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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
Start Test
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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
.
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. When you have two or more competing models - choose the simpler of the two models.
observational study
Credence
A likelihood function
Law of Parsimony
2. When there is an even number of values...
Sample space
Interval measurements
Coefficient of determination
That is the median value
3. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o
Marginal probability
Observational study
The Covariance between two random variables X and Y - with expected values E(X) =
the population variance
4. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
s-algebras
Law of Large Numbers
A data point
Ordinal measurements
5. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A statistic
Valid measure
Atomic event
A Random vector
6. Is a parameter that indexes a family of probability distributions.
A Random vector
A Statistical parameter
Statistic
A data set
7. 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'
Probability density
Conditional probability
Null hypothesis
The Mean of a random variable
8. ?r
Random variables
Statistic
covariance of X and Y
the population cumulants
9. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
Prior probability
That value is the median value
Variable
10. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Divide the sum by the number of values.
Sampling
Sampling Distribution
Variability
11. E[X] :
expected value of X
Binomial experiment
Count data
Atomic event
12. (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
Null hypothesis
Estimator
Posterior probability
A likelihood function
13. Rejecting a true null hypothesis.
Type 1 Error
Independent Selection
Simulation
Posterior probability
14. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Pairwise independence
Joint probability
Quantitative variable
Cumulative distribution functions
15. 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
Probability and statistics
Variability
Inferential statistics
Statistics
16. 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.
Parameter
covariance of X and Y
Standard error
Independent Selection
17. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Average and arithmetic mean
Divide the sum by the number of values.
nominal - ordinal - interval - and ratio
Bias
18. Gives the probability distribution for a continuous random variable.
Type I errors
An estimate of a parameter
Block
A probability density function
19. 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
A Statistical parameter
A data set
A probability space
20. Is data that can take only two values - usually represented by 0 and 1.
Cumulative distribution functions
Binary data
A data set
Statistic
21. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Joint distribution
Dependent Selection
Ordinal measurements
Particular realizations of a random variable
22. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
the population variance
A data set
An estimate of a parameter
Marginal probability
23. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
Conditional distribution
Sampling frame
Lurking variable
24. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
A statistic
Joint distribution
Simple random sample
25. Describes a characteristic of an individual to be measured or observed.
Step 1 of a statistical experiment
Variable
Standard error
the population mean
26. The proportion of the explained variation by a linear regression model in the total variation.
Coefficient of determination
That value is the median value
A probability distribution
Sampling frame
27. 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).
The Covariance between two random variables X and Y - with expected values E(X) =
Outlier
An event
quantitative variables
28. 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)
A probability space
Interval measurements
An estimate of a parameter
Average and arithmetic mean
29. 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
Correlation
Variability
Treatment
hypotheses
30. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
the sample or population mean
Standard error
descriptive statistics
categorical variables
31. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
hypotheses
categorical variables
quantitative variables
Type II errors
32. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
A data point
Dependent Selection
Statistic
The standard deviation
33. Long-term upward or downward movement over time.
Trend
hypotheses
Bias
A random variable
34. Is denoted by - pronounced 'x bar'.
An event
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Sampling Distribution
Parameter - or 'statistical parameter'
35. Where the null hypothesis is falsely rejected giving a 'false positive'.
That value is the median value
Type I errors
Null hypothesis
Sample space
36. (cdfs) are denoted by upper case letters - e.g. F(x).
A sample
Divide the sum by the number of values.
Step 3 of a statistical experiment
Cumulative distribution functions
37. 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.
Law of Parsimony
The Covariance between two random variables X and Y - with expected values E(X) =
Statistical inference
Estimator
38. 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
The sample space
hypothesis
Coefficient of determination
Likert scale
39. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
Residuals
Law of Parsimony
Simulation
40. A measure that is relevant or appropriate as a representation of that property.
Correlation coefficient
Count data
Valid measure
Average and arithmetic mean
41. 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
Step 3 of a statistical experiment
hypotheses
Independent Selection
Experimental and observational studies
42. Gives the probability of events in a probability space.
An event
A Probability measure
variance of X
Qualitative variable
43. Any specific experimental condition applied to the subjects
Variability
A probability distribution
Experimental and observational studies
Treatment
44. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Inferential
Descriptive
A Distribution function
Ordinal measurements
45. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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46. The collection of all possible outcomes in an experiment.
Descriptive statistics
An event
Ordinal measurements
Sample space
47. A numerical measure that describes an aspect of a population.
the population mean
Power of a test
Parameter
inferential statistics
48. 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
Binomial experiment
Variability
the population cumulants
49. 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.
Average and arithmetic mean
Greek letters
Seasonal effect
Credence
50. 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
A statistic
An experimental study
Likert scale
Step 1 of a statistical experiment