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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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Instructions:
Answer 50 questions in 15 minutes.
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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. 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
A Statistical parameter
Probability and statistics
Cumulative distribution functions
Outlier
2. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
Random variables
Null hypothesis
Simple random sample
3. Have imprecise differences between consecutive values - but have a meaningful order to those values
Variability
Kurtosis
Ordinal measurements
Binomial experiment
4. Is a sample and the associated data points.
Experimental and observational studies
Ordinal measurements
An event
A data set
5. Describes the spread in the values of the sample statistic when many samples are taken.
quantitative variables
Variability
descriptive statistics
That value is the median value
6. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
the sample or population mean
the population variance
Law of Large Numbers
7. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Statistical adjustment
Greek letters
That value is the median value
8. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
quantitative variables
Type I errors
A probability distribution
the sample or population mean
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.
Simple random sample
A likelihood function
The Range
Credence
10. (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
Atomic event
A sampling distribution
Nominal measurements
A likelihood function
11. 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.
An experimental study
Law of Large Numbers
Statistical inference
Mutual independence
12. S^2
Type I errors & Type II errors
Coefficient of determination
Nominal measurements
the population variance
13. 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.
Count data
Bias
Mutual independence
The median value
14. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
s-algebras
hypothesis
Pairwise independence
Count data
15. Failing to reject a false null hypothesis.
Inferential
The Mean of a random variable
Type 2 Error
Particular realizations of a random variable
16. 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
Statistics
Step 3 of a statistical experiment
the sample or population mean
Mutual independence
17. Gives the probability distribution for a continuous random variable.
Binary data
Divide the sum by the number of values.
Descriptive
A probability density function
18. Is that part of a population which is actually observed.
Atomic event
Marginal probability
A random variable
A sample
19. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
observational study
An Elementary event
Block
20. A measure that is relevant or appropriate as a representation of that property.
Type I errors & Type II errors
Step 3 of a statistical experiment
Valid measure
Conditional probability
21. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistical adjustment
Correlation coefficient
A statistic
Simple random sample
22. ?r
Statistic
Valid measure
the population cumulants
Simple random sample
23. 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
Null hypothesis
Mutual independence
Seasonal effect
Interval measurements
24. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Sampling Distribution
Individual
Divide the sum by the number of values.
25. Can be a population parameter - a distribution parameter - an unobserved parameter (with different shades of meaning). In statistics - this is often a quantity to be estimated.
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26. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
the population variance
Prior probability
the population correlation
That is the median value
27. 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.
A Distribution function
Observational study
Bias
Prior probability
28. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
Statistical inference
Law of Parsimony
Dependent Selection
29. 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.
expected value of X
Step 1 of a statistical experiment
Trend
Kurtosis
30. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Sampling
Statistical dispersion
Simpson's Paradox
A data set
31. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
The Expected value
Pairwise independence
Parameter
A sampling distribution
32. 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.
Independence or Statistical independence
A random variable
Statistics
A Statistical parameter
33. Working from a null hypothesis two basic forms of error are recognized:
Treatment
Type I errors & Type II errors
Sample space
quantitative variables
34. Is denoted by - pronounced 'x bar'.
A sample
Mutual independence
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
the sample or population mean
35. Another name for elementary event.
the sample or population mean
Atomic event
A statistic
The standard deviation
36. 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
An event
Simulation
Prior probability
37. 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
Estimator
A Statistical parameter
Statistics
38. A subjective estimate of probability.
Sample space
The Covariance between two random variables X and Y - with expected values E(X) =
Credence
Particular realizations of a random variable
39. 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}.
Type 1 Error
The sample space
Statistical inference
The Expected value
40. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Null hypothesis
Law of Large Numbers
Correlation coefficient
Marginal probability
41. 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
hypothesis
Mutual independence
Variable
the population cumulants
42. 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
the population mean
Inferential statistics
nominal - ordinal - interval - and ratio
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
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).
Cumulative distribution functions
observational study
variance of X
Joint probability
44. 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.
Reliable measure
applied statistics
Lurking variable
A Random vector
45.
Particular realizations of a random variable
Confounded variables
the population mean
methods of least squares
46. The collection of all possible outcomes in an experiment.
Sample space
the population mean
Type I errors & Type II errors
Greek letters
47. 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.
Correlation coefficient
Type 1 Error
Step 1 of a statistical experiment
A population or statistical population
48. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
A likelihood function
A probability space
Conditional probability
49. Any specific experimental condition applied to the subjects
s-algebras
Skewness
Treatment
Binary data
50. Some commonly used symbols for sample statistics
Valid measure
Statistical adjustment
Residuals
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.