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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 subjective estimate of probability.
Placebo effect
applied statistics
The average - or arithmetic mean
Credence
2. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
The Covariance between two random variables X and Y - with expected values E(X) =
the population mean
applied statistics
A Statistical parameter
3. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data
Atomic event
Ratio measurements
Pairwise independence
Outlier
4. 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.
A Distribution function
Marginal probability
Probability and statistics
Beta value
5. Of a group of numbers is the center point of all those number values.
An experimental study
covariance of X and Y
The average - or arithmetic mean
Power of a test
6. Failing to reject a false null hypothesis.
Type 2 Error
P-value
A sampling distribution
A probability space
7. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no
Probability and statistics
Estimator
The Mean of a random variable
Valid measure
8. 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.
A sample
A Random vector
the population mean
Marginal distribution
9. 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).
Parameter - or 'statistical parameter'
Joint distribution
the population correlation
An event
10. Var[X] :
Nominal measurements
Parameter
variance of X
A data set
11. Have imprecise differences between consecutive values - but have a meaningful order to those values
The variance of a random variable
Ordinal measurements
A likelihood function
Skewness
12. Gives the probability of events in a probability space.
A Probability measure
Step 1 of a statistical experiment
The variance of a random variable
A sampling distribution
13. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.
A population or statistical population
Residuals
Bias
Probability
14. The collection of all possible outcomes in an experiment.
Prior probability
A Statistical parameter
Sample space
Coefficient of determination
15. Is the exact middle value of a set of numbers Arrange the numbers in numerical order. Find the value in the middle of the list.
The median value
The standard deviation
A Statistical parameter
Variable
16. Is its expected value. The mean (or sample mean of a data set is just the average value.
covariance of X and Y
inferential statistics
The Mean of a random variable
Atomic event
17. A numerical measure that assesses the strength of a linear relationship between two variables.
A sampling distribution
Type I errors
Correlation coefficient
Trend
18. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Statistics
Null hypothesis
Probability density functions
Type 2 Error
19. 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.
Power of a test
Average and arithmetic mean
Ordinal measurements
Estimator
20. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
Alpha value (Level of Significance)
Type 1 Error
applied statistics
21. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
Null hypothesis
Inferential
A Probability measure
22. A common goal for a statistical research project is to investigate causality - and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response.
Experimental and observational studies
Conditional probability
A probability distribution
An event
23. 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
A sampling distribution
The Expected value
Probability density
Sampling
24. ?r
the population cumulants
nominal - ordinal - interval - and ratio
A data set
Qualitative variable
25. Data are gathered and correlations between predictors and response are investigated.
Statistic
A probability distribution
observational study
the population correlation
26. 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}.
Credence
Dependent Selection
The sample space
Joint probability
27. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
observational study
Joint distribution
The standard deviation
An event
28. A measure that is relevant or appropriate as a representation of that property.
Valid measure
Statistics
A sampling distribution
Trend
29. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
nominal - ordinal - interval - and ratio
Descriptive statistics
Kurtosis
A Random vector
30. 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
Correlation
covariance of X and Y
Sample space
Type I errors & Type II errors
31. 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.
Seasonal effect
A random variable
Marginal probability
A Distribution function
32. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.
Posterior probability
Reliable measure
Random variables
A random variable
33. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.
Conditional distribution
experimental studies and observational studies.
Posterior probability
Atomic event
34. Describes a characteristic of an individual to be measured or observed.
s-algebras
Outlier
hypotheses
Variable
35. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
the population mean
Law of Large Numbers
Posterior probability
Credence
36. A group of individuals sharing some common features that might affect the treatment.
Divide the sum by the number of values.
Binary data
Block
Posterior probability
37. Rejecting a true null hypothesis.
Conditional distribution
Type I errors
Average and arithmetic mean
Type 1 Error
38. Is the probability distribution - under repeated sampling of the population - of a given statistic.
A sampling distribution
A probability distribution
the population variance
the population mean
39. 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
Sampling
A population or statistical population
Conditional distribution
40. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.
the population correlation
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Lurking variable
Atomic event
41. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
the sample or population mean
Inferential
Qualitative variable
applied statistics
42. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Variable
A likelihood function
Joint distribution
Treatment
43. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
inferential statistics
Block
Sampling Distribution
Residuals
44. Many statistical methods seek to minimize the mean-squared error - and these are called
Ratio measurements
methods of least squares
hypothesis
A statistic
45. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
the sample or population mean
Sampling frame
Law of Large Numbers
Particular realizations of a random variable
46. 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
experimental studies and observational studies.
Bias
Joint probability
Skewness
47. A numerical facsimilie or representation of a real-world phenomenon.
Binomial experiment
Parameter - or 'statistical parameter'
Joint probability
Simulation
48. 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).
A Statistical parameter
Step 3 of a statistical experiment
Null hypothesis
Joint probability
49. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i
Treatment
Independence or Statistical independence
descriptive statistics
Posterior probability
50. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Random vector
categorical variables
the population mean
Credence