SUBJECTS
|
BROWSE
|
CAREER CENTER
|
POPULAR
|
JOIN
|
LOGIN
Business Skills
|
Soft Skills
|
Basic Literacy
|
Certifications
About
|
Help
|
Privacy
|
Terms
|
Email
Search
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. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
statistically significant
re-express data
case
confounded
2. Values of this record the results of each trial with respect to what we were interested in
quantitative variable
response variable
outlier
statistically significant
3. The ith ___ is the number that falls above i% of the data
changing center and spread
sample survey
percentile
simpson's paradox
4. The natural tendency of randomly drawn samples to differ
sampling variability
placebo
bimodal
sampling frame
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
extrapolation
symmetric
area principle
variance
6. 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
normal percentile
median
simulation component
slope
7. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
blinding
r2
lurking variable
systematic sample
8. Doing this is equivalent to changing its units
changing center and spread
spread
single-blind
random assignment
9. Distributions with more than two modes
rescaling
randomized block
multimodal
matching
10. The most basic situation in a simulation in which something happens at random
double-blind
slope
normal probability plot
simulation component
11. Shows a bar representing the count of each category in a categorical variable
conditional distribution
histogram
categorical variable
bar chart
12. Shows how a 'whole' divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category
sample survey
experimental units
pie chart
statistically significant
13. Any data point that stands away from the others; can be extraordinary by having a large residual or by having high leverage
outlier
z-score
68-95-99.7 rule
random
14. A sample that consists of the entire population
census
comparing distributions
marginal distribution
retrospective study
15. Individuals on whom an experiment is performed
standardized value
bias
principles of experimental design
experimental units
16. 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
factor
contingency table
standardized value
normal percentile
17. The difference between the first and third quartiles
matching
representative
interquartile range
normal model
18. Anything in a survey design that influences response
response bias
rescaling
comparing distributions
spread
19. 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
changing center and spread
normal probability plot
center
undercoverage
20. Found by substituting the x-value in the regression equation; they're the values on the fitted line
experiment
lurking variable
factor
predicted value
21. A numerically valued attribute of a model for a population
tails
population parameter
5-number summary
context
22. An event is this if we know what outcomes could happen - but not which particular values will happen
influential point
blinding
random
extrapolation
23. Distributions with two modes
independence
bimodal
sampling frame
unimodal
24. Value calculated from data to summarize aspects of the data
sample size
quantitative variable
statistic
outcome
25. A normal model with a mean of 0 and a standard deviation of 1
data
population
rescaling
standard normal model
26. Value found by subtracting the mean and dividing by the standard deviation
z-score
census
least squares
standardized value
27. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness
shape
r2
shifting
timeplot
28. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
experiment
level
prospective study
lurking variable
29. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
random
unimodal
frequency table
spread
30. An observational study in which subjects are followed to observe future outcomes
prospective study
scatterplots
re-express data
spread
31. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
sampling frame
direction
normal percentile
standardizing
32. An equation or formula that simplifies and represents reality
model
outcome
r2
uniform
33. Systematically recorded information - whether numbers or labels - together with its context
data
median
distribution
bar chart
34. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
contingency table
mean
standardizing
intercept
35. Useful family of models for unimodal - symmetric distributions
center
normal model
least squares
simple random sample
36. A variable that names categories (whether with words or numerals)
completely randomized design
categorical variable
level
double-blind
37. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
observational study
center
predicted value
skewed
38. 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
bimodal
normal model
experiment
confounded
39. A point that does not fit the overall pattern seen in the scatterplot
cluster sample
simulation component
representative
outlier
40. The entire group of individuals or instances about whom we hope to learn
parameter
stratified random sample
population
strength
41. 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
matching
quartile
matched
form
42. Summarized with the standard deviation - interquartile range - and range
spread
symmetric
intercept
sampling variability
43. Variables are said to be this if the conditional distribution of one variable is the same for each category of the other
range
conditional distribution
independence
subset
44. 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
form
68-95-99.7 rule
sample size
categorical variable
45. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
sampling variability
correlation
random
intercept
46. 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
observational study
intercept
standardized value
leverage
47. The distribution of a variable restricting the who to consider only a smaller group of individuals
bar chart
conditional distribution
placebo
boxplot
48. A positive ____ or association means that - in general - as one variable increases - so does the other; when increases in one variable generally correspond to decreases in the other - the association is negative
lurking variable
direction
symmetric
treatment
49. Found by summing all the data values and dividing by the count
context
control group
mean
completely randomized design
50. When groups of experimental units are similar - it is a good idea to gather them together into these
normal probability plot
ladder of powers
scatterplots
block