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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. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.
That value is the median value
Null hypothesis
Pairwise independence
Inferential statistics
2. A list of individuals from which the sample is actually selected.
Sampling frame
Correlation
Interval measurements
A sample
3. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors & Type II errors
That value is the median value
Type I errors
Seasonal effect
4. Some commonly used symbols for population parameters
Statistic
the population mean
The standard deviation
Statistical inference
5. 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.
A random variable
Alpha value (Level of Significance)
An event
Statistics
6. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Quantitative variable
That is the median value
The average - or arithmetic mean
applied statistics
7. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
descriptive statistics
Residuals
Correlation coefficient
Marginal probability
8. Is its expected value. The mean (or sample mean of a data set is just the average value.
A sample
The Mean of a random variable
A Statistical parameter
Type I errors & Type II errors
9. Data are gathered and correlations between predictors and response are investigated.
Atomic event
Qualitative variable
Type I errors & Type II errors
observational study
10. ?
Simulation
Joint probability
A statistic
the population correlation
11. Can be a population parameter - a distribution parameter - an unobserved parameter (with different shades of meaning). In statistics - this is often a quantity to be estimated.
12. Have no meaningful rank order among values.
Nominal measurements
Random variables
Probability and statistics
Correlation
13. 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'
Inferential statistics
Conditional probability
methods of least squares
Beta value
14. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
A probability distribution
the population cumulants
Kurtosis
The standard deviation
15. A measure that is relevant or appropriate as a representation of that property.
That is the median value
Valid measure
Residuals
Sampling frame
16. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
A Statistical parameter
Type 2 Error
Random variables
17. Describes a characteristic of an individual to be measured or observed.
The average - or arithmetic mean
Variable
Independent Selection
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
18. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
The Covariance between two random variables X and Y - with expected values E(X) =
The Expected value
Binomial experiment
A probability space
19. 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
The sample space
Dependent Selection
Step 3 of a statistical experiment
Average and arithmetic mean
20. Gives the probability of events in a probability space.
Posterior probability
A Probability measure
Parameter - or 'statistical parameter'
Reliable measure
21. 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
hypothesis
expected value of X
Interval measurements
Individual
22. Is data arising from counting that can take only non-negative integer values.
expected value of X
Count data
A sample
Nominal measurements
23. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Sample space
An experimental study
A sample
24. 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.
Marginal probability
Individual
A sampling distribution
Type I errors & Type II errors
25. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re
The Expected value
The Range
Independent Selection
Inferential
26. A numerical measure that describes an aspect of a sample.
Average and arithmetic mean
Statistic
A random variable
Probability density functions
27. 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
Statistical inference
the population mean
Pairwise independence
28. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Kurtosis
An estimate of a parameter
Ratio measurements
observational study
29. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
A Random vector
The Range
the sample or population mean
Likert scale
30. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
The variance of a random variable
the population mean
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Sampling Distribution
31. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
The Range
Type II errors
quantitative variables
Experimental and observational studies
32. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
33. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
The Covariance between two random variables X and Y - with expected values E(X) =
Sampling Distribution
Skewness
Particular realizations of a random variable
34. 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.
Quantitative variable
An Elementary event
Independent Selection
That value is the median value
35. A numerical measure that assesses the strength of a linear relationship between two variables.
hypotheses
Correlation coefficient
Step 2 of a statistical experiment
Prior probability
36. A group of individuals sharing some common features that might affect the treatment.
Estimator
Outlier
Block
Dependent Selection
37. 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.
A data point
A Distribution function
Marginal distribution
observational study
38. The standard deviation of a sampling distribution.
Law of Parsimony
Simple random sample
Probability and statistics
Standard error
39.
nominal - ordinal - interval - and ratio
the population mean
Simulation
Sampling frame
40. 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
Confounded variables
Ratio measurements
nominal - ordinal - interval - and ratio
Treatment
41. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Marginal distribution
Posterior probability
Joint distribution
That value is the median value
42. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Skewness
An estimate of a parameter
Treatment
A statistic
43. 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.
quantitative variables
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Simple random sample
The Expected value
44. 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
The average - or arithmetic mean
Simpson's Paradox
Interval measurements
45. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
variance of X
Dependent Selection
Placebo effect
Prior probability
46. Is that part of a population which is actually observed.
Pairwise independence
Kurtosis
A sample
Marginal distribution
47. Is data that can take only two values - usually represented by 0 and 1.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Experimental and observational studies
Probability and statistics
Binary data
48. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Individual
Power of a test
Statistical dispersion
An event
49. (cdfs) are denoted by upper case letters - e.g. F(x).
experimental studies and observational studies.
Ordinal measurements
Statistical inference
Cumulative distribution functions
50. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Standard error
Law of Large Numbers
Descriptive
Inferential