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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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clep
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math
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. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
That is the median value
Atomic event
Probability and statistics
Joint distribution
2. Two variables such that their effects on the response variable cannot be distinguished from each other.
Joint distribution
Confounded variables
Lurking variable
the population cumulants
3. Are usually written in upper case roman letters: X - Y - etc.
Random variables
Nominal measurements
Simple random sample
Law of Parsimony
4. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Binomial experiment
Random variables
Likert scale
Binary data
5. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i
Independence or Statistical independence
An experimental study
inferential statistics
Interval measurements
6. When there is an even number of values...
That is the median value
Seasonal effect
Estimator
Placebo effect
7. (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
Statistical adjustment
A likelihood function
observational study
P-value
8. 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).
Quantitative variable
Outlier
Skewness
An event
9. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Law of Large Numbers
Posterior probability
Qualitative variable
The Mean of a random variable
10. 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
Bias
Inferential
Trend
11. 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
Coefficient of determination
quantitative variables
The average - or arithmetic mean
12. Probability of rejecting a true null hypothesis.
Variability
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Skewness
Alpha value (Level of Significance)
13. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
The Mean of a random variable
descriptive statistics
Pairwise independence
f(z) - and its cdf by F(z).
14. A numerical measure that describes an aspect of a population.
The Expected value
Statistical adjustment
Parameter
Placebo effect
15. 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.
Seasonal effect
Power of a test
Variability
Average and arithmetic mean
16. ?r
the population cumulants
Correlation coefficient
Individual
Pairwise independence
17. 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
The Range
A Probability measure
Skewness
Sampling Distribution
18. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
An event
Law of Large Numbers
Nominal measurements
Valid measure
19. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Binomial experiment
Interval measurements
Statistical inference
20. Many statistical methods seek to minimize the mean-squared error - and these are called
Binomial experiment
methods of least squares
the population variance
The average - or arithmetic mean
21. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
The standard deviation
Step 3 of a statistical experiment
The sample space
22. 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
A random variable
An experimental study
experimental studies and observational studies.
23. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
descriptive statistics
Type 1 Error
A population or statistical population
quantitative variables
24. Cov[X - Y] :
methods of least squares
Type 1 Error
covariance of X and Y
Treatment
25. A variable describes an individual by placing the individual into a category or a group.
The Mean of a random variable
Credence
Qualitative variable
Statistical adjustment
26. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are
Statistic
A Probability measure
Posterior probability
Descriptive statistics
27. 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 data point
The standard deviation
A Probability measure
hypotheses
28. 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 sample or population mean
Statistical inference
A data set
Inferential statistics
29. When you have two or more competing models - choose the simpler of the two models.
A probability distribution
Block
Simpson's Paradox
Law of Parsimony
30. 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
Particular realizations of a random variable
Parameter
Probability and statistics
The Covariance between two random variables X and Y - with expected values E(X) =
31. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Valid measure
Sampling
Mutual independence
Prior probability
32. ?
That is the median value
the population correlation
variance of X
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
33. Is a sample space over which a probability measure has been defined.
Dependent Selection
Residuals
A probability space
Divide the sum by the number of values.
34. In particular - the pdf of the standard normal distribution is denoted by
Conditional distribution
f(z) - and its cdf by F(z).
A random variable
A likelihood function
35. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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36. A measure that is relevant or appropriate as a representation of that property.
Count data
Valid measure
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A sampling distribution
37. 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 1 of a statistical experiment
Likert scale
Step 2 of a statistical experiment
Binomial experiment
38. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Dependent Selection
Parameter - or 'statistical parameter'
A Probability measure
Sampling Distribution
39. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Divide the sum by the number of values.
A probability distribution
Interval measurements
Block
40. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
The Range
Conditional distribution
An event
41. A subjective estimate of probability.
Lurking variable
Experimental and observational studies
variance of X
Credence
42. The proportion of the explained variation by a linear regression model in the total variation.
That is the median value
Descriptive
The standard deviation
Coefficient of determination
43. Gives the probability distribution for a continuous random variable.
The Covariance between two random variables X and Y - with expected values E(X) =
Type I errors
A probability density function
Pairwise independence
44. 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.
A likelihood function
descriptive statistics
Marginal probability
Likert scale
45. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
nominal - ordinal - interval - and ratio
Estimator
Standard error
An event
46. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Step 3 of a statistical experiment
Inferential
Conditional probability
Likert scale
47. Is data arising from counting that can take only non-negative integer values.
Probability density
Count data
A Probability measure
A data set
48. S^2
A population or statistical population
the population variance
Correlation coefficient
A probability distribution
49. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
A Distribution function
Variability
Binomial experiment
Ratio measurements
50. Failing to reject a false null hypothesis.
Probability density functions
Statistics
Type 2 Error
Confounded variables