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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. Holds information about the same characteristic for many cases
variable
standard normal model
quantitative variable
population parameter
2. Gives the possible values of the variable and the frequency or relative frequency of each value
regression line
distribution
68-95-99.7 rule
confounded
3. Summarized with the mean or the median
variable
center
direction
regression line
4. A normal model with a mean of 0 and a standard deviation of 1
completely randomized design
intercept
r2
standard normal model
5. Gives the possible values of the variable and the relative frequency of each value
symmetric
random assignment
distribution
marginal distribution
6. The natural tendency of randomly drawn samples to differ
scatterplots
model
sampling variability
simple random sample
7. The best defense against bias - in which each individual is given a fair - random chance of selection
treatment
normal percentile
randomization
shifting
8. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
bias
intercept
voluntary response bias
symmetric
9. Places in order the effects that many re-expressions have on the data
observational study
randomized block
range
ladder of powers
10. The entire group of individuals or instances about whom we hope to learn
population
percentile
quartile
shape
11. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
matched
observational study
correlation
center
12. The sequence of several components representing events that we are pretending will take place
trial
simple random sample
experiment
tails
13. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units
multistage sample
stem-and-leaf display
treatment
response variable
14. Numerically valued attribute of a model
percentile
prospective study
treatment
parameter
15. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
standard normal model
regression line
level
cluster sample
16. Found by substituting the x-value in the regression equation; they're the values on the fitted line
predicted value
statistically significant
sample survey
boxplot
17. The square root of the variance
ladder of powers
prospective study
treatment
standard deviation
18. A variable in which the numbers act as numerical values; always has units
quantitative variable
re-express data
bar chart
outliers
19. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
lurking variable
conditional distribution
blinding
outcome
20. A quantity or amount adopted as a standard of measurement - such as dollars - hours - or grams
normal probability plot
bar chart
least squares
units
21. When either those who could influence or evaluate the results is blinded
distribution
bar chart
predicted value
single-blind
22. Value found by subtracting the mean and dividing by the standard deviation
double-blind
standardized value
case
unimodal
23. A numerical measure of the direction and strength of a linear association
normal probability plot
spread
correlation
response
24. To be valid - an experiment must assign experimental units to treatment groups at random
center
random assignment
regression line
interquartile range
25. 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
statistically significant
tails
outlier
extrapolation
26. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
regression to the mean
statistically significant
blinding
skewed
27. When averages are taken across different groups - they can appear to contradict the overall averages
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28. Displays data that change over time
timeplot
subset
outlier
experiment
29. The specific values that the experimenter chooses for a factor
normal percentile
normal model
outlier
level
30. An event is this if we know what outcomes could happen - but not which particular values will happen
range
parameter
random
standardizing
31. Sampling schemes that combine several sampling methods
multistage sample
undercoverage
multimodal
distribution
32. All experimental units have an equal chance of receiving any treatment
variable
response bias
completely randomized design
median
33. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
percentile
residuals
outliers
center
34. Displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values
tails
standardizing
boxplot
68-95-99.7 rule
35. Bias introduced to a sample when a large fraction of those sampled fails to respond
conditional distribution
control group
context
nonresponse bias
36. In a retrospective or prospective study Subjects who are similar in ways not under study may be ____ and then compared with each other on the variables of interest
contingency table
5-number summary
matched
placebo effect
37. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
normal probability plot
distribution
linear model
outlier
38. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
simple random sample
multistage sample
r2
block
39. Shows a bar representing the count of each category in a categorical variable
multimodal
blinding
r2
bar chart
40. In a statistical display - each data value should be represented by the same amount of area
5-number summary
ladder of powers
area principle
contingency table
41. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
contingency table
observational study
independence
response
42. The number of individuals in a sample
quartile
normal probability plot
frequency table
sample size
43. Ideally tells who was measured - what was measured - how the data were collected - where the data were collected - and when and why the study was performed
response bias
units
simpson's paradox
context
44. Any attempt to force a sample to resemble specified attributes of the population
correlation
systematic sample
matching
standardized value
45. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
predicted value
standard normal model
normal probability plot
frequency table
46. 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
percentile
simpson's paradox
contingency table
47. A distribution is this if it's not symmetric and one tail stretches out farther than the other
undercoverage
skewed
simpson's paradox
block
48. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
stratified random sample
variance
population
randomization
49. Any systematic failure of a sampling method to represent its population; common errors are voluntary response - undercoverage - nonresponse ____ - and response ____
normal probability plot
bias
statistically significant
quartile
50. When omitting a point from the data results in a very different regression model - the point is an ____
variable
percentile
linear model
influential point