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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. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Count data
applied statistics
Parameter - or 'statistical parameter'
quantitative variables
2. Var[X] :
variance of X
Individual
Binomial experiment
Probability density
3. Working from a null hypothesis two basic forms of error are recognized:
Marginal distribution
Valid measure
Statistic
Type I errors & Type II errors
4. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
Divide the sum by the number of values.
Pairwise independence
A sample
5. A measure that is relevant or appropriate as a representation of that property.
Mutual independence
Ordinal measurements
Valid measure
A probability distribution
6. 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 sample
An event
P-value
Type 1 Error
7. The collection of all possible outcomes in an experiment.
That is the median value
Divide the sum by the number of values.
Sample space
Dependent Selection
8. Is data arising from counting that can take only non-negative integer values.
Count data
The average - or arithmetic mean
A population or statistical population
Simpson's Paradox
9. Describes a characteristic of an individual to be measured or observed.
Observational study
Variable
Interval measurements
Likert scale
10. 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
quantitative variables
Sample space
Greek letters
Step 2 of a statistical experiment
11. Is a sample space over which a probability measure has been defined.
A statistic
Greek letters
Confounded variables
A probability space
12. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistical adjustment
Inferential statistics
Marginal probability
Probability density functions
13. Have imprecise differences between consecutive values - but have a meaningful order to those values
Type I errors
Ordinal measurements
Average and arithmetic mean
Prior probability
14. Have no meaningful rank order among values.
Simulation
quantitative variables
Nominal measurements
Greek letters
15. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Binary data
That value is the median value
Greek letters
Descriptive
16. 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.
Inferential statistics
The variance of a random variable
Probability density
The median value
17. 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.
An Elementary event
Simulation
A random variable
Prior probability
18. 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.
A likelihood function
Sampling Distribution
That value is the median value
Null hypothesis
19. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Pairwise independence
An Elementary event
Correlation
20. 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
Simpson's Paradox
Ratio measurements
Joint probability
Prior probability
21. A measurement such that the random error is small
Marginal distribution
Qualitative variable
Ordinal measurements
Reliable measure
22. 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
The standard deviation
The variance of a random variable
A Random vector
Mutual independence
23. 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
Sampling Distribution
experimental studies and observational studies.
expected value of X
the population mean
24. (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
s-algebras
quantitative variables
Standard error
The Expected value
25. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co
Marginal probability
The Covariance between two random variables X and Y - with expected values E(X) =
Step 1 of a statistical experiment
f(z) - and its cdf by F(z).
26. A group of individuals sharing some common features that might affect the treatment.
A statistic
hypotheses
Block
Binomial experiment
27. 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
Parameter
Sample space
Simple random sample
Null hypothesis
28. A list of individuals from which the sample is actually selected.
Sampling frame
Variable
Reliable measure
Block
29. Another name for elementary event.
Ordinal measurements
P-value
Statistic
Atomic event
30. 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
Statistical inference
f(z) - and its cdf by F(z).
Correlation
Inferential statistics
31. A numerical facsimilie or representation of a real-world phenomenon.
Statistics
Simulation
Sampling Distribution
Confounded variables
32. Are usually written in upper case roman letters: X - Y - etc.
Pairwise independence
Statistical dispersion
Random variables
expected value of X
33. Is data that can take only two values - usually represented by 0 and 1.
Sampling frame
Variable
Experimental and observational studies
Binary data
34. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Step 2 of a statistical experiment
Simple random sample
Likert scale
Pairwise independence
35. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
The Covariance between two random variables X and Y - with expected values E(X) =
methods of least squares
categorical variables
36. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.
Statistical dispersion
A statistic
the population mean
Kurtosis
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).
hypotheses
Type 2 Error
An experimental study
Joint probability
38. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
the sample or population mean
applied statistics
Joint distribution
An estimate of a parameter
39. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Independent Selection
Step 2 of a statistical experiment
Type 2 Error
Placebo effect
40. Many statistical methods seek to minimize the mean-squared error - and these are called
A Distribution function
Step 2 of a statistical experiment
quantitative variables
methods of least squares
41. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
Sampling frame
the population cumulants
Cumulative distribution functions
42. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Probability
Parameter
Binomial experiment
Statistical dispersion
43. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
Correlation coefficient
A Distribution function
Coefficient of determination
44. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Random vector
Independent Selection
the population cumulants
Step 2 of a statistical experiment
45. Is the length of the smallest interval which contains all the data.
The Range
Random variables
the population variance
descriptive statistics
46. 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 population or statistical population
methods of least squares
Treatment
categorical variables
47. 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.
Correlation
Statistics
Binomial experiment
Power of a test
48. 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
observational study
Experimental and observational studies
Correlation
Valid measure
49. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
A data point
the population cumulants
Estimator
Block
50. Is defined as the expected value of random variable (X -
experimental studies and observational studies.
A population or statistical population
Joint probability
The Covariance between two random variables X and Y - with expected values E(X) =