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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. Many statistical methods seek to minimize the mean-squared error - and these are called
Greek letters
variance of X
Independence or Statistical independence
methods of least squares
2. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
Simpson's Paradox
P-value
Simple random sample
3. Is defined as the expected value of random variable (X -
Lurking variable
The Covariance between two random variables X and Y - with expected values E(X) =
Experimental and observational studies
Random variables
4. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Simpson's Paradox
Probability
Sampling Distribution
Placebo effect
5. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
A probability space
Independent Selection
An estimate of a parameter
6. Some commonly used symbols for population parameters
Binomial experiment
A probability distribution
An Elementary event
the population mean
7. 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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8. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Null hypothesis
the sample or population mean
quantitative variables
Joint probability
9. When there is an even number of values...
Statistical adjustment
That is the median value
The Mean of a random variable
Valid measure
10. A variable describes an individual by placing the individual into a category or a group.
Qualitative variable
Placebo effect
Inferential statistics
Beta value
11. A numerical measure that describes an aspect of a population.
Alpha value (Level of Significance)
Parameter
Skewness
Variable
12. 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.
Count data
nominal - ordinal - interval - and ratio
inferential statistics
A population or statistical population
13. 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
descriptive statistics
The Range
Independent Selection
Independence or Statistical independence
14. Is that part of a population which is actually observed.
Block
A sample
covariance of X and Y
Type I errors & Type II errors
15. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
Parameter - or 'statistical parameter'
quantitative variables
Treatment
16. Is a parameter that indexes a family of probability distributions.
A Random vector
Divide the sum by the number of values.
A Statistical parameter
Inferential statistics
17. Cov[X - Y] :
Average and arithmetic mean
the population cumulants
covariance of X and Y
Parameter - or 'statistical parameter'
18. Is its expected value. The mean (or sample mean of a data set is just the average value.
Statistics
categorical variables
inferential statistics
The Mean of a random variable
19. A subjective estimate of probability.
An experimental study
P-value
Seasonal effect
Credence
20. 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.
Sampling
Statistics
A population or statistical population
An experimental study
21. 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.
Independence or Statistical independence
Credence
Random variables
The median value
22. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Bias
applied statistics
Inferential
Sample space
23. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
That is the median value
Posterior probability
categorical variables
Average and arithmetic mean
24. Probability of rejecting a true null hypothesis.
Alpha value (Level of Significance)
Coefficient of determination
A Probability measure
Sampling Distribution
25. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no
Probability and statistics
Power of a test
the population correlation
Marginal distribution
26. 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)
Interval measurements
s-algebras
categorical variables
Parameter
27. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
An event
Statistical inference
Binomial experiment
A probability space
28. (cdfs) are denoted by upper case letters - e.g. F(x).
Statistical dispersion
Cumulative distribution functions
The standard deviation
descriptive statistics
29. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
the sample or population mean
Lurking variable
Likert scale
Statistical inference
30. 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
Step 2 of a statistical experiment
The variance of a random variable
Correlation
Type 2 Error
31. Gives the probability of events in a probability space.
A Probability measure
f(z) - and its cdf by F(z).
A Distribution function
Quantitative variable
32. 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.
Marginal distribution
nominal - ordinal - interval - and ratio
Outlier
Nominal measurements
33. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Independence or Statistical independence
Joint distribution
Type 1 Error
Coefficient of determination
34. Is a sample space over which a probability measure has been defined.
A probability space
Individual
Block
Posterior probability
35. Another name for elementary event.
Atomic event
A random variable
Mutual independence
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
36. 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
A probability distribution
Ordinal measurements
Inferential statistics
Qualitative variable
37. A numerical measure that describes an aspect of a sample.
Statistic
Quantitative variable
Interval measurements
Power of a test
38. Is the length of the smallest interval which contains all the data.
The Range
Law of Large Numbers
Descriptive
Descriptive statistics
39. 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
A Random vector
Ratio measurements
f(z) - and its cdf by F(z).
A statistic
40. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a
Step 3 of a statistical experiment
Binary data
Null hypothesis
The Range
41. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
Bias
observational study
categorical variables
42. A measurement such that the random error is small
Reliable measure
the population mean
Simulation
Alpha value (Level of Significance)
43. Failing to reject a false null hypothesis.
Type 2 Error
Independence or Statistical independence
applied statistics
Joint probability
44. Are usually written in upper case roman letters: X - Y - etc.
Bias
Random variables
The median value
Valid measure
45. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
A probability density function
An estimate of a parameter
Sampling Distribution
hypothesis
46. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Independence or Statistical independence
Probability
Descriptive
Sampling
47. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
Statistical dispersion
Simple random sample
Greek letters
48. The collection of all possible outcomes in an experiment.
Inferential
Type I errors
s-algebras
Sample space
49. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistical adjustment
Confounded variables
Marginal distribution
A random variable
50. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Particular realizations of a random variable
Conditional probability
A probability space
Step 2 of a statistical experiment