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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. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Particular realizations of a random variable
Observational study
Descriptive
That value is the median value
2. (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
A likelihood function
Coefficient of determination
Inferential
Lurking variable
3. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Prior probability
expected value of X
Quantitative variable
Power of a test
4. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
A probability distribution
Joint distribution
Confounded variables
the sample or population mean
5. Is a function that gives the probability of all elements in a given space: see List of probability distributions
nominal - ordinal - interval - and ratio
That is the median value
Valid measure
A probability distribution
6. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Probability density functions
A probability distribution
A random variable
descriptive statistics
7. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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8. 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
Beta value
Likert scale
A Distribution function
Correlation
9. ?
Placebo effect
The standard deviation
the population correlation
A Random vector
10. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
A sampling distribution
Cumulative distribution functions
observational study
categorical variables
11. Probability of accepting a false null hypothesis.
Beta value
observational study
The Mean of a random variable
categorical variables
12. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
descriptive statistics
A Random vector
the population mean
Step 2 of a statistical experiment
13. 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
Dependent Selection
Skewness
experimental studies and observational studies.
An event
14. 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)
f(z) - and its cdf by F(z).
Type 2 Error
Simpson's Paradox
Interval measurements
15. 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
Reliable measure
Binomial experiment
The Expected value
hypothesis
16. 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
A likelihood function
Estimator
P-value
Step 3 of a statistical experiment
17. A data value that falls outside the overall pattern of the graph.
A probability space
Binary data
Outlier
Conditional probability
18. Many statistical methods seek to minimize the mean-squared error - and these are called
A Distribution function
Simpson's Paradox
methods of least squares
Type 1 Error
19. 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.
That value is the median value
Reliable measure
The median value
A sampling distribution
20. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
An estimate of a parameter
Simple random sample
The Expected value
experimental studies and observational studies.
21. 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
A data set
Power of a test
Simulation
22. The standard deviation of a sampling distribution.
Inferential statistics
Marginal probability
Outlier
Standard error
23. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Parameter
the population variance
Descriptive statistics
Prior probability
24. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Nominal measurements
hypotheses
A probability density function
A statistic
25. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistical adjustment
Outlier
Type I errors & Type II errors
Marginal probability
26. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Observational study
Individual
nominal - ordinal - interval - and ratio
Simulation
27. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Skewness
Statistical dispersion
variance of X
Conditional probability
28. Are simply two different terms for the same thing. Add the given values
Outlier
Average and arithmetic mean
Qualitative variable
Step 1 of a statistical experiment
29. Describes a characteristic of an individual to be measured or observed.
Conditional distribution
expected value of X
The Range
Variable
30. 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.
The Expected value
Bias
Simple random sample
Ratio measurements
31. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
nominal - ordinal - interval - and ratio
Joint distribution
experimental studies and observational studies.
Count data
32. A common goal for a statistical research project is to investigate causality - and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response.
hypotheses
Correlation coefficient
Experimental and observational studies
Independent Selection
33. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
the population cumulants
Marginal distribution
Correlation
34. Have no meaningful rank order among values.
Joint probability
Power of a test
Sampling Distribution
Nominal measurements
35. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.
The variance of a random variable
Type II errors
Observational study
An estimate of a parameter
36. Rejecting a true null hypothesis.
Type 1 Error
A population or statistical population
Inferential statistics
Valid measure
37. 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.
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38. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Inferential
hypotheses
Confounded variables
Sample space
39. A numerical measure that describes an aspect of a sample.
Beta value
the sample or population mean
categorical variables
Statistic
40. A measurement such that the random error is small
Simple random sample
Reliable measure
Probability density functions
Conditional probability
41. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a
descriptive statistics
Cumulative distribution functions
Step 2 of a statistical experiment
Kurtosis
42. 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
Sampling
Ratio measurements
Joint probability
Residuals
43. 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
Quantitative variable
the population variance
44. S^2
Prior probability
the population variance
Conditional distribution
the population correlation
45. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
Atomic event
Residuals
Experimental and observational studies
46. Statistical methods can be used for summarizing or describing a collection of data; this is called
A Probability measure
descriptive statistics
A sampling distribution
Average and arithmetic mean
47. 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.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Bias
Observational study
Simpson's Paradox
48. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.
Conditional distribution
Residuals
Interval measurements
A sample
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.
Seasonal effect
Statistics
A Random vector
Null hypothesis
50. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.
Inferential
observational study
Skewness
Sampling