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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
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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. (cdfs) are denoted by upper case letters - e.g. F(x).
Nominal measurements
Standard error
Outlier
Cumulative distribution functions
2. A numerical facsimilie or representation of a real-world phenomenon.
Seasonal effect
Variability
Probability
Simulation
3. S^2
That is the median value
Individual
the population correlation
the population variance
4. 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.
A Statistical parameter
The sample space
Credence
Dependent Selection
5. Is a sample and the associated data points.
An estimate of a parameter
the population mean
A data set
applied statistics
6. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
The Covariance between two random variables X and Y - with expected values E(X) =
Sample space
Experimental and observational studies
7. Is the length of the smallest interval which contains all the data.
The Range
An experimental study
Inferential statistics
s-algebras
8. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Mutual independence
A probability space
Probability density functions
Placebo effect
9. 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 data point
Standard error
A Distribution function
Lurking variable
10. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
A Probability measure
Sample space
nominal - ordinal - interval - and ratio
Type 1 Error
11. Is a sample space over which a probability measure has been defined.
Power of a test
Confounded variables
A probability space
Posterior probability
12. Is data that can take only two values - usually represented by 0 and 1.
Type I errors & Type II errors
The average - or arithmetic mean
Binary data
The sample space
13. 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
variance of X
Sample space
Step 3 of a statistical experiment
Ratio measurements
14. ?
Simulation
the population correlation
Inferential statistics
A Random vector
15. 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.
Greek letters
Estimator
Step 1 of a statistical experiment
Probability density
16. Some commonly used symbols for population parameters
A probability distribution
Posterior probability
the population mean
Probability density functions
17. 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).
Joint probability
Statistic
variance of X
The Mean of a random variable
18. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Statistical dispersion
Treatment
Statistics
Random variables
19. A measurement such that the random error is small
Joint probability
Reliable measure
A random variable
Binomial experiment
20. 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).
An event
expected value of X
methods of least squares
That is the median value
21. A data value that falls outside the overall pattern of the graph.
Sample space
Credence
Outlier
Bias
22. 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
Descriptive statistics
Nominal measurements
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Correlation
23. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.
Statistics
An Elementary event
A Distribution function
Marginal probability
24. Probability of rejecting a true null hypothesis.
Alpha value (Level of Significance)
Sample space
A data point
the population mean
25. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
The variance of a random variable
Marginal distribution
Joint distribution
26. 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}.
A Distribution function
Parameter
The sample space
The Covariance between two random variables X and Y - with expected values E(X) =
27. 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
Outlier
Interval measurements
applied statistics
28. 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.
Coefficient of determination
Marginal probability
Simulation
The average - or arithmetic mean
29. Probability of accepting a false null hypothesis.
Valid measure
Independence or Statistical independence
Beta value
Posterior probability
30. Describes a characteristic of an individual to be measured or observed.
Simple random sample
Variable
Marginal distribution
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
31. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
An estimate of a parameter
Independence or Statistical independence
inferential statistics
The Range
32. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
s-algebras
Conditional probability
Standard error
Marginal probability
33. Is its expected value. The mean (or sample mean of a data set is just the average value.
Seasonal effect
The Mean of a random variable
Trend
Probability and statistics
34. Is the exact middle value of a set of numbers Arrange the numbers in numerical order. Find the value in the middle of the list.
Alpha value (Level of Significance)
Simple random sample
The median value
Variable
35. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
Inferential statistics
A population or statistical population
Valid measure
36. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.
Interval measurements
An Elementary event
Cumulative distribution functions
Bias
37. The collection of all possible outcomes in an experiment.
Sample space
P-value
Likert scale
Confounded variables
38. A numerical measure that assesses the strength of a linear relationship between two variables.
A likelihood function
expected value of X
Correlation coefficient
Type I errors
39. 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.
Independent Selection
Bias
Simple random sample
Placebo effect
40. 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
Valid measure
Conditional probability
Inferential statistics
An estimate of a parameter
41. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Cumulative distribution functions
Count data
A probability space
Statistical adjustment
42. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Power of a test
Prior probability
Probability density
hypothesis
43. 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.
Confounded variables
Kurtosis
Null hypothesis
Placebo effect
44. 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'
Independent Selection
Type II errors
Variability
Conditional probability
45. In particular - the pdf of the standard normal distribution is denoted by
f(z) - and its cdf by F(z).
Step 1 of a statistical experiment
Type 1 Error
Average and arithmetic mean
46. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
the population correlation
An experimental study
Mutual independence
Binomial experiment
47. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.
The Covariance between two random variables X and Y - with expected values E(X) =
A population or statistical population
Reliable measure
Variable
48. Var[X] :
Outlier
Nominal measurements
variance of X
Skewness
49. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
Atomic event
Sampling frame
Statistical dispersion
50. The standard deviation of a sampling distribution.
Conditional probability
methods of least squares
Standard error
Posterior probability