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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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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
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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. (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
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Average and arithmetic mean
Binary data
2. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Marginal distribution
A sample
Variability
Type II errors
3. The standard deviation of a sampling distribution.
Coefficient of determination
Ordinal measurements
Standard error
Inferential statistics
4. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.
Conditional probability
An Elementary event
Reliable measure
Step 3 of a statistical experiment
5. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
Binary data
Cumulative distribution functions
s-algebras
Law of Large Numbers
6. 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.
Statistics
Marginal probability
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Treatment
7. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Observational study
A sampling distribution
observational study
the population cumulants
8. (cdfs) are denoted by upper case letters - e.g. F(x).
Mutual independence
Cumulative distribution functions
P-value
expected value of X
9. Any specific experimental condition applied to the subjects
Treatment
f(z) - and its cdf by F(z).
hypotheses
An estimate of a parameter
10. Is a sample space over which a probability measure has been defined.
A probability space
Statistical dispersion
That value is the median value
Outlier
11. Statistical methods can be used for summarizing or describing a collection of data; this is called
quantitative variables
descriptive statistics
Descriptive
Conditional distribution
12. 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
Lurking variable
covariance of X and Y
An estimate of a parameter
13. 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.
The Covariance between two random variables X and Y - with expected values E(X) =
Valid measure
the population variance
Sampling
14. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Correlation
hypotheses
Descriptive
The Mean of a random variable
15. ?r
the population cumulants
Cumulative distribution functions
the population variance
Lurking variable
16.
the population mean
Trend
Ratio measurements
The sample space
17. Working from a null hypothesis two basic forms of error are recognized:
Binomial experiment
Type I errors & Type II errors
expected value of X
Outlier
18. Var[X] :
The Covariance between two random variables X and Y - with expected values E(X) =
A likelihood function
Greek letters
variance of X
19. Data are gathered and correlations between predictors and response are investigated.
observational study
Count data
Type 1 Error
the population mean
20. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Ordinal measurements
Coefficient of determination
Sampling frame
Bias
21. S^2
The Range
Sampling frame
the population variance
Statistical dispersion
22. Some commonly used symbols for population parameters
Bias
the population mean
Step 1 of a statistical experiment
Sampling Distribution
23. A group of individuals sharing some common features that might affect the treatment.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Block
Probability
Placebo effect
24. A numerical facsimilie or representation of a real-world phenomenon.
Joint distribution
Simulation
Cumulative distribution functions
Conditional distribution
25. Gives the probability of events in a probability space.
A statistic
observational study
A data set
A Probability measure
26. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
the population variance
P-value
An event
Trend
27. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
expected value of X
Law of Large Numbers
Step 3 of a statistical experiment
Kurtosis
28. Some commonly used symbols for sample statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Qualitative variable
Type I errors
covariance of X and Y
29. 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
covariance of X and Y
A population or statistical population
A probability space
Descriptive statistics
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
A probability density function
Statistical inference
Joint probability
31. 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.
Mutual independence
Marginal distribution
Kurtosis
Ordinal measurements
32. When there is an even number of values...
Type II errors
Residuals
That is the median value
An Elementary event
33. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Descriptive statistics
An Elementary event
A probability distribution
Bias
34. 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
applied statistics
inferential statistics
expected value of X
Ratio measurements
35. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
observational study
Law of Parsimony
Quantitative variable
Placebo effect
36. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Simulation
Binomial experiment
Lurking variable
Likert scale
37. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Independence or Statistical independence
Inferential
Probability and statistics
A probability density function
38. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
Correlation
Count data
Prior probability
39. 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.
Qualitative variable
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Bias
Independent Selection
40. A numerical measure that describes an aspect of a sample.
The Range
Statistic
Correlation
the population cumulants
41. A numerical measure that describes an aspect of a population.
Parameter
A Distribution function
A Statistical parameter
Ratio measurements
42. 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.
Dependent Selection
Type I errors
Mutual independence
A data point
43. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Power of a test
variance of X
Ratio measurements
Residuals
44. (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
Correlation
The Expected value
Qualitative variable
Residuals
45. Have imprecise differences between consecutive values - but have a meaningful order to those values
the population cumulants
The Mean of a random variable
Ratio measurements
Ordinal measurements
46. 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
Atomic event
Type II errors
Observational study
Sample space
47. 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.
the population mean
Divide the sum by the number of values.
Statistical inference
The median value
48. Is that part of a population which is actually observed.
Correlation
A sample
Step 1 of a statistical experiment
Random variables
49. Gives the probability distribution for a continuous random variable.
Experimental and observational studies
the sample or population mean
Independent Selection
A probability density function
50. ?
Atomic event
A probability density function
Posterior probability
the population correlation