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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.
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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. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.
Quantitative variable
A statistic
Dependent Selection
Bias
2. 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.
Statistics
The Range
A Probability measure
Lurking variable
3. 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
Seasonal effect
Observational study
Prior probability
Parameter
4. 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.
Parameter
Posterior probability
Bias
methods of least squares
5. 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.
The Covariance between two random variables X and Y - with expected values E(X) =
The Range
Bias
Marginal distribution
6. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
quantitative variables
The average - or arithmetic mean
Null hypothesis
the population correlation
7. ?r
the population cumulants
Variability
f(z) - and its cdf by F(z).
Likert scale
8. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
the population cumulants
Variability
Parameter
Statistical dispersion
9. Is a sample and the associated data points.
A data set
Variable
the population cumulants
Sampling frame
10. Cov[X - Y] :
A random variable
covariance of X and Y
Block
A likelihood function
11. 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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12. Is a subset of the sample space - to which a probability can be assigned. For example - on rolling a die - 'getting a five or a six' is an event (with a probability of one third if the die is fair).
An event
f(z) - and its cdf by F(z).
Joint probability
methods of least squares
13. Rejecting a true null hypothesis.
Statistic
Type 1 Error
Cumulative distribution functions
Mutual independence
14. Is a parameter that indexes a family of probability distributions.
A data set
f(z) - and its cdf by F(z).
the population cumulants
A Statistical parameter
15. 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.
Probability and statistics
Correlation
Cumulative distribution functions
Experimental and observational studies
16. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies
Divide the sum by the number of values.
A likelihood function
A statistic
experimental studies and observational studies.
17. 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
A statistic
hypothesis
categorical variables
18. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
the population correlation
A data point
applied statistics
Conditional probability
19. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Pairwise independence
A Probability measure
the population mean
Quantitative variable
20. A measurement such that the random error is small
A Distribution function
Conditional distribution
Reliable measure
Prior probability
21. S^2
the population variance
experimental studies and observational studies.
methods of least squares
Simulation
22. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. 4. Further examining the data set in secondary analyses - to suggest new hypotheses for future study. 5. Documenting and present
The Range
A data point
Step 3 of a statistical experiment
Independent Selection
23. The standard deviation of a sampling distribution.
Count data
methods of least squares
Standard error
The average - or arithmetic mean
24. Working from a null hypothesis two basic forms of error are recognized:
Sample space
Type I errors & Type II errors
A population or statistical population
categorical variables
25. Describes the spread in the values of the sample statistic when many samples are taken.
Block
Variability
applied statistics
Inferential
26. E[X] :
expected value of X
Bias
Parameter - or 'statistical parameter'
Individual
27. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Statistical dispersion
Conditional distribution
experimental studies and observational studies.
Bias
28. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Descriptive
A statistic
Type 2 Error
A data point
29. Have no meaningful rank order among values.
Nominal measurements
Binomial experiment
That value is the median value
nominal - ordinal - interval - and ratio
30. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
Binomial experiment
Trend
Standard error
31. Probability of accepting a false null hypothesis.
Mutual independence
A Distribution function
Parameter - or 'statistical parameter'
Beta value
32. Some commonly used symbols for sample statistics
Kurtosis
Sampling Distribution
Prior probability
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
33. (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.
Cumulative distribution functions
Confounded variables
Type II errors
An Elementary event
34. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
A Random vector
quantitative variables
Statistical adjustment
Particular realizations of a random variable
35. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.
The median value
An experimental study
Type I errors & Type II errors
Kurtosis
36.
Ordinal measurements
the population mean
Step 3 of a statistical experiment
A Random vector
37. 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
Skewness
Type 2 Error
Simple random sample
Inferential
38. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that
Mutual independence
A sampling distribution
Bias
hypothesis
39. Is data that can take only two values - usually represented by 0 and 1.
Statistical inference
Experimental and observational studies
Binary data
Likert scale
40. A numerical measure that describes an aspect of a population.
Probability density functions
Particular realizations of a random variable
Parameter
Skewness
41. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
Statistical adjustment
That is the median value
A Statistical parameter
42. Another name for elementary event.
Posterior probability
Atomic event
Sampling
s-algebras
43. Two variables such that their effects on the response variable cannot be distinguished from each other.
Marginal probability
Sample space
Confounded variables
Sampling
44. 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.
Lurking variable
Qualitative variable
Sampling
Conditional distribution
45. Failing to reject a false null hypothesis.
inferential statistics
Correlation
expected value of X
Type 2 Error
46. A numerical facsimilie or representation of a real-world phenomenon.
Statistical adjustment
Likert scale
Lurking variable
Simulation
47. Any specific experimental condition applied to the subjects
Marginal distribution
Treatment
quantitative variables
nominal - ordinal - interval - and ratio
48. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
Statistical dispersion
Cumulative distribution functions
A likelihood function
49. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.
The variance of a random variable
Credence
categorical variables
Probability density functions
50. Is the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.
Interval measurements
Trend
Simulation
Marginal probability