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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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clep
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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. The standard deviation of a sampling distribution.
Sample space
Binomial experiment
A population or statistical population
Standard error
2. Another name for elementary event.
Atomic event
A data point
Average and arithmetic mean
Probability
3. 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.
P-value
Independent Selection
Type I errors & Type II errors
The Mean of a random variable
4. To find the average - or arithmetic mean - of a set of numbers:
Law of Parsimony
Block
A sample
Divide the sum by the number of values.
5. Is data arising from counting that can take only non-negative integer values.
the sample or population mean
Count data
Variable
Block
6. 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
Mutual independence
Descriptive
A sample
Conditional distribution
7. The proportion of the explained variation by a linear regression model in the total variation.
Type I errors
Coefficient of determination
Ordinal measurements
experimental studies and observational studies.
8. 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
Ratio measurements
Binomial experiment
Correlation
Parameter
9. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
Outlier
Law of Parsimony
Atomic event
10. A data value that falls outside the overall pattern of the graph.
Standard error
Outlier
Simpson's Paradox
The sample space
11. Gives the probability of events in a probability space.
A Probability measure
Ratio measurements
Step 2 of a statistical experiment
Variable
12. 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
Step 1 of a statistical experiment
Marginal distribution
Probability density
13. 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
A Random vector
Random variables
Step 3 of a statistical experiment
Conditional distribution
14. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
A population or statistical population
A data point
Count data
15. 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)
Trend
Step 3 of a statistical experiment
hypotheses
Interval measurements
16. 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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17. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'
The standard deviation
the population variance
Type I errors & Type II errors
Conditional probability
18. Is a sample and the associated data points.
expected value of X
A data set
Sampling Distribution
An event
19. 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.
Estimator
Statistical inference
Reliable measure
Particular realizations of a random variable
20. Var[X] :
variance of X
Average and arithmetic mean
The standard deviation
f(z) - and its cdf by F(z).
21. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
A statistic
Descriptive
Ratio measurements
A data point
22. E[X] :
Descriptive
expected value of X
Interval measurements
Cumulative distribution functions
23. Is denoted by - pronounced 'x bar'.
Atomic event
s-algebras
A population or statistical population
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
24. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Type II errors
Residuals
experimental studies and observational studies.
Statistical adjustment
25. (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
Nominal measurements
Binary data
Parameter
A likelihood function
26. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Dependent Selection
Parameter - or 'statistical parameter'
Standard error
Type II errors
27. Is the length of the smallest interval which contains all the data.
Probability
The Range
the population variance
Marginal probability
28. 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.
Confounded variables
Kurtosis
Estimator
The variance of a random variable
29. 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.
experimental studies and observational studies.
Binary data
Simple random sample
The Expected value
30. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
Cumulative distribution functions
Descriptive statistics
Marginal probability
31. Failing to reject a false null hypothesis.
Count data
A statistic
hypothesis
Type 2 Error
32. 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
A Random vector
Type II errors
Nominal measurements
33. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
applied statistics
Mutual independence
Sample space
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
34. S^2
Particular realizations of a random variable
Null hypothesis
The Expected value
the population variance
35. A group of individuals sharing some common features that might affect the treatment.
Sampling Distribution
Bias
Block
Valid measure
36. Cov[X - Y] :
Credence
The median value
covariance of X and Y
Step 3 of a statistical experiment
37. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Law of Large Numbers
A Distribution function
quantitative variables
f(z) - and its cdf by F(z).
38. Is defined as the expected value of random variable (X -
methods of least squares
Lurking variable
The Covariance between two random variables X and Y - with expected values E(X) =
Type I errors
39. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Reliable measure
Statistical dispersion
The standard deviation
Ratio measurements
40. Some commonly used symbols for population parameters
Variable
Ratio measurements
Statistics
the population mean
41. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Type II errors
the sample or population mean
the population mean
Prior probability
42. (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
Divide the sum by the number of values.
Type 1 Error
The Expected value
Correlation coefficient
43. 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.
A likelihood function
The Mean of a random variable
Binary data
A random variable
44. 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).
Nominal measurements
Joint probability
Law of Large Numbers
Bias
45. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.
Quantitative variable
Sampling
expected value of X
observational study
46. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
s-algebras
Independent Selection
categorical variables
Ratio measurements
47. Have no meaningful rank order among values.
Estimator
the population mean
Binomial experiment
Nominal measurements
48. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Sampling
nominal - ordinal - interval - and ratio
The standard deviation
Statistical adjustment
49. 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
inferential statistics
Conditional distribution
A Probability measure
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
50. Long-term upward or downward movement over time.
Trend
hypotheses
The sample space
the population mean