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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. Working from a null hypothesis two basic forms of error are recognized:
Count data
Lurking variable
Type I errors & Type II errors
A data set
2. 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
Pairwise independence
Inferential statistics
A Statistical parameter
Type 2 Error
3. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
Lurking variable
Law of Parsimony
Step 1 of a statistical experiment
4. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
The standard deviation
Cumulative distribution functions
Valid measure
A population or statistical population
5. S^2
the population variance
Type 2 Error
Simple random sample
Type I errors
6. 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
The variance of a random variable
Ratio measurements
The Expected value
hypotheses
7. 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).
A probability distribution
An event
Likert scale
descriptive statistics
8. Have no meaningful rank order among values.
Bias
Reliable measure
Nominal measurements
Descriptive
9. 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.
Null hypothesis
Dependent Selection
Probability and statistics
Simulation
10. Is a sample space over which a probability measure has been defined.
Correlation coefficient
A probability space
Observational study
Statistical inference
11. A numerical measure that assesses the strength of a linear relationship between two variables.
Standard error
Correlation coefficient
Parameter - or 'statistical parameter'
Kurtosis
12. The collection of all possible outcomes in an experiment.
Particular realizations of a random variable
The Mean of a random variable
An event
Sample space
13. Gives the probability distribution for a continuous random variable.
A probability density function
The average - or arithmetic mean
An Elementary event
That value is the median value
14. 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.
Estimator
Count data
Alpha value (Level of Significance)
The Mean of a random variable
15. 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.
Seasonal effect
Joint distribution
A data set
A random variable
16. E[X] :
Independent Selection
The standard deviation
expected value of X
The average - or arithmetic mean
17. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P
Correlation
descriptive statistics
Reliable measure
f(z) - and its cdf by F(z).
18. 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'
Conditional probability
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Alpha value (Level of Significance)
Correlation
19. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.
Step 2 of a statistical experiment
categorical variables
Marginal distribution
Dependent Selection
20. A measurement such that the random error is small
Law of Parsimony
Quantitative variable
Reliable measure
Nominal measurements
21. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
descriptive statistics
variance of X
Statistical adjustment
the sample or population mean
22. 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
Descriptive statistics
Law of Parsimony
Likert scale
Estimator
23. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Valid measure
A probability distribution
Posterior probability
Random variables
24. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Cumulative distribution functions
A random variable
Individual
Experimental and observational studies
25. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
Bias
An estimate of a parameter
Step 3 of a statistical experiment
26. 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.
Kurtosis
Valid measure
Divide the sum by the number of values.
Independent Selection
27. Var[X] :
variance of X
Conditional probability
Block
Alpha value (Level of Significance)
28. 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
Sampling Distribution
Binomial experiment
Skewness
Independence or Statistical independence
29. Are simply two different terms for the same thing. Add the given values
Qualitative variable
Beta value
Average and arithmetic mean
Nominal measurements
30. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Random variables
Sample space
Sampling Distribution
That is the median value
31. Rejecting a true null hypothesis.
Statistical adjustment
A Distribution function
Type 1 Error
f(z) - and its cdf by F(z).
32. 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.
Confounded variables
Lurking variable
The Expected value
Quantitative variable
33. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies
experimental studies and observational studies.
Step 2 of a statistical experiment
the population mean
Law of Parsimony
34. A measure that is relevant or appropriate as a representation of that property.
Valid measure
covariance of X and Y
The average - or arithmetic mean
Descriptive statistics
35. Failing to reject a false null hypothesis.
Count data
Type 2 Error
Pairwise independence
The Range
36. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
Particular realizations of a random variable
A likelihood function
Simple random sample
s-algebras
37. Many statistical methods seek to minimize the mean-squared error - and these are called
f(z) - and its cdf by F(z).
Average and arithmetic mean
methods of least squares
Type I errors
38. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Inferential statistics
Probability density
The median value
39. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
That is the median value
the sample or population mean
nominal - ordinal - interval - and ratio
Independence or Statistical independence
40. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
The Expected value
The Covariance between two random variables X and Y - with expected values E(X) =
Power of a test
Greek letters
41. ?r
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Type 2 Error
Divide the sum by the number of values.
the population cumulants
42. Is data arising from counting that can take only non-negative integer values.
Residuals
Quantitative variable
Count data
Sampling
43. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Valid measure
Conditional distribution
Likert scale
Quantitative variable
44. ?
the population correlation
Probability density
Treatment
A statistic
45. A variable describes an individual by placing the individual into a category or a group.
Law of Parsimony
Qualitative variable
Step 1 of a statistical experiment
nominal - ordinal - interval - and ratio
46. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
applied statistics
Simulation
Placebo effect
Parameter
47. 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
Type I errors & Type II errors
The Covariance between two random variables X and Y - with expected values E(X) =
Type II errors
Ratio measurements
48. 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.
Statistical inference
Trend
Simpson's Paradox
Standard error
49. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.
An Elementary event
expected value of X
Trend
Inferential statistics
50. 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
Type II errors
Statistical adjustment
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Step 3 of a statistical experiment