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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. Design Randomization occurring within blocks
randomized block
spread
5-number summary
strength
2. A representative subset of a population - examined in hope of learning about the population
random
sample
outlier
center
3. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0
lurking variable
intercept
conditional distribution
dotplot
4. The linear equation y-hat = b0 + b1x that satisfies the least squares criterion
matching
level
double-blind
regression line
5. Found by summing all the data values and dividing by the count
random assignment
census
comparing distributions
mean
6. Each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean
center
nonresponse bias
regression to the mean
68-95-99.7 rule
7. When averages are taken across different groups - they can appear to contradict the overall averages
8. Doing this is equivalent to changing its units
spread
standard deviation
changing center and spread
prospective study
9. Holds information about the same characteristic for many cases
confounded
variable
shape
sample
10. The most basic situation in a simulation in which something happens at random
simulation component
contingency table
units
model
11. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value
spread
double-blind
center
conditional distribution
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
area principle
r2
bias
slope
13. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum
stratified random sample
case
quantitative variable
normal percentile
14. This criterion specifies the unique line that minimizes the variance of the residuals or - equivalently - the sum of the squared residuals
response variable
stem-and-leaf display
least squares
parameter
15. Numerically valued attribute of a model
parameter
mean
trial
percentile
16. Control - randomize - replicate - block
response variable
normal model
principles of experimental design
uniform
17. When doing this - consider their shape - center - and spread
level
residuals
leverage
comparing distributions
18. A distribution is this if the two halves on either side of the center look approximately like mirror images of each other
center
multimodal
symmetric
voluntary response bias
19. Found by substituting the x-value in the regression equation; they're the values on the fitted line
systematic sample
re-express data
predicted value
sample survey
20. A numerically valued attribute of a model for a population
population parameter
strength
undercoverage
mode
21. A numerical measure of the direction and strength of a linear association
placebo
z-score
representative
correlation
22. These are hard to generate - but several websites offer an unlimited supply of equally likely random values
histogram
random numbers
census
blinding
23. Value found by subtracting the mean and dividing by the standard deviation
blinding
factor
standardized value
response bias
24. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related
sample
lurking variable
retrospective study
normal model
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
extrapolation
experiment
parameter
voluntary response bias
26. The sequence of several components representing events that we are pretending will take place
slope
standardized value
trial
simpson's paradox
27. A normal model with a mean of 0 and a standard deviation of 1
standard normal model
r2
outliers
simulation
28. 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
normal probability plot
direction
undercoverage
spread
29. The differences between data values and the corresponding values predicted by the regression model; ____ = observed value - predicted value
response variable
variable
confounded
residuals
30. A treatment known to have no effect - administered so that all groups experience the same conditions
multimodal
independence
outlier
placebo
31. A numerical summary of how tightly the values are clustered around the 'center'
direction
spread
percentile
block
32. An individual about whom or which we have data
case
cluster sample
experiment
linear model
33. Graphs a dot for each case against a single axis
area principle
dotplot
68-95-99.7 rule
pie chart
34. A variable other than x and y that simultaneously affects both variables - accounting for the correlation between the two
trial
multistage sample
lurking variable
subset
35. Lists the categories in a categorical variable and gives the count or percentage of observations for each category
5-number summary
frequency table
matching
regression line
36. A variable whose levels are controlled by the experimenter
random
placebo
subset
factor
37. The number of individuals in a sample
response variable
placebo effect
control group
sample size
38. A distribution is this if it's not symmetric and one tail stretches out farther than the other
regression to the mean
skewed
direction
conditional distribution
39. Sampling schemes that combine several sampling methods
residuals
multistage sample
pie chart
trial
40. The specific values that the experimenter chooses for a factor
simulation component
randomized block
level
convenience sample
41. A quantity or amount adopted as a standard of measurement - such as dollars - hours - or grams
lurking variable
normal probability plot
context
units
42. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo
linear model
frequency table
normal percentile
placebo effect
43. This corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below
placebo effect
center
normal probability plot
normal percentile
44. The sum of squared deviations from the mean - divided by the count minus one
independence
control group
mean
variance
45. Shows how a 'whole' divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category
response bias
median
pie chart
statistically significant
46. A sample drawn by selecting individuals systematically from a sampling frame
strength
systematic sample
single-blind
completely randomized design
47. The distribution of a variable restricting the who to consider only a smaller group of individuals
conditional distribution
outlier
variance
center
48. 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
least squares
symmetric
bias
experiment
49. 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
double-blind
simulation
parameter
completely randomized design
50. An arrangement of data in which each row represents a case and each column represents a variable
matched
nonresponse bias
data table
correlation