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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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Subjects
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clep
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math
Instructions:
Answer 50 questions in 15 minutes.
If you are not ready to take this test, you can
study here
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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. A measurement such that the random error is small
Posterior probability
Conditional probability
Placebo effect
Reliable measure
2. Var[X] :
Block
Bias
variance of X
Ratio measurements
3. Is denoted by - pronounced 'x bar'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
The average - or arithmetic mean
A probability distribution
Beta value
4. Failing to reject a false null hypothesis.
Law of Parsimony
Type 2 Error
Null hypothesis
Divide the sum by the number of values.
5. Are simply two different terms for the same thing. Add the given values
P-value
Residuals
Average and arithmetic mean
Independent Selection
6. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
Count data
Posterior probability
Coefficient of determination
7. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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8. Any specific experimental condition applied to the subjects
Treatment
Probability
observational study
Binary data
9. The collection of all possible outcomes in an experiment.
Experimental and observational studies
Simpson's Paradox
Statistics
Sample space
10. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Correlation
Probability and statistics
Prior probability
Cumulative distribution functions
11. Is that part of a population which is actually observed.
A probability space
Likert scale
Divide the sum by the number of values.
A sample
12. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
quantitative variables
Type I errors
The standard deviation
A probability density function
13. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit
Independent Selection
Probability density
Variable
P-value
14. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co
Step 1 of a statistical experiment
Binomial experiment
Step 2 of a statistical experiment
Parameter
15. (cdfs) are denoted by upper case letters - e.g. F(x).
An estimate of a parameter
Cumulative distribution functions
Observational study
Conditional probability
16. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.
That value is the median value
Nominal measurements
Descriptive
A sampling distribution
17. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
covariance of X and Y
categorical variables
A sample
A random variable
18. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then
A Probability measure
A likelihood function
Sampling Distribution
Particular realizations of a random variable
19. Long-term upward or downward movement over time.
Statistics
Trend
Binary data
Nominal measurements
20. E[X] :
expected value of X
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Statistical dispersion
The variance of a random variable
21. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.
Conditional distribution
applied statistics
Statistics
Statistical inference
22. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
That value is the median value
Confounded variables
Step 1 of a statistical experiment
23. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.
Probability density
Simpson's Paradox
Trend
Marginal distribution
24. Is defined as the expected value of random variable (X -
Block
Correlation
The Covariance between two random variables X and Y - with expected values E(X) =
Atomic event
25. S^2
Descriptive statistics
A Random vector
the population variance
Inferential
26. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
Pairwise independence
Ratio measurements
Statistical inference
A data point
27. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Law of Parsimony
Divide the sum by the number of values.
A sampling distribution
Skewness
28. Is the length of the smallest interval which contains all the data.
The Range
Probability density functions
Beta value
Statistics
29. Where the null hypothesis is falsely rejected giving a 'false positive'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Reliable measure
Type I errors
Null hypothesis
30. A list of individuals from which the sample is actually selected.
Sampling frame
quantitative variables
Statistical inference
A data point
31. Some commonly used symbols for population parameters
Marginal probability
Binary data
the population mean
hypotheses
32. In particular - the pdf of the standard normal distribution is denoted by
Inferential
the population mean
categorical variables
f(z) - and its cdf by F(z).
33. A numerical measure that describes an aspect of a sample.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Type II errors
Statistic
A sample
34. Uses patterns in the sample data to draw inferences about the population represented - accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing) - estimating numerical characteris
A sampling distribution
Simpson's Paradox
Inferential statistics
nominal - ordinal - interval - and ratio
35. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
expected value of X
Probability density functions
Residuals
applied statistics
36. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe
Skewness
Sampling Distribution
P-value
Type 2 Error
37. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Probability and statistics
Inferential
Prior probability
Credence
38. Gives the probability distribution for a continuous random variable.
Probability density
Type 1 Error
A probability density function
Joint distribution
39. A group of individuals sharing some common features that might affect the treatment.
Type I errors & Type II errors
Skewness
Count data
Block
40. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re
The Expected value
An event
Prior probability
the sample or population mean
41. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.
Simple random sample
Variability
Count data
Descriptive
42. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Variability
Bias
Individual
Kurtosis
43. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a
methods of least squares
Step 2 of a statistical experiment
P-value
That value is the median value
44. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
hypothesis
Lurking variable
Type 1 Error
Posterior probability
45. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.
A Probability measure
Sampling
Dependent Selection
applied statistics
46. Data are gathered and correlations between predictors and response are investigated.
Sampling frame
Interval measurements
observational study
Statistic
47. The standard deviation of a sampling distribution.
Standard error
A data point
Atomic event
nominal - ordinal - interval - and ratio
48. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
Alpha value (Level of Significance)
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
applied statistics
49. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl
Mutual independence
A data point
Probability density
Statistic
50. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Bias
Interval measurements
Quantitative variable
Coefficient of determination