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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 a parameter that indexes a family of probability distributions.
Independent Selection
hypothesis
Treatment
A Statistical parameter
2. 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
The standard deviation
Null hypothesis
Conditional distribution
Simulation
3. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
The sample space
Statistical inference
An Elementary event
4. The probability of correctly detecting a false null hypothesis.
Alpha value (Level of Significance)
Simulation
Power of a test
applied statistics
5. When there is an even number of values...
That is the median value
expected value of X
A random variable
Likert scale
6. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
A Probability measure
Placebo effect
Marginal probability
Bias
7. 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.
Dependent Selection
An Elementary event
A population or statistical population
Outlier
8. 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.
The median value
covariance of X and Y
Correlation coefficient
Simple random sample
9. Is defined as the expected value of random variable (X -
Kurtosis
The Covariance between two random variables X and Y - with expected values E(X) =
Probability and statistics
Power of a test
10. The standard deviation of a sampling distribution.
Type I errors
Simple random sample
Standard error
Alpha value (Level of Significance)
11. Failing to reject a false null hypothesis.
A statistic
Type 2 Error
Binary data
That is the median value
12. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.
A data set
f(z) - and its cdf by F(z).
The sample space
Trend
13. 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
Variability
f(z) - and its cdf by F(z).
Correlation
Coefficient of determination
14. Of a group of numbers is the center point of all those number values.
Random variables
Sample space
The average - or arithmetic mean
The standard deviation
15. Some commonly used symbols for population parameters
the population mean
Statistics
Probability and statistics
applied statistics
16. 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
Interval measurements
Skewness
nominal - ordinal - interval - and ratio
Type I errors & Type II errors
17. 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.
Step 3 of a statistical experiment
descriptive statistics
Sampling
Variability
18. Is a sample and the associated data points.
A data set
Dependent Selection
That value is the median value
The Expected value
19. Two variables such that their effects on the response variable cannot be distinguished from each other.
Variability
Power of a test
Confounded variables
Null hypothesis
20. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Particular realizations of a random variable
Individual
A Statistical parameter
Law of Parsimony
21. Where the null hypothesis is falsely rejected giving a 'false positive'.
covariance of X and Y
Type I errors
Confounded variables
Statistics
22. 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
Null hypothesis
Law of Large Numbers
methods of least squares
23. (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.
An Elementary event
Particular realizations of a random variable
covariance of X and Y
the sample or population mean
24. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Inferential
Prior probability
Dependent Selection
Type I errors
25. Is a sample space over which a probability measure has been defined.
Binary data
Statistical dispersion
A probability space
Statistics
26. Cov[X - Y] :
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Simulation
Variable
covariance of X and Y
27. Another name for elementary event.
Atomic event
Type I errors & Type II errors
Inferential statistics
A probability space
28. 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.
Joint probability
A likelihood function
Kurtosis
Marginal probability
29. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Type II errors
the population variance
A statistic
Simple random sample
30. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Block
Individual
A Probability measure
Placebo effect
31. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
A probability space
inferential statistics
Simple random sample
32. Some commonly used symbols for sample statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Probability density
Seasonal effect
The Range
33. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
f(z) - and its cdf by F(z).
Mutual independence
Standard error
34. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.
Probability and statistics
variance of X
Independent Selection
Experimental and observational studies
35. 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
A statistic
Divide the sum by the number of values.
experimental studies and observational studies.
variance of X
36. 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.
A Distribution function
The average - or arithmetic mean
Bias
An experimental study
37. 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
Observational study
Correlation coefficient
Divide the sum by the number of values.
Power of a test
38. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.
Estimator
Valid measure
Dependent Selection
the sample or population mean
39. (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
the population cumulants
Type I errors
Inferential
40. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Sampling Distribution
An experimental study
Credence
Skewness
41. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Type 2 Error
A statistic
A sampling distribution
The average - or arithmetic mean
42. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Sampling Distribution
applied statistics
the sample or population mean
A probability space
43. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Inferential
A probability density function
A probability distribution
Law of Parsimony
44. 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
Descriptive statistics
Probability and statistics
Independent Selection
Confounded variables
45. 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
The standard deviation
the population mean
Alpha value (Level of Significance)
46. 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
Dependent Selection
Bias
Mutual independence
A probability space
47. A numerical facsimilie or representation of a real-world phenomenon.
A statistic
Skewness
nominal - ordinal - interval - and ratio
Simulation
48. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Variable
Average and arithmetic mean
Coefficient of determination
quantitative variables
49. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.
Estimator
The median value
A Random vector
Conditional probability
50. Is a subset of the sample space - to which a probability can be assigned. For example - on rolling a die - 'getting a five or a six' is an event (with a probability of one third if the die is fair).
Statistical dispersion
An event
Placebo effect
Marginal distribution