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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. In a statistical display - each data value should be represented by the same amount of area
sampling frame
multistage sample
area principle
normal model
2. The distribution of either variable alone in a contingency table; the counts or percentages are the totals found in the margins (last row or column) of the table
boxplot
interquartile range
slope
marginal distribution
3. Found by summing all the data values and dividing by the count
placebo effect
quartile
simple random sample
mean
4. This of sample size n is one in which each set of n elements in the population has an equal chance of selection
response variable
single-blind
simple random sample
random
5. Gives the possible values of the variable and the frequency or relative frequency of each value
stem-and-leaf display
normal percentile
r2
distribution
6. The best defense against bias - in which each individual is given a fair - random chance of selection
percentile
randomization
outcome
lurking variable
7. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
single-blind
spread
tails
placebo effect
8. Distributions with more than two modes
single-blind
response
case
multimodal
9. An individual about whom or which we have data
mean
experimental units
case
outcome
10. A sample drawn by selecting individuals systematically from a sampling frame
re-express data
timeplot
systematic sample
median
11. Any attempt to force a sample to resemble specified attributes of the population
ladder of powers
re-express data
matching
quartile
12. An arrangement of data in which each row represents a case and each column represents a variable
sample
normal probability plot
extrapolation
data table
13. Holds information about the same characteristic for many cases
lurking variable
strength
variable
frequency table
14. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
simple random sample
residuals
regression line
placebo
15. The experimental units assigned to a baseline treatment level - typically either the default treatment - which is well understood - or a null - placebo treatment
representative
control group
slope
range
16. A study based on data in which no manipulation of factors has been employed
model
outcome
sample survey
observational study
17. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
intercept
rescaling
linear model
shape
18. A distribution is this if it's not symmetric and one tail stretches out farther than the other
contingency table
outcome
skewed
experimental units
19. 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
population
median
percentile
68-95-99.7 rule
20. The distribution of a variable restricting the who to consider only a smaller group of individuals
sample size
conditional distribution
bias
principles of experimental design
21. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
stratified random sample
median
slope
categorical variable
22. The difference between the first and third quartiles
scatterplots
comparing distributions
bar chart
interquartile range
23. Adding a constant to each data value adds the same constant to the mean - the median - and the quartiles - but does not change the standard deviation or IQR
variance
shifting
representative
5-number summary
24. The sequence of several components representing events that we are pretending will take place
leverage
trial
dotplot
direction
25. All experimental units have an equal chance of receiving any treatment
observational study
completely randomized design
simpson's paradox
outcome
26. A variable that names categories (whether with words or numerals)
re-express data
categorical variable
rescaling
principles of experimental design
27. The ____ we care about most is straight
form
systematic sample
simple random sample
matched
28. Design Randomization occurring within blocks
scatterplots
randomized block
form
bar chart
29. This criterion specifies the unique line that minimizes the variance of the residuals or - equivalently - the sum of the squared residuals
standardized value
independence
sample survey
least squares
30. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups
blinding
randomized block
scatterplots
slope
31. 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
randomization
histogram
experimental units
intercept
32. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
timeplot
nonresponse bias
lurking variable
shape
33. When the levels of one factor are associated with the levels of another factor so their effects cannot be separated
5-number summary
confounded
experimental units
quartile
34. The number of individuals in a sample
sample size
percentile
random assignment
statistically significant
35. 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
experiment
statistically significant
variance
normal probability plot
36. Numerically valued attribute of a model
sample size
parameter
response variable
variance
37. 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
slope
variance
response bias
convenience sample
38. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
shifting
simulation component
stratified random sample
center
39. Useful family of models for unimodal - symmetric distributions
blinding
least squares
variable
normal model
40. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set
re-express data
r2
outlier
quartile
41. When doing this - consider their shape - center - and spread
block
comparing distributions
re-express data
multistage sample
42. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped
direction
data table
sampling frame
unimodal
43. A representative subset of a population - examined in hope of learning about the population
sample
blinding
boxplot
residuals
44. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
frequency table
simulation
experiment
lurking variable
45. A sampling design in which entire groups are chosen at random
randomized block
cluster sample
contingency table
experiment
46. A numerical summary of how tightly the values are clustered around the 'center'
model
spread
census
statistically significant
47. Value found by subtracting the mean and dividing by the standard deviation
matched
retrospective study
standardized value
experiment
48. 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
case
control group
placebo effect
49. Summarized with the mean or the median
center
unimodal
sample survey
double-blind
50. When averages are taken across different groups - they can appear to contradict the overall averages
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