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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. S^2
expected value of X
Correlation coefficient
descriptive statistics
the population variance
2. A subjective estimate of probability.
Type I errors & Type II errors
Kurtosis
Credence
the population cumulants
3. Where the null hypothesis is falsely rejected giving a 'false positive'.
the population mean
Greek letters
Type I errors
Atomic event
4. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
An experimental study
applied statistics
Beta value
Posterior probability
5. 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}.
The sample space
Parameter - or 'statistical parameter'
covariance of X and Y
observational study
6. 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.
Ratio measurements
A Distribution function
Probability density
Likert scale
7. 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
Conditional probability
inferential statistics
Marginal distribution
quantitative variables
8. 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.
Block
Lurking variable
Type 2 Error
The variance of a random variable
9. When you have two or more competing models - choose the simpler of the two models.
P-value
f(z) - and its cdf by F(z).
Law of Parsimony
hypothesis
10. 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
The Mean of a random variable
Probability density
Joint distribution
A Random vector
11. 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.
Qualitative variable
An estimate of a parameter
That value is the median value
Independence or Statistical independence
12. Cov[X - Y] :
The average - or arithmetic mean
covariance of X and Y
Greek letters
The Mean of a random variable
13. 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.
variance of X
Ordinal measurements
Skewness
The variance of a random variable
14. 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
Sample space
Nominal measurements
Dependent Selection
Step 3 of a statistical experiment
15. Of a group of numbers is the center point of all those number values.
Pairwise independence
the population cumulants
The average - or arithmetic mean
A population or statistical population
16. 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.
Statistics
the population correlation
f(z) - and its cdf by F(z).
Probability density functions
17. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Average and arithmetic mean
Particular realizations of a random variable
Variable
An estimate of a parameter
18. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
A Random vector
Inferential statistics
Conditional probability
19. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
methods of least squares
inferential statistics
Probability density functions
Joint distribution
20. A numerical measure that assesses the strength of a linear relationship between two variables.
Joint distribution
Experimental and observational studies
Correlation coefficient
Parameter - or 'statistical parameter'
21. Is data that can take only two values - usually represented by 0 and 1.
Placebo effect
Marginal distribution
Binary data
Skewness
22. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Binomial experiment
expected value of X
Step 3 of a statistical experiment
Residuals
23. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.
Dependent Selection
f(z) - and its cdf by F(z).
hypothesis
Divide the sum by the number of values.
24. (cdfs) are denoted by upper case letters - e.g. F(x).
A population or statistical population
Cumulative distribution functions
An estimate of a parameter
That is the median value
25. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
The average - or arithmetic mean
Type II errors
Statistical adjustment
The standard deviation
26. 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
the population cumulants
A Statistical parameter
Power of a test
Ratio measurements
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
A sampling distribution
nominal - ordinal - interval - and ratio
Nominal measurements
Null hypothesis
28. 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)
Interval measurements
the population cumulants
Correlation coefficient
Seasonal effect
29. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
nominal - ordinal - interval - and ratio
Prior probability
experimental studies and observational studies.
Law of Large Numbers
30. 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.
s-algebras
The Mean of a random variable
Interval measurements
Kurtosis
31. 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.
Block
The median value
Step 1 of a statistical experiment
the sample or population mean
32. 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
Credence
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Inferential statistics
The Covariance between two random variables X and Y - with expected values E(X) =
33. 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.
Mutual independence
Nominal measurements
Marginal probability
hypothesis
34. A measure that is relevant or appropriate as a representation of that property.
That value is the median value
the population correlation
observational study
Valid measure
35. Is defined as the expected value of random variable (X -
Step 3 of a statistical experiment
A probability distribution
The Covariance between two random variables X and Y - with expected values E(X) =
An experimental study
36. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Variability
The variance of a random variable
Step 2 of a statistical experiment
A statistic
37. 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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38. A numerical measure that describes an aspect of a population.
Lurking variable
The sample space
The Expected value
Parameter
39. 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).
P-value
A random variable
Statistical inference
Joint probability
40. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Bias
Inferential
Statistical adjustment
Null hypothesis
41. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Step 1 of a statistical experiment
Greek letters
A Probability measure
Statistical inference
42. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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43. (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
A likelihood function
Law of Parsimony
Nominal measurements
Power of a test
44. The proportion of the explained variation by a linear regression model in the total variation.
A Probability measure
Coefficient of determination
A probability distribution
P-value
45. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
Descriptive statistics
Null hypothesis
Dependent Selection
46. 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
Step 2 of a statistical experiment
Ordinal measurements
A Random vector
Variability
47. Gives the probability distribution for a continuous random variable.
A probability density function
Parameter - or 'statistical parameter'
Dependent Selection
Conditional probability
48. A data value that falls outside the overall pattern of the graph.
Parameter - or 'statistical parameter'
Outlier
Sample space
A Random vector
49. Is a sample and the associated data points.
applied statistics
A data set
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Posterior probability
50. A variable describes an individual by placing the individual into a category or a group.
Standard error
Beta value
Kurtosis
Qualitative variable