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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
Posterior probability
methods of least squares
A probability density function
the population cumulants
2. (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
Kurtosis
the population mean
Interval measurements
3. 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.
A population or statistical population
Estimator
The average - or arithmetic mean
Binomial experiment
4. Long-term upward or downward movement over time.
A Random vector
hypothesis
Trend
Independent Selection
5. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Probability
Joint probability
Pairwise independence
The standard deviation
6. 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'
Conditional probability
Prior probability
Credence
Statistical dispersion
7. Some commonly used symbols for sample statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A population or statistical population
Inferential
Sampling
8. Are usually written in upper case roman letters: X - Y - etc.
Statistical dispersion
Law of Large Numbers
Descriptive statistics
Random variables
9. 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
A probability distribution
Inferential statistics
Bias
10. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Statistics
Nominal measurements
Divide the sum by the number of values.
nominal - ordinal - interval - and ratio
11. A measure that is relevant or appropriate as a representation of that property.
Reliable measure
Valid measure
Step 2 of a statistical experiment
Nominal measurements
12. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Probability
A statistic
categorical variables
Divide the sum by the number of values.
13. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
hypotheses
Probability density functions
Binary data
Binomial experiment
14. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
categorical variables
Probability density functions
Greek letters
Bias
15. 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
Sampling
A probability density function
Confounded variables
hypotheses
16. (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
A data point
Atomic event
Variability
17. 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.
Likert scale
Independence or Statistical independence
The median value
the population mean
18. 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
Probability
Statistics
The Mean of a random variable
the population correlation
19. Describes the spread in the values of the sample statistic when many samples are taken.
Probability and statistics
f(z) - and its cdf by F(z).
Experimental and observational studies
Variability
20. Probability of rejecting a true null hypothesis.
Step 2 of a statistical experiment
Type II errors
A data set
Alpha value (Level of Significance)
21. 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
Independence or Statistical independence
nominal - ordinal - interval - and ratio
Type 1 Error
The average - or arithmetic mean
22. 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
Prior probability
The median value
Inferential statistics
Law of Parsimony
23. Is that part of a population which is actually observed.
An event
A likelihood function
covariance of X and Y
A sample
24. Is denoted by - pronounced 'x bar'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
applied statistics
Statistic
Conditional distribution
25. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Law of Large Numbers
Descriptive statistics
Greek letters
The average - or arithmetic mean
26. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Type I errors & Type II errors
Individual
quantitative variables
The Expected value
27. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl
Inferential statistics
Greek letters
Statistics
Mutual independence
28. Are simply two different terms for the same thing. Add the given values
Law of Parsimony
Type 2 Error
Statistical dispersion
Average and arithmetic mean
29. A numerical measure that describes an aspect of a sample.
inferential statistics
variance of X
Marginal distribution
Statistic
30.
applied statistics
the population mean
expected value of X
Probability
31. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
A probability distribution
Sampling Distribution
the population correlation
Inferential
32. When you have two or more competing models - choose the simpler of the two models.
The Mean of a random variable
Law of Parsimony
Sample space
expected value of X
33. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are
Pairwise independence
Independence or Statistical independence
experimental studies and observational studies.
Descriptive statistics
34. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
Variability
A probability density function
Trend
35. Any specific experimental condition applied to the subjects
The Range
Treatment
experimental studies and observational studies.
The variance of a random variable
36. Describes a characteristic of an individual to be measured or observed.
Variable
Estimator
Step 1 of a statistical experiment
An event
37. Working from a null hypothesis two basic forms of error are recognized:
Cumulative distribution functions
Type I errors & Type II errors
Count data
Probability density functions
38. 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.
Likert scale
Experimental and observational studies
Correlation
Probability and statistics
39. 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.
Type II errors
A random variable
f(z) - and its cdf by F(z).
Count data
40. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
methods of least squares
Statistical adjustment
The Range
Experimental and observational studies
41. Some commonly used symbols for population parameters
Individual
Standard error
the population mean
Statistical dispersion
42. 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
The variance of a random variable
Qualitative variable
Null hypothesis
A probability density function
43. Is a sample and the associated data points.
A data set
Valid measure
Parameter
Correlation
44. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
Likert scale
Step 3 of a statistical experiment
Particular realizations of a random variable
45. S^2
Trend
Credence
A probability density function
the population variance
46. The standard deviation of a sampling distribution.
Standard error
A population or statistical population
Experimental and observational studies
Placebo effect
47. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
The variance of a random variable
A likelihood function
Descriptive
Joint probability
48. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
An estimate of a parameter
categorical variables
experimental studies and observational studies.
Law of Parsimony
49. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Law of Parsimony
applied statistics
A sampling distribution
Quantitative variable
50. Is defined as the expected value of random variable (X -
the population cumulants
Sampling Distribution
hypothesis
The Covariance between two random variables X and Y - with expected values E(X) =