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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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Study First
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
.
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. Any specific experimental condition applied to the subjects
the population variance
Treatment
Particular realizations of a random variable
Sampling
2. Have imprecise differences between consecutive values - but have a meaningful order to those values
hypotheses
inferential statistics
Ordinal measurements
Bias
3. Is data arising from counting that can take only non-negative integer values.
the sample or population mean
Count data
An experimental study
Binomial experiment
4. A numerical measure that assesses the strength of a linear relationship between two variables.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
The Covariance between two random variables X and Y - with expected values E(X) =
Correlation coefficient
A sample
5. Is data that can take only two values - usually represented by 0 and 1.
Type II errors
Statistical dispersion
Variable
Binary data
6. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Variable
Power of a test
A probability density function
Posterior probability
7. (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
Bias
Type II errors
Step 1 of a statistical experiment
The Expected value
8. Of a group of numbers is the center point of all those number values.
A probability distribution
Estimator
An event
The average - or arithmetic mean
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.
Statistics
The average - or arithmetic mean
Marginal distribution
Average and arithmetic mean
10. E[X] :
expected value of X
Correlation
The sample space
Valid measure
11. 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.
Step 3 of a statistical experiment
Interval measurements
Dependent Selection
Greek letters
12. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Pairwise independence
Divide the sum by the number of values.
Statistical adjustment
applied statistics
13. 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
The Covariance between two random variables X and Y - with expected values E(X) =
experimental studies and observational studies.
An Elementary event
inferential statistics
14. Gives the probability distribution for a continuous random variable.
the sample or population mean
Atomic event
The standard deviation
A probability density function
15. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
A data point
Bias
Probability
16. 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).
Mutual independence
A statistic
An event
nominal - ordinal - interval - and ratio
17. 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
A data point
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
experimental studies and observational studies.
the population variance
18. 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.
Variable
Residuals
Experimental and observational studies
Type 2 Error
19. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Qualitative variable
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
The median value
Sampling Distribution
20. 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.
A random variable
Independence or Statistical independence
covariance of X and Y
A sample
21. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P
Simpson's Paradox
The Range
Correlation
Type II errors
22. The proportion of the explained variation by a linear regression model in the total variation.
Coefficient of determination
Conditional probability
A sampling distribution
Random variables
23. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
An event
quantitative variables
Seasonal effect
Law of Large Numbers
24. A measure that is relevant or appropriate as a representation of that property.
A data set
Valid measure
That is the median value
Particular realizations of a random variable
25. Long-term upward or downward movement over time.
Average and arithmetic mean
Trend
Individual
experimental studies and observational studies.
26. 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.
Correlation coefficient
Cumulative distribution functions
Residuals
An experimental study
27. S^2
the population variance
The average - or arithmetic mean
A Probability measure
An experimental study
28. A list of individuals from which the sample is actually selected.
Simpson's Paradox
The Expected value
Probability
Sampling frame
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
Joint probability
Atomic event
Skewness
Type II errors
30. A data value that falls outside the overall pattern of the graph.
Alpha value (Level of Significance)
Outlier
Individual
variance of X
31. A numerical measure that describes an aspect of a population.
The Range
Parameter
Kurtosis
Type 2 Error
32. Gives the probability of events in a probability space.
A Probability measure
Bias
Particular realizations of a random variable
Type II errors
33. Rejecting a true null hypothesis.
Type 1 Error
The Range
A Random vector
Cumulative distribution functions
34. 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
Null hypothesis
Count data
Skewness
Ratio measurements
35. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
Beta value
Step 1 of a statistical experiment
Probability density functions
36. Some commonly used symbols for sample statistics
A probability density function
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Seasonal effect
The standard deviation
37. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Greek letters
Seasonal effect
Average and arithmetic mean
Descriptive
38. 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
Step 3 of a statistical experiment
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Estimator
Joint distribution
39. Cov[X - Y] :
Statistical inference
A Random vector
covariance of X and Y
inferential statistics
40. A group of individuals sharing some common features that might affect the treatment.
Block
Particular realizations of a random variable
Statistics
The average - or arithmetic mean
41. 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.
Conditional distribution
The standard deviation
Lurking variable
observational study
42. Var[X] :
A statistic
Interval measurements
variance of X
Beta value
43. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Reliable measure
Joint distribution
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Descriptive
44. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
Block
quantitative variables
Joint distribution
45. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Particular realizations of a random variable
Inferential
The Covariance between two random variables X and Y - with expected values E(X) =
Type I errors
46. Is the length of the smallest interval which contains all the data.
Experimental and observational studies
Statistical inference
The Range
s-algebras
47. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
A data point
Greek letters
Lurking variable
The Covariance between two random variables X and Y - with expected values E(X) =
48. 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
the population mean
experimental studies and observational studies.
Independence or Statistical independence
Estimator
49. A numerical measure that describes an aspect of a sample.
Cumulative distribution functions
Probability
Law of Parsimony
Statistic
50. A numerical facsimilie or representation of a real-world phenomenon.
Independence or Statistical independence
Marginal probability
Nominal measurements
Simulation