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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. Displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values
uniform
sample size
scatterplots
boxplot
2. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
changing center and spread
shape
blinding
simple random sample
3. A variable whose values are compared across different treatments
response
parameter
normal probability plot
marginal distribution
4. A distribution that's roughly flat
uniform
lurking variable
population
timeplot
5. The most basic situation in a simulation in which something happens at random
outcome
form
simulation component
experimental units
6. 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
simulation
extrapolation
statistically significant
changing center and spread
7. Distributions with two modes
factor
bimodal
sampling variability
distribution
8. The difference between the first and third quartiles
interquartile range
context
randomized block
statistically significant
9. When groups of experimental units are similar - it is a good idea to gather them together into these
block
intercept
random
population
10. A list of individuals from whom the sample is drawn
data table
residuals
sampling frame
cluster sample
11. Shows the relationship between two quantitative variables measured on the same cases
block
shape
shifting
scatterplots
12. 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
ladder of powers
slope
sample size
uniform
13. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
blinding
regression line
categorical variable
z-score
14. Models random events by using random numbers to specify event outcomes with relative frequencies that correspond to the true real-world relative frequencies we are trying to model
shifting
spread
simulation
variance
15. Anything in a survey design that influences response
principles of experimental design
pie chart
matched
response bias
16. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
rescaling
census
center
linear model
17. Design Randomization occurring within blocks
randomized block
principles of experimental design
simple random sample
ladder of powers
18. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
boxplot
simulation component
marginal distribution
re-express data
19. 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
mode
changing center and spread
percentile
frequency table
20. This criterion specifies the unique line that minimizes the variance of the residuals or - equivalently - the sum of the squared residuals
least squares
normal probability plot
case
placebo effect
21. Useful family of models for unimodal - symmetric distributions
units
normal model
influential point
categorical variable
22. The ith ___ is the number that falls above i% of the data
trial
regression line
percentile
standardizing
23. The sequence of several components representing events that we are pretending will take place
bar chart
confounded
trial
level
24. 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
range
units
68-95-99.7 rule
random
25. 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
contingency table
model
matching
outlier
26. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
predicted value
sampling frame
lurking variable
population
27. 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
leverage
area principle
model
skewed
28. Displays data that change over time
case
cluster sample
timeplot
randomization
29. The difference between the lowest and highest values in a data set
range
multimodal
matching
correlation
30. The number of individuals in a sample
variance
population parameter
mode
sample size
31. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
sampling variability
tails
random assignment
regression to the mean
32. When averages are taken across different groups - they can appear to contradict the overall averages
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33. 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
sampling variability
experiment
lurking variable
correlation
34. The entire group of individuals or instances about whom we hope to learn
68-95-99.7 rule
population
range
slope
35. In a statistical display - each data value should be represented by the same amount of area
quantitative variable
bias
area principle
voluntary response bias
36. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
standard normal model
confounded
re-express data
population
37. An arrangement of data in which each row represents a case and each column represents a variable
treatment
observational study
data table
comparing distributions
38. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes
normal probability plot
categorical variable
standardizing
direction
39. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
least squares
percentile
placebo
unimodal
40. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
conditional distribution
control group
factor
placebo effect
41. A variable that names categories (whether with words or numerals)
center
distribution
area principle
categorical variable
42. A distribution is this if it's not symmetric and one tail stretches out farther than the other
pie chart
placebo
skewed
experiment
43. An equation of the form y-hat = b0 + b1x
r2
linear model
least squares
standard normal model
44. A representative subset of a population - examined in hope of learning about the population
placebo
sample
random
symmetric
45. The natural tendency of randomly drawn samples to differ
sampling variability
influential point
bar chart
variance
46. All experimental units have an equal chance of receiving any treatment
completely randomized design
block
response variable
spread
47. Values of this record the results of each trial with respect to what we were interested in
variable
units
response variable
mean
48. The middle value with half of the data above and half below it
sampling variability
predicted value
median
bias
49. Multiplying each data value by a constant multiplies both the measures of position and the measures of spread by that constant
mean
level
outliers
rescaling
50. 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
quartile
regression to the mean
changing center and spread
context