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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.
the population mean
An estimate of a parameter
Trend
Count data
2. Is a parameter that indexes a family of probability distributions.
Standard error
Reliable measure
Marginal distribution
A Statistical parameter
3. Describes the spread in the values of the sample statistic when many samples are taken.
Parameter - or 'statistical parameter'
inferential statistics
Ratio measurements
Variability
4. Gives the probability of events in a probability space.
A Probability measure
The variance of a random variable
the sample or population mean
Placebo effect
5. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Type I errors & Type II errors
A probability space
quantitative variables
Law of Large Numbers
6. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
That is the median value
A probability density function
Sampling frame
7. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o
the population variance
Ratio measurements
Observational study
Binomial experiment
8. Long-term upward or downward movement over time.
Sampling Distribution
A data point
Trend
A Distribution function
9. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Marginal distribution
hypotheses
nominal - ordinal - interval - and ratio
The standard deviation
10. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
An estimate of a parameter
Statistical dispersion
s-algebras
11. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
The Mean of a random variable
A statistic
A Random vector
Residuals
12. Another name for elementary event.
variance of X
Atomic event
Coefficient of determination
A probability density function
13. Rejecting a true null hypothesis.
A probability distribution
Type 1 Error
Ordinal measurements
Step 1 of a statistical experiment
14. Data are gathered and correlations between predictors and response are investigated.
Confounded variables
observational study
The Mean of a random variable
s-algebras
15. 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.
Count data
A Probability measure
The median value
Interval measurements
16. 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'
hypotheses
Simple random sample
Conditional probability
Dependent Selection
17. 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
The average - or arithmetic mean
Sampling frame
Descriptive statistics
Ordinal measurements
18. Two variables such that their effects on the response variable cannot be distinguished from each other.
Posterior probability
Confounded variables
The variance of a random variable
Inferential
19. 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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20. 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.
The Range
Statistics
Statistical dispersion
Quantitative variable
21. A numerical facsimilie or representation of a real-world phenomenon.
Variability
Simulation
Quantitative variable
Probability density functions
22. 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)
Type II errors
Interval measurements
A Random vector
Sampling frame
23. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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24. 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.
observational study
A probability distribution
A random variable
Joint probability
25. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
covariance of X and Y
Law of Parsimony
Greek letters
nominal - ordinal - interval - and ratio
26. S^2
the population variance
Independent Selection
An event
Simple random sample
27. 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
Probability density
A data point
A statistic
Type 2 Error
28. 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
Quantitative variable
Observational study
Type I errors
inferential statistics
29. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Sampling frame
Random variables
Law of Large Numbers
Marginal distribution
30. 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.
Descriptive statistics
Statistical inference
Probability density functions
A probability distribution
31. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Marginal probability
An Elementary event
Type 2 Error
A probability distribution
32. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Simpson's Paradox
The Mean of a random variable
Pairwise independence
Probability and statistics
33. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
That value is the median value
A Random vector
Descriptive
A Probability measure
34. In particular - the pdf of the standard normal distribution is denoted by
f(z) - and its cdf by F(z).
Statistical inference
Parameter - or 'statistical parameter'
Dependent Selection
35. 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
hypotheses
Prior probability
Descriptive
Descriptive statistics
36. Some commonly used symbols for sample statistics
A statistic
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Null hypothesis
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
37. Describes a characteristic of an individual to be measured or observed.
Variable
Simpson's Paradox
expected value of X
Random variables
38. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Type 2 Error
Likert scale
descriptive statistics
Cumulative distribution functions
39. 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
Step 2 of a statistical experiment
the population cumulants
The Range
Mutual independence
40. 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.
Average and arithmetic mean
A sample
A Distribution function
The Mean of a random variable
41. Is the probability of two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).
Probability density
observational study
Joint probability
Estimator
42. 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
Divide the sum by the number of values.
applied statistics
Null hypothesis
43. 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.
A probability distribution
Block
Seasonal effect
A data point
44. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Probability density functions
Lurking variable
Skewness
Statistical adjustment
45. Statistical methods can be used for summarizing or describing a collection of data; this is called
Observational study
descriptive statistics
A Random vector
Variable
46. A data value that falls outside the overall pattern of the graph.
A random variable
Simulation
Outlier
Likert scale
47. A group of individuals sharing some common features that might affect the treatment.
Treatment
A data set
Inferential statistics
Block
48. Some commonly used symbols for population parameters
A sampling distribution
Statistics
the population mean
Conditional distribution
49. 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.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Kurtosis
The sample space
Descriptive
50. Is denoted by - pronounced 'x bar'.
Type II errors
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Treatment
A Probability measure