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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. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
A sample
Prior probability
Statistics
2. 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
Placebo effect
Correlation
The Mean of a random variable
3. 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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4. A measure that is relevant or appropriate as a representation of that property.
Valid measure
Conditional distribution
Lurking variable
The Mean of a random variable
5. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
Outlier
That value is the median value
Binomial experiment
6. A numerical measure that describes an aspect of a sample.
Statistic
An estimate of a parameter
The sample space
hypothesis
7. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
Residuals
experimental studies and observational studies.
s-algebras
8. Have imprecise differences between consecutive values - but have a meaningful order to those values
Variable
Probability density
Ordinal measurements
The standard deviation
9. 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.
Pairwise independence
Statistics
Joint probability
An experimental study
10. A numerical measure that describes an aspect of a population.
Parameter
Estimator
s-algebras
methods of least squares
11. Is data arising from counting that can take only non-negative integer values.
Statistics
Binary data
Count data
Confounded variables
12. Gives the probability distribution for a continuous random variable.
A probability density function
f(z) - and its cdf by F(z).
An estimate of a parameter
Joint distribution
13. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Null hypothesis
Standard error
categorical variables
Residuals
14. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Marginal probability
the sample or population mean
Quantitative variable
Observational study
15. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.
Trend
Statistical inference
Pairwise independence
Interval measurements
16. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Cumulative distribution functions
The sample space
Statistical dispersion
Parameter
17. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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18. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
the population cumulants
Joint distribution
Ratio measurements
Type I errors
19. 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
Ratio measurements
Inferential statistics
Placebo effect
hypotheses
20. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
nominal - ordinal - interval - and ratio
Likert scale
Alpha value (Level of Significance)
experimental studies and observational studies.
21. Some commonly used symbols for sample statistics
covariance of X and Y
hypotheses
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Parameter
22. S^2
the population variance
the sample or population mean
Nominal measurements
A Probability measure
23. 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
Descriptive statistics
Null hypothesis
Statistical dispersion
Type 1 Error
24. Another name for elementary event.
Individual
Atomic event
Estimator
Simpson's Paradox
25. In particular - the pdf of the standard normal distribution is denoted by
The median value
Random variables
A probability space
f(z) - and its cdf by F(z).
26. 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.
s-algebras
Credence
The median value
Parameter
27. Are simply two different terms for the same thing. Add the given values
Block
Average and arithmetic mean
Parameter - or 'statistical parameter'
Individual
28. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit
expected value of X
An event
Probability density
Statistics
29. 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
applied statistics
quantitative variables
Skewness
the sample or population mean
30. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Residuals
the sample or population mean
Ordinal measurements
Bias
31. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
The average - or arithmetic mean
P-value
Independent Selection
Block
32. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the
Law of Large Numbers
Marginal distribution
Outlier
Probability
33. Describes a characteristic of an individual to be measured or observed.
P-value
Probability
Sample space
Variable
34. 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.
Seasonal effect
Conditional distribution
Posterior probability
Beta value
35. Patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations - and are then used for drawing inferences about the process or population being studied; this is called
Variability
The Expected value
inferential statistics
P-value
36. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
categorical variables
An estimate of a parameter
The Expected value
Type 2 Error
37. Is a parameter that indexes a family of probability distributions.
Confounded variables
A Statistical parameter
variance of X
Simulation
38. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Probability density functions
A sample
Joint probability
Quantitative variable
39. 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
Law of Large Numbers
Reliable measure
Divide the sum by the number of values.
40. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
P-value
applied statistics
A data point
A random variable
41. A numerical facsimilie or representation of a real-world phenomenon.
Statistics
Statistic
Simulation
An event
42. 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
Block
The Expected value
Type 2 Error
Probability and statistics
43. Working from a null hypothesis two basic forms of error are recognized:
Variable
A probability density function
Likert scale
Type I errors & Type II errors
44. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.
Sampling
Lurking variable
Marginal distribution
Placebo effect
45. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
Parameter - or 'statistical parameter'
Pairwise independence
Cumulative distribution functions
46. 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
Type II errors
Alpha value (Level of Significance)
Experimental and observational studies
47. 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.
f(z) - and its cdf by F(z).
Binomial experiment
A Distribution function
Conditional distribution
48. Long-term upward or downward movement over time.
Ratio measurements
categorical variables
Trend
An event
49. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
quantitative variables
observational study
Statistical inference
50. Is that part of a population which is actually observed.
A probability space
A sample
Prior probability
Step 1 of a statistical experiment