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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. Shows the relationship between two quantitative variables measured on the same cases
scatterplots
residuals
lurking variable
quantitative variable
2. An equation of the form y-hat = b0 + b1x
nonresponse bias
linear model
stratified random sample
standard deviation
3. A variable whose values are compared across different treatments
sample size
data
response
percentile
4. 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
data
bar chart
leverage
range
5. An equation or formula that simplifies and represents reality
shifting
model
stratified random sample
bimodal
6. The entire group of individuals or instances about whom we hope to learn
data
population
68-95-99.7 rule
correlation
7. This criterion specifies the unique line that minimizes the variance of the residuals or - equivalently - the sum of the squared residuals
units
percentile
least squares
prospective study
8. Places in order the effects that many re-expressions have on the data
regression to the mean
retrospective study
ladder of powers
simulation component
9. The best defense against bias - in which each individual is given a fair - random chance of selection
units
normal model
randomization
center
10. 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
matching
case
experiment
leverage
11. Anything in a survey design that influences response
observational study
center
simulation component
response bias
12. 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
regression to the mean
randomized block
normal probability plot
dotplot
13. A variable that names categories (whether with words or numerals)
re-express data
pie chart
experiment
categorical variable
14. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
simpson's paradox
regression to the mean
spread
sample size
15. Gives the possible values of the variable and the relative frequency of each value
linear model
distribution
spread
median
16. Value found by subtracting the mean and dividing by the standard deviation
matching
percentile
standardized value
interquartile range
17. Graphs a dot for each case against a single axis
dotplot
sample survey
tails
comparing distributions
18. Multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant
outlier
outlier
matched
rescaling
19. A sampling design in which entire groups are chosen at random
randomization
scatterplots
percentile
cluster sample
20. A variable whose levels are controlled by the experimenter
standard normal model
normal percentile
factor
experiment
21. Any attempt to force a sample to resemble specified attributes of the population
census
stratified random sample
dotplot
matching
22. A variable in which the numbers act as numerical values; always has units
trial
frequency table
center
quantitative variable
23. 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
experiment
direction
nonresponse bias
convenience sample
24. The natural tendency of randomly drawn samples to differ
randomized block
simpson's paradox
sampling variability
sample survey
25. A numerically valued attribute of a model for a population
changing center and spread
retrospective study
blinding
population parameter
26. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
center
symmetric
contingency table
scatterplots
27. Consists of the minimum and maximum - the quartiles Q1 and Q3 - and the median
5-number summary
center
68-95-99.7 rule
response variable
28. The distribution of a variable restricting the who to consider only a smaller group of individuals
placebo
timeplot
response bias
conditional distribution
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
contingency table
form
direction
subset
30. 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
regression line
subset
quartile
extrapolation
31. Doing this is equivalent to changing its units
changing center and spread
bimodal
multimodal
response bias
32. All experimental units have an equal chance of receiving any treatment
completely randomized design
normal percentile
68-95-99.7 rule
standard normal model
33. A sample drawn by selecting individuals systematically from a sampling frame
systematic sample
form
strength
units
34. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
outlier
correlation
data
control group
35. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
correlation
units
lurking variable
sampling variability
36. To be valid - an experiment must assign experimental units to treatment groups at random
experimental units
timeplot
random assignment
observational study
37. The square of the correlation between y and x; gives the fraction of the variability of y accounted for by the least squares linear regression on x; an overall measure of how successful the regression is in linearly relating y to x
independence
r2
lurking variable
slope
38. A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population
sample survey
population
simpson's paradox
least squares
39. Distributions with two modes
bimodal
normal model
lurking variable
tails
40. Control - randomize - replicate - block
principles of experimental design
sampling frame
intercept
factor
41. A scatterplot shows an association that is this if there is little scatter around the underlying relationship
strength
outcome
timeplot
multimodal
42. An individual about whom or which we have data
case
treatment
influential point
blinding
43. Bias introduced to a sample when a large fraction of those sampled fails to respond
spread
nonresponse bias
dotplot
statistically significant
44. Tells how many standard deviations a value is from the mean; have a mean of zero and a standard deviation of one
block
form
multistage sample
z-score
45. A normal model with a mean of 0 and a standard deviation of 1
ladder of powers
standard normal model
matched
context
46. Distributions with more than two modes
form
matched
multimodal
5-number summary
47. When both those who could influence and evaluate the results are blinded
predicted value
standardized value
blinding
double-blind
48. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
convenience sample
intercept
linear model
level
49. The sum of squared deviations from the mean - divided by the count minus one
variance
z-score
leverage
categorical variable
50. The square root of the variance
standard deviation
population
retrospective study
response