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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. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Nominal measurements
Residuals
Inferential
An event
2. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
3. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
A statistic
Statistical dispersion
Ratio measurements
Bias
4. 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.
hypothesis
A random variable
The Range
A Distribution function
5. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.
Bias
Atomic event
Seasonal effect
Average and arithmetic mean
6. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Quantitative variable
Conditional distribution
That value is the median value
A statistic
7. 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.
Kurtosis
Sampling
A probability distribution
Qualitative variable
8. A numerical measure that describes an aspect of a population.
Parameter
the population variance
Law of Large Numbers
Posterior probability
9. 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
The median value
A Distribution function
Step 1 of a statistical experiment
The Covariance between two random variables X and Y - with expected values E(X) =
10. 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.
Random variables
Standard error
That value is the median value
Correlation
11. Another name for elementary event.
Count data
Atomic event
Divide the sum by the number of values.
That value is the median value
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.
A likelihood function
The variance of a random variable
Valid measure
expected value of X
13. Statistical methods can be used for summarizing or describing a collection of data; this is called
Binary data
Step 1 of a statistical experiment
descriptive statistics
A probability density function
14. 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.
Statistical dispersion
Independent Selection
A Probability measure
Simpson's Paradox
15. 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
An event
Count data
Null hypothesis
Pairwise independence
16. 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.
Statistical inference
P-value
Type II errors
Marginal probability
17. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
Law of Parsimony
Outlier
Sampling
18. 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
Mutual independence
Ordinal measurements
Probability density
Sampling frame
19. ?
Simpson's Paradox
the population correlation
Random variables
Sampling Distribution
20. Is a sample space over which a probability measure has been defined.
A probability space
Quantitative variable
Count data
Step 2 of a statistical experiment
21. The collection of all possible outcomes in an experiment.
Qualitative variable
Statistical dispersion
Sample space
Type 2 Error
22. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
hypothesis
Bias
Average and arithmetic mean
Particular realizations of a random variable
23. Of a group of numbers is the center point of all those number values.
Cumulative distribution functions
The average - or arithmetic mean
Valid measure
Credence
24. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
Parameter - or 'statistical parameter'
s-algebras
The average - or arithmetic mean
Marginal probability
25. 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
A sample
A likelihood function
Descriptive statistics
hypotheses
26. Describes the spread in the values of the sample statistic when many samples are taken.
The Mean of a random variable
Variability
categorical variables
observational study
27. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
applied statistics
descriptive statistics
Mutual independence
Random variables
28. 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
hypothesis
Independence or Statistical independence
29. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Estimator
Variable
Credence
Residuals
30. Probability of accepting a false null hypothesis.
experimental studies and observational studies.
Beta value
That value is the median value
That is the median value
31. In particular - the pdf of the standard normal distribution is denoted by
Posterior probability
Conditional probability
Skewness
f(z) - and its cdf by F(z).
32. Is a sample and the associated data points.
Mutual independence
A data set
The standard deviation
Prior probability
33. 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
An experimental study
Valid measure
nominal - ordinal - interval - and ratio
Step 3 of a statistical experiment
34. 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
Probability density functions
Step 1 of a statistical experiment
Ratio measurements
nominal - ordinal - interval - and ratio
35. A group of individuals sharing some common features that might affect the treatment.
Statistics
the population cumulants
Kurtosis
Block
36. Some commonly used symbols for sample statistics
Variability
the population cumulants
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A data set
37. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
A statistic
experimental studies and observational studies.
The standard deviation
A Random vector
38. Is the length of the smallest interval which contains all the data.
Observational study
The Range
Simpson's Paradox
Estimator
39. 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
Conditional distribution
Step 1 of a statistical experiment
experimental studies and observational studies.
Descriptive
40. Is that part of a population which is actually observed.
A sample
P-value
Correlation coefficient
A random variable
41. 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
A statistic
Skewness
Inferential statistics
That is the median value
42. Are simply two different terms for the same thing. Add the given values
Type I errors & Type II errors
Average and arithmetic mean
Independent Selection
Block
43. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Kurtosis
An estimate of a parameter
Probability density
Sampling Distribution
44. Is defined as the expected value of random variable (X -
Particular realizations of a random variable
The Covariance between two random variables X and Y - with expected values E(X) =
Step 2 of a statistical experiment
quantitative variables
45. (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
Ordinal measurements
Simpson's Paradox
hypotheses
The Expected value
46. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)
Random variables
Law of Large Numbers
Interval measurements
Marginal probability
47. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Independence or Statistical independence
Probability density
Sample space
48. 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.
Probability and statistics
A random variable
The median value
The average - or arithmetic mean
49. 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).
The median value
Skewness
An event
The variance of a random variable
50. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
A likelihood function
Variability
Pairwise independence
Correlation coefficient