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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. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Dependent Selection
Simple random sample
A Random vector
descriptive statistics
2. 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
A likelihood function
Type I errors
A probability space
3. Is defined as the expected value of random variable (X -
Marginal probability
The Covariance between two random variables X and Y - with expected values E(X) =
Placebo effect
experimental studies and observational studies.
4. A list of individuals from which the sample is actually selected.
P-value
An experimental study
the population mean
Sampling frame
5. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe
Confounded variables
Sampling
Skewness
Simple random sample
6. Are usually written in upper case roman letters: X - Y - etc.
the population mean
Random variables
That is the median value
Bias
7. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
Beta value
Type I errors & Type II errors
The standard deviation
8. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.
Credence
The average - or arithmetic mean
A sampling distribution
An experimental study
9. A numerical measure that describes an aspect of a sample.
A Probability measure
P-value
Correlation coefficient
Statistic
10. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Atomic event
Step 2 of a statistical experiment
11. 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
Ratio measurements
Probability density
the population mean
Beta value
12. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Statistical dispersion
expected value of X
Type II errors
Seasonal effect
13. 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.
A data set
Statistical adjustment
An experimental study
Lurking variable
14. 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
Sampling
hypotheses
Coefficient of determination
Bias
15.
Likert scale
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
the population mean
Nominal measurements
16. 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.
applied statistics
Statistics
the population correlation
Estimator
17. Two variables such that their effects on the response variable cannot be distinguished from each other.
expected value of X
A probability distribution
Confounded variables
the population mean
18. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Kurtosis
Independent Selection
Posterior probability
A data set
19. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Probability density functions
Independence or Statistical independence
the population correlation
The Mean of a random variable
20. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Qualitative variable
inferential statistics
Law of Large Numbers
Divide the sum by the number of values.
21. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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22. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Residuals
Beta value
Null hypothesis
A data set
23. 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.
Alpha value (Level of Significance)
Independent Selection
Probability
descriptive statistics
24. ?
descriptive statistics
Sampling Distribution
the population correlation
Binary data
25. 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
Nominal measurements
inferential statistics
Marginal probability
Probability and statistics
26. ?r
Descriptive
the population cumulants
the sample or population mean
Power of a test
27. A numerical measure that assesses the strength of a linear relationship between two variables.
hypothesis
Simulation
Correlation coefficient
f(z) - and its cdf by F(z).
28. 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.
Law of Large Numbers
Simpson's Paradox
Bias
Variable
29. 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
Nominal measurements
Correlation
Sampling frame
Step 1 of a statistical experiment
30. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Particular realizations of a random variable
Ratio measurements
Ordinal measurements
Joint probability
31. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that
hypothesis
An event
Law of Large Numbers
A probability density function
32. 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}.
A sampling distribution
hypothesis
The sample space
Statistical dispersion
33. Many statistical methods seek to minimize the mean-squared error - and these are called
experimental studies and observational studies.
covariance of X and Y
That value is the median value
methods of least squares
34. Is the probability distribution - under repeated sampling of the population - of a given statistic.
A sampling distribution
A Distribution function
The median value
P-value
35. Is that part of a population which is actually observed.
Divide the sum by the number of values.
A sample
A statistic
Pairwise independence
36. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Particular realizations of a random variable
Sampling frame
37. Is data that can take only two values - usually represented by 0 and 1.
Binary data
Seasonal effect
A sampling distribution
Skewness
38. 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.
That value is the median value
A Distribution function
Binomial experiment
the population mean
39. 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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40. 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
Mutual independence
Reliable measure
Ratio measurements
Statistical adjustment
41. 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
The standard deviation
hypothesis
covariance of X and Y
42. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
A likelihood function
An Elementary event
Statistical adjustment
Statistics
43. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Cumulative distribution functions
Law of Large Numbers
Joint distribution
experimental studies and observational studies.
44. Probability of accepting a false null hypothesis.
Skewness
Coefficient of determination
The sample space
Beta value
45. Have imprecise differences between consecutive values - but have a meaningful order to those values
Atomic event
The Covariance between two random variables X and Y - with expected values E(X) =
A sample
Ordinal measurements
46. When there is an even number of values...
expected value of X
That is the median value
The Covariance between two random variables X and Y - with expected values E(X) =
P-value
47. 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).
Law of Large Numbers
Joint probability
Type 1 Error
Individual
48. 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.
That value is the median value
Sampling Distribution
Credence
An event
49. Probability of rejecting a true null hypothesis.
The average - or arithmetic mean
Coefficient of determination
Type 2 Error
Alpha value (Level of Significance)
50. 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.
Ordinal measurements
Dependent Selection
Atomic event
Statistical inference