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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 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.
Experimental and observational studies
Probability density functions
Statistics
Kurtosis
2. Probability of rejecting a true null hypothesis.
Average and arithmetic mean
A probability distribution
Statistical inference
Alpha value (Level of Significance)
3. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
Divide the sum by the number of values.
Pairwise independence
experimental studies and observational studies.
4. When there is an even number of values...
Joint distribution
An experimental study
Conditional distribution
That is the median value
5. Data are gathered and correlations between predictors and response are investigated.
Conditional distribution
An experimental study
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
observational study
6. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Qualitative variable
A likelihood function
Reliable measure
Prior probability
7. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies
Prior probability
Statistical inference
experimental studies and observational studies.
Observational study
8. A variable describes an individual by placing the individual into a category or a group.
variance of X
the population mean
A Distribution function
Qualitative variable
9. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Power of a test
A probability space
Alpha value (Level of Significance)
Inferential
10. 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
hypotheses
Random variables
Correlation
Statistical dispersion
11. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. 4. Further examining the data set in secondary analyses - to suggest new hypotheses for future study. 5. Documenting and present
Binomial experiment
Step 3 of a statistical experiment
observational study
Type I errors & Type II errors
12. Long-term upward or downward movement over time.
Qualitative variable
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Type 1 Error
Trend
13. 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
Sampling frame
Observational study
The Mean of a random variable
14. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
A Statistical parameter
Joint distribution
Binary data
Sampling
15. 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
Descriptive statistics
Atomic event
experimental studies and observational studies.
16. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
Simulation
s-algebras
The sample space
Credence
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.
Step 2 of a statistical experiment
Law of Parsimony
Marginal distribution
nominal - ordinal - interval - and ratio
18. 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.
methods of least squares
Simple random sample
Descriptive statistics
quantitative variables
19. Describes a characteristic of an individual to be measured or observed.
That value is the median value
Divide the sum by the number of values.
Variable
Mutual independence
20. Have imprecise differences between consecutive values - but have a meaningful order to those values
Dependent Selection
covariance of X and Y
Step 3 of a statistical experiment
Ordinal measurements
21. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)
Interval measurements
Placebo effect
Binomial experiment
nominal - ordinal - interval - and ratio
22. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Joint distribution
A probability density function
A sampling distribution
A data set
23. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Simulation
applied statistics
A random variable
nominal - ordinal - interval - and ratio
24. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Ratio measurements
Statistical adjustment
Sampling Distribution
An event
25. 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
Type I errors
Greek letters
Particular realizations of a random variable
Inferential statistics
26. ?
Reliable measure
The variance of a random variable
the population correlation
Power of a test
27. Gives the probability of events in a probability space.
A population or statistical population
A Probability measure
expected value of X
Estimator
28. A numerical measure that describes an aspect of a sample.
hypotheses
Statistic
observational study
An experimental study
29. Is defined as the expected value of random variable (X -
Experimental and observational studies
applied statistics
The Covariance between two random variables X and Y - with expected values E(X) =
hypothesis
30. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Quantitative variable
A probability space
Atomic event
Placebo effect
31. 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
Descriptive statistics
Null hypothesis
Independent Selection
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
32. 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
Simulation
methods of least squares
Step 2 of a statistical experiment
33. Is a sample space over which a probability measure has been defined.
Probability density
A probability space
A Distribution function
Statistical adjustment
34. (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
Ordinal measurements
Particular realizations of a random variable
Interval measurements
35. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Residuals
Experimental and observational studies
Descriptive
Atomic event
36. Have no meaningful rank order among values.
Type 1 Error
Parameter - or 'statistical parameter'
Nominal measurements
That is the median value
37. 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).
Joint probability
Type II errors
observational study
Qualitative variable
38. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
A probability density function
methods of least squares
Simulation
39. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.
Binomial experiment
Statistical inference
Treatment
An experimental study
40. 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
Qualitative variable
Probability density
Step 1 of a statistical experiment
Block
41. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
P-value
Statistical adjustment
Statistics
the sample or population mean
42. Is the length of the smallest interval which contains all the data.
Joint distribution
A probability density function
nominal - ordinal - interval - and ratio
The Range
43. Any specific experimental condition applied to the subjects
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Treatment
Parameter
That is the median value
44. 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.
the population correlation
Reliable measure
Parameter
Marginal probability
45. A list of individuals from which the sample is actually selected.
Credence
Sampling frame
Statistics
Valid measure
46. The probability of correctly detecting a false null hypothesis.
Power of a test
Conditional probability
Variable
Statistical adjustment
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
Statistics
expected value of X
the population correlation
Probability
48. In particular - the pdf of the standard normal distribution is denoted by
Quantitative variable
f(z) - and its cdf by F(z).
Kurtosis
observational study
49. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
The standard deviation
Coefficient of determination
Binary data
An experimental study
50. Working from a null hypothesis two basic forms of error are recognized:
A likelihood function
s-algebras
Type I errors & Type II errors
A population or statistical population