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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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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. Rejecting a true null hypothesis.
Power of a test
Type 1 Error
Parameter
Sample space
2. Is a parameter that indexes a family of probability distributions.
Marginal distribution
A Probability measure
A Statistical parameter
Treatment
3. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
variance of X
Cumulative distribution functions
the sample or population mean
4. 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
Law of Large Numbers
Posterior probability
Ratio measurements
Seasonal effect
5. Is data arising from counting that can take only non-negative integer values.
Binomial experiment
s-algebras
Type 2 Error
Count data
6. Probability of accepting a false null hypothesis.
Divide the sum by the number of values.
Count data
Beta value
Law of Large Numbers
7. Is the length of the smallest interval which contains all the data.
A probability density function
The standard deviation
Probability
The Range
8. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
covariance of X and Y
Type I errors & Type II errors
Descriptive
9. 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 population correlation
Simple random sample
Confounded variables
Step 2 of a statistical experiment
10. Describes the spread in the values of the sample statistic when many samples are taken.
Mutual independence
Binomial experiment
Variability
Valid measure
11. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
Credence
Law of Parsimony
Type 1 Error
12. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.
Marginal distribution
Descriptive
An Elementary event
The variance of a random variable
13. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Variable
Parameter - or 'statistical parameter'
Probability and statistics
Placebo effect
14. 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
expected value of X
inferential statistics
Type II errors
The Mean of a random variable
15. Working from a null hypothesis two basic forms of error are recognized:
s-algebras
Simpson's Paradox
Observational study
Type I errors & Type II errors
16. S^2
Likert scale
A likelihood function
Variability
the population variance
17. The proportion of the explained variation by a linear regression model in the total variation.
An Elementary event
Bias
categorical variables
Coefficient of determination
18. 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.
Nominal measurements
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Statistics
A sample
19. 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
Sampling Distribution
the population mean
Type II errors
20. A subjective estimate of probability.
Statistic
Estimator
Null hypothesis
Credence
21. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.
A sample
Statistical inference
A random variable
A probability density function
22. 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.
Individual
Bias
Inferential
f(z) - and its cdf by F(z).
23. 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
the population mean
The average - or arithmetic mean
A sample
24. 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 probability density function
Binary data
inferential statistics
25. In particular - the pdf of the standard normal distribution is denoted by
The average - or arithmetic mean
Interval measurements
Trend
f(z) - and its cdf by F(z).
26. 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
Skewness
Seasonal effect
Variable
27. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Dependent Selection
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
A Random vector
the sample or population mean
28. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Type I errors
Law of Large Numbers
Statistics
Treatment
29. Probability of rejecting a true null hypothesis.
Type 1 Error
Alpha value (Level of Significance)
A Statistical parameter
Conditional distribution
30. Two variables such that their effects on the response variable cannot be distinguished from each other.
observational study
Confounded variables
variance of X
Binomial experiment
31. 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
variance of X
Alpha value (Level of Significance)
A random variable
32. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Variable
A probability distribution
Interval measurements
Probability density functions
33. Any specific experimental condition applied to the subjects
Step 2 of a statistical experiment
observational study
Binary data
Treatment
34. Failing to reject a false null hypothesis.
Type 2 Error
A data set
Prior probability
The Covariance between two random variables X and Y - with expected values E(X) =
35. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
The Covariance between two random variables X and Y - with expected values E(X) =
the population correlation
Greek letters
Type II errors
36. When there is an even number of values...
The average - or arithmetic mean
Cumulative distribution functions
That is the median value
The sample space
37. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Random variables
The Range
Probability density functions
A likelihood function
38.
observational study
Joint probability
the population mean
That is the median value
39. Are usually written in upper case roman letters: X - Y - etc.
Experimental and observational studies
Descriptive
Random variables
Probability density
40. Gives the probability of events in a probability space.
Residuals
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Law of Parsimony
A Probability measure
41. A numerical measure that describes an aspect of a sample.
A statistic
Marginal distribution
Conditional probability
Statistic
42. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Pairwise independence
the population cumulants
Individual
Binomial experiment
43. 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.
A probability distribution
A population or statistical population
Type I errors & Type II errors
Average and arithmetic mean
44. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Reliable measure
Simple random sample
Independence or Statistical independence
45. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
The standard deviation
methods of least squares
Law of Large Numbers
46. A list of individuals from which the sample is actually selected.
An experimental study
Coefficient of determination
Sampling frame
Independent Selection
47. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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48. 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
A random variable
Variability
Independence or Statistical independence
Descriptive statistics
49. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Random vector
Independence or Statistical independence
Count data
Bias
50. Long-term upward or downward movement over time.
The Mean of a random variable
Qualitative variable
Ratio measurements
Trend