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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. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
That value is the median value
Step 1 of a statistical experiment
variance of X
2. 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 Mean of a random variable
A Distribution function
Lurking variable
The average - or arithmetic mean
3. 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.
Beta value
Reliable measure
Skewness
4. 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.
the population variance
Reliable measure
Bias
A statistic
5. Describes a characteristic of an individual to be measured or observed.
Variable
The standard deviation
Placebo effect
The Expected value
6. 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
Probability density
s-algebras
Valid measure
Individual
7. Var[X] :
nominal - ordinal - interval - and ratio
Null hypothesis
Power of a test
variance of X
8. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Binary data
categorical variables
Inferential statistics
The Range
9. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistical adjustment
A sampling distribution
methods of least squares
The average - or arithmetic mean
10. Long-term upward or downward movement over time.
applied statistics
Probability and statistics
Valid measure
Trend
11. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Simulation
covariance of X and Y
applied statistics
Correlation coefficient
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.
The variance of a random variable
P-value
Type II errors
An experimental study
13. ?
Variability
the population correlation
Statistical inference
Observational study
14. 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
Beta value
A data point
Probability and statistics
Confounded variables
15. The standard deviation of a sampling distribution.
Ratio measurements
Joint distribution
Standard error
Inferential
16. 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.
categorical variables
Prior probability
A probability density function
The median value
17. A variable describes an individual by placing the individual into a category or a group.
Bias
Joint distribution
Qualitative variable
A sample
18. Is defined as the expected value of random variable (X -
Statistics
categorical variables
The Covariance between two random variables X and Y - with expected values E(X) =
Variability
19. 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
the sample or population mean
Correlation
A data set
20. (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
Qualitative variable
Alpha value (Level of Significance)
A likelihood function
the population mean
21. 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
Joint distribution
Quantitative variable
Posterior probability
22. 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.
Lurking variable
Credence
Correlation coefficient
Type 2 Error
23. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Law of Large Numbers
Descriptive
24. 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
descriptive statistics
Step 3 of a statistical experiment
A Random vector
expected value of X
25. Failing to reject a false null hypothesis.
Placebo effect
The average - or arithmetic mean
Type 2 Error
Count data
26. 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.
Pairwise independence
Statistics
Probability density
descriptive statistics
27. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Random vector
Type I errors
Marginal probability
A random variable
28. Cov[X - Y] :
Quantitative variable
Qualitative variable
covariance of X and Y
observational study
29. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.
Posterior probability
Block
That value is the median value
A sample
30. When you have two or more competing models - choose the simpler of the two models.
The Range
Probability density
A data point
Law of Parsimony
31. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Law of Large Numbers
A Statistical parameter
32. A numerical facsimilie or representation of a real-world phenomenon.
Sampling frame
the population mean
Simulation
The Covariance between two random variables X and Y - with expected values E(X) =
33. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
applied statistics
Probability density functions
Correlation coefficient
Type II errors
34. S^2
the population variance
the population correlation
Confounded variables
Kurtosis
35. The probability of correctly detecting a false null hypothesis.
Sampling
Lurking variable
Power of a test
Bias
36. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
hypothesis
Step 2 of a statistical experiment
the population mean
Probability density functions
37. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
A random variable
nominal - ordinal - interval - and ratio
Simpson's Paradox
A likelihood function
38. (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.
categorical variables
nominal - ordinal - interval - and ratio
Step 2 of a statistical experiment
An Elementary event
39. A numerical measure that describes an aspect of a sample.
Statistic
the population mean
Probability and statistics
Sampling
40. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Sampling
Conditional probability
variance of X
Particular realizations of a random variable
41. A group of individuals sharing some common features that might affect the treatment.
Block
A Random vector
Statistical adjustment
Law of Large Numbers
42. A measurement such that the random error is small
Type I errors & Type II errors
Law of Parsimony
Reliable measure
Block
43. 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 likelihood function
Joint probability
Marginal probability
Simulation
44. Are usually written in upper case roman letters: X - Y - etc.
Kurtosis
Divide the sum by the number of values.
the sample or population mean
Random variables
45. 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
Type I errors
f(z) - and its cdf by F(z).
Correlation coefficient
Step 2 of a statistical experiment
46. The collection of all possible outcomes in an experiment.
Experimental and observational studies
Bias
quantitative variables
Sample space
47. 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
Ratio measurements
Lurking variable
methods of least squares
Type I errors
48. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
Statistical dispersion
Seasonal effect
Posterior probability
49. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
s-algebras
Credence
Statistical adjustment
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).
A random variable
An event
A probability distribution
the population mean