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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. Is that part of a population which is actually observed.
The sample space
A sample
Ordinal measurements
Seasonal effect
2. Is a sample and the associated data points.
An estimate of a parameter
The Covariance between two random variables X and Y - with expected values E(X) =
Treatment
A data set
3. 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
Step 2 of a statistical experiment
Nominal measurements
Placebo effect
hypotheses
4. To find the average - or arithmetic mean - of a set of numbers:
Conditional distribution
the population mean
Divide the sum by the number of values.
Alpha value (Level of Significance)
5. Gives the probability of events in a probability space.
Quantitative variable
A Distribution function
A Probability measure
Atomic event
6. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.
Mutual independence
An Elementary event
Probability
Individual
7. Working from a null hypothesis two basic forms of error are recognized:
Probability density
The Covariance between two random variables X and Y - with expected values E(X) =
Type I errors & Type II errors
Coefficient of determination
8. Is the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.
Valid measure
Marginal probability
experimental studies and observational studies.
Dependent Selection
9. Are simply two different terms for the same thing. Add the given values
Posterior probability
s-algebras
Average and arithmetic mean
Qualitative variable
10. 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.
A probability space
Estimator
A data point
hypothesis
11. Of a group of numbers is the center point of all those number values.
Probability
s-algebras
Statistical adjustment
The average - or arithmetic mean
12. E[X] :
Statistical adjustment
A Random vector
expected value of X
Marginal distribution
13. The probability of correctly detecting a false null hypothesis.
inferential statistics
Statistic
Step 2 of a statistical experiment
Power of a test
14. S^2
hypothesis
Step 3 of a statistical experiment
the population variance
Random variables
15. A data value that falls outside the overall pattern of the graph.
Seasonal effect
Outlier
Placebo effect
expected value of X
16. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
A Probability measure
The Covariance between two random variables X and Y - with expected values E(X) =
Quantitative variable
A data set
17. 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.
Marginal distribution
applied statistics
A Probability measure
Mutual independence
18. Is the probability of two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).
Confounded variables
Outlier
Joint probability
applied statistics
19. Rejecting a true null hypothesis.
Interval measurements
Count data
Estimator
Type 1 Error
20. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Bias
Statistic
Binomial experiment
the population cumulants
21. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o
Lurking variable
A Distribution function
Inferential statistics
Observational study
22. Is data that can take only two values - usually represented by 0 and 1.
Binary data
A statistic
Qualitative variable
Independence or Statistical independence
23. (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
Estimator
Random variables
The Expected value
Valid measure
24. (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 likelihood function
A Distribution function
Variability
Lurking variable
25. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.
That is the median value
Mutual independence
Conditional probability
A population or statistical population
26. Probability of accepting a false null hypothesis.
Beta value
A probability distribution
Bias
Individual
27. 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.
Independence or Statistical independence
A statistic
Statistics
Estimator
28. Cov[X - Y] :
A Distribution function
Descriptive
Probability
covariance of X and Y
29. In number theory - scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns - which may then lead to
A population or statistical population
hypotheses
Particular realizations of a random variable
Inferential
30. A numerical measure that assesses the strength of a linear relationship between two variables.
Parameter - or 'statistical parameter'
Posterior probability
Correlation coefficient
Simulation
31. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
covariance of X and Y
Law of Parsimony
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Individual
32. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P
Estimator
Correlation
Skewness
Residuals
33. ?
the population correlation
hypotheses
Experimental and observational studies
Joint distribution
34. Patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations - and are then used for drawing inferences about the process or population being studied; this is called
Binary data
Variability
inferential statistics
the population mean
35. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.
The sample space
A Distribution function
Valid measure
observational study
36. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'
Placebo effect
Residuals
Conditional probability
Statistical dispersion
37. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
A likelihood function
Placebo effect
The median value
Probability density
38. Data are gathered and correlations between predictors and response are investigated.
The Covariance between two random variables X and Y - with expected values E(X) =
observational study
Null hypothesis
A Probability measure
39. Any specific experimental condition applied to the subjects
A probability density function
Correlation coefficient
Treatment
Probability and statistics
40. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are
expected value of X
Descriptive statistics
Beta value
Statistical inference
41. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Parameter - or 'statistical parameter'
Joint distribution
Inferential
The median value
42. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Type I errors & Type II errors
A probability distribution
Independence or Statistical independence
Inferential statistics
43. Some commonly used symbols for sample statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Probability
That value is the median value
A probability space
44. Another name for elementary event.
Correlation
Atomic event
A data set
applied statistics
45. Where the null hypothesis is falsely rejected giving a 'false positive'.
Correlation
Correlation coefficient
Individual
Type I errors
46. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a
A probability density function
Null hypothesis
Law of Large Numbers
Binary data
47. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the
Probability
A Statistical parameter
Law of Parsimony
Interval measurements
48. Is a sample space over which a probability measure has been defined.
A probability density function
Bias
A probability space
Particular realizations of a random variable
49. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
A Random vector
Independence or Statistical independence
Block
50. Is data arising from counting that can take only non-negative integer values.
Descriptive
Correlation
Count data
Statistics