regression to the mean
convenience sample
percentile
sampling frame
19. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
quantitative variable
normal percentile
principles of experimental design
center
20. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
standard normal model
statistically significant
stratified random sample
spread
21. The square root of the variance
confounded
normal model
regression line
standard deviation
22. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
range
shifting
rescaling
normal percentile
23. Summarized with the mean or the median
center
response
experiment
nonresponse bias
24. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
control group
standardizing
simple random sample
experiment
25. A scatterplot shows an association that is this if there is little scatter around the underlying relationship
68-95-99.7 rule
level
strength
lurking variable
26. Value calculated from data to summarize aspects of the data
statistic
response variable
linear model
blinding
27. Bias introduced to a sample when individuals can choose on their own whether to participate in the sample
center
mean
voluntary response bias
confounded
28. Found by summing all the data values and dividing by the count
interquartile range
shape
subset
mean
29. 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
ladder of powers
residuals
conditional distribution
subset
30. A sample that consists of the entire population
census
stem-and-leaf display
r2
response
31. The distribution of a variable restricting the who to consider only a smaller group of individuals
conditional distribution
population
normal probability plot
center
32. The parts of a distribution that typically trail off on either side; they can be characterized as long or short
spread
random assignment
shifting
tails
33. The specific values that the experimenter chooses for a factor
bias
single-blind
level
interquartile range
34. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units
68-95-99.7 rule
principles of experimental design
systematic sample
treatment
35. Holds information about the same characteristic for many cases
mean
confounded
variable
pie chart
36. When groups of experimental units are similar - it is a good idea to gather them together into these
outlier
block
mean
correlation
37. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
representative
bar chart
re-express data
independence
38. An equation or formula that simplifies and represents reality
variable
scatterplots
context
model
39. The middle value with half of the data above and half below it
standardized value
context
blinding
median
40. An arrangement of data in which each row represents a case and each column represents a variable
standard normal model
random
variable
data table
41. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
bias
blinding
systematic sample
random assignment
42. A distribution is this if it's not symmetric and one tail stretches out farther than the other
bar chart
response variable
skewed
standardizing
43. A distribution is this if the two halves on either side of the center look approximately like mirror images of each other
variable
symmetric
quartile
regression to the mean
44. The difference between the first and third quartiles
linear model
pie chart
sample size
interquartile range
45. 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
nonresponse bias
extrapolation
standard normal model
interquartile range
46. 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
quartile
shifting
form
quantitative variable
47. Distributions with more than two modes
multimodal
independence
retrospective study
center
48. Individuals on whom an experiment is performed
experimental units
least squares
systematic sample
center
49. Any attempt to force a sample to resemble specified attributes of the population
statistically significant
matching
pie chart
unimodal
50. 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
bimodal
experimental units
convenience sample
68-95-99.7 rule