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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.
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. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
s-algebras
That value is the median value
Particular realizations of a random variable
2. A numerical measure that assesses the strength of a linear relationship between two variables.
the population cumulants
applied statistics
experimental studies and observational studies.
Correlation coefficient
3. 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.
experimental studies and observational studies.
An experimental study
Particular realizations of a random variable
Beta value
4. 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
quantitative variables
hypothesis
Skewness
P-value
5. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Probability and statistics
Simulation
Placebo effect
applied statistics
6. Is a sample space over which a probability measure has been defined.
A Random vector
Alpha value (Level of Significance)
Simple random sample
A probability space
7. The collection of all possible outcomes in an experiment.
Sample space
Joint distribution
Probability and statistics
Prior probability
8. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
the population variance
Likert scale
Correlation
Reliable measure
9. Some commonly used symbols for sample statistics
Type II errors
Qualitative variable
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Valid measure
10. Is the length of the smallest interval which contains all the data.
applied statistics
The Range
Marginal distribution
A statistic
11. Any specific experimental condition applied to the subjects
Treatment
An experimental study
The Range
Ordinal measurements
12. 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.
The Expected value
Dependent Selection
Bias
Reliable measure
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.
Inferential statistics
An experimental study
Law of Parsimony
Lurking variable
14. (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
Independent Selection
Beta value
A statistic
15. Describes the spread in the values of the sample statistic when many samples are taken.
Joint probability
Variability
Sampling Distribution
Type 1 Error
16. Probability of accepting a false null hypothesis.
Credence
Joint distribution
Independent Selection
Beta value
17. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Type 2 Error
The Mean of a random variable
Probability density
Placebo effect
18. Is a sample and the associated data points.
The median value
A data set
Binary data
Simple random sample
19. When you have two or more competing models - choose the simpler of the two models.
Count data
Law of Parsimony
nominal - ordinal - interval - and ratio
An Elementary event
20. The probability of correctly detecting a false null hypothesis.
Power of a test
Law of Large Numbers
hypothesis
A probability density function
21. S^2
Placebo effect
the population variance
Type I errors & Type II errors
A likelihood function
22. A variable describes an individual by placing the individual into a category or a group.
The Covariance between two random variables X and Y - with expected values E(X) =
Statistic
nominal - ordinal - interval - and ratio
Qualitative variable
23. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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24. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
Inferential statistics
P-value
Divide the sum by the number of values.
The standard deviation
25. 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)
Reliable measure
Interval measurements
Ratio measurements
quantitative variables
26. In particular - the pdf of the standard normal distribution is denoted by
f(z) - and its cdf by F(z).
A likelihood function
Confounded variables
Likert scale
27. ?
Power of a test
the population correlation
A statistic
P-value
28. 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
Binary data
methods of least squares
Alpha value (Level of Significance)
29. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
A sampling distribution
Descriptive
Inferential
s-algebras
30. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
methods of least squares
Kurtosis
Statistical inference
31. 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.
The variance of a random variable
Bias
Independent Selection
A Distribution function
32. 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).
Alpha value (Level of Significance)
Treatment
Joint probability
Average and arithmetic mean
33. Is data arising from counting that can take only non-negative integer values.
Parameter - or 'statistical parameter'
Observational study
Count data
Law of Large Numbers
34. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Cumulative distribution functions
Individual
The average - or arithmetic mean
Quantitative variable
35. 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'
Statistical adjustment
Power of a test
Conditional probability
A Probability measure
36. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
quantitative variables
Ordinal measurements
Ratio measurements
An estimate of a parameter
37. 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
Interval measurements
Inferential statistics
Standard error
Probability
38. The standard deviation of a sampling distribution.
Inferential statistics
Standard error
A population or statistical population
Experimental and observational studies
39. Is that part of a population which is actually observed.
A sample
Likert scale
Conditional probability
Posterior probability
40. 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
Beta value
Average and arithmetic mean
Descriptive statistics
41. Is its expected value. The mean (or sample mean of a data set is just the average value.
Sampling Distribution
Simple random sample
A probability distribution
The Mean of a random variable
42. A data value that falls outside the overall pattern of the graph.
A probability density function
The Range
A sampling distribution
Outlier
43. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
The average - or arithmetic mean
P-value
Conditional probability
44. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
hypothesis
A Random vector
Pairwise independence
Reliable measure
45. Describes a characteristic of an individual to be measured or observed.
variance of X
Alpha value (Level of Significance)
experimental studies and observational studies.
Variable
46. Working from a null hypothesis two basic forms of error are recognized:
Binomial experiment
Law of Parsimony
Type I errors & Type II errors
experimental studies and observational studies.
47. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Atomic event
the population mean
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Statistical dispersion
48. 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
Step 1 of a statistical experiment
Bias
Statistical dispersion
Descriptive
49. 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
Descriptive
A Random vector
Reliable measure
50. Cov[X - Y] :
Null hypothesis
Posterior probability
covariance of X and Y
Ordinal measurements