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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
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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 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
Conditional probability
Cumulative distribution functions
The Expected value
A sampling distribution
2. Describes a characteristic of an individual to be measured or observed.
A statistic
Individual
Variable
Step 1 of a statistical experiment
3. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Statistical adjustment
Observational study
the sample or population mean
Skewness
4.
Conditional probability
Binary data
Simulation
the population mean
5. ?r
Simpson's Paradox
the population cumulants
Statistic
Type I errors
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.
the population variance
A probability space
Statistics
A probability distribution
7. Are usually written in upper case roman letters: X - Y - etc.
Ordinal measurements
A sample
Random variables
Atomic event
8. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Type II errors
Atomic event
Descriptive statistics
9. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
the population variance
descriptive statistics
The Range
10. The probability of correctly detecting a false null hypothesis.
Probability
The Covariance between two random variables X and Y - with expected values E(X) =
Inferential
Power of a test
11. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.
A statistic
Sample space
Observational study
A population or statistical population
12. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.
Greek letters
Sampling
quantitative variables
Independent Selection
13. Probability of rejecting a true null hypothesis.
s-algebras
the sample or population mean
Alpha value (Level of Significance)
Standard error
14. 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
Bias
Observational study
Interval measurements
the population variance
15. Working from a null hypothesis two basic forms of error are recognized:
Simple random sample
An experimental study
Type I errors & Type II errors
Simulation
16. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Individual
Marginal probability
The variance of a random variable
Greek letters
17. A data value that falls outside the overall pattern of the graph.
The Mean of a random variable
f(z) - and its cdf by F(z).
Beta value
Outlier
18. Is denoted by - pronounced 'x bar'.
Statistical inference
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
A sampling distribution
categorical variables
19. 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
Dependent Selection
The standard deviation
A statistic
20. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Binomial experiment
Atomic event
inferential statistics
the population cumulants
21. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Variable
Residuals
nominal - ordinal - interval - and ratio
A random variable
22. The standard deviation of a sampling distribution.
Treatment
Probability density
Standard error
Average and arithmetic mean
23. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Placebo effect
An estimate of a parameter
categorical variables
Quantitative variable
24. (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.
Step 1 of a statistical experiment
An Elementary event
Coefficient of determination
Variable
25. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Step 2 of a statistical experiment
Law of Parsimony
categorical variables
Individual
26. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
The Range
Estimator
Statistical adjustment
Type I errors
27. Of a group of numbers is the center point of all those number values.
Skewness
Joint probability
The average - or arithmetic mean
A data set
28. The proportion of the explained variation by a linear regression model in the total variation.
Coefficient of determination
Conditional distribution
the population mean
Average and arithmetic mean
29. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
Beta value
That value is the median value
Lurking variable
30. Var[X] :
Type I errors
variance of X
Sampling
Pairwise independence
31. Two variables such that their effects on the response variable cannot be distinguished from each other.
Posterior probability
Ratio measurements
Inferential statistics
Confounded variables
32. Is data arising from counting that can take only non-negative integer values.
Count data
A likelihood function
Sampling
Type I errors & Type II errors
33. In particular - the pdf of the standard normal distribution is denoted by
Step 2 of a statistical experiment
Independence or Statistical independence
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
f(z) - and its cdf by F(z).
34. To find the average - or arithmetic mean - of a set of numbers:
Simulation
A data set
methods of least squares
Divide the sum by the number of values.
35. 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.
Alpha value (Level of Significance)
Bias
That is the median value
the population variance
36. (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
Probability and statistics
A data set
A likelihood function
A statistic
37. (cdfs) are denoted by upper case letters - e.g. F(x).
Statistical adjustment
Mutual independence
Cumulative distribution functions
Probability and statistics
38. 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
Sample space
A probability distribution
experimental studies and observational studies.
methods of least squares
39. 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
the population mean
Conditional distribution
Statistical dispersion
40. 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
The standard deviation
Bias
The Expected value
hypotheses
41. 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.
An experimental study
Sampling
f(z) - and its cdf by F(z).
the population mean
42. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
The Mean of a random variable
Pairwise independence
s-algebras
Bias
43. A numerical facsimilie or representation of a real-world phenomenon.
Credence
Step 1 of a statistical experiment
Simulation
categorical variables
44. A numerical measure that assesses the strength of a linear relationship between two variables.
Power of a test
Correlation coefficient
Statistics
Kurtosis
45. 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).
Joint probability
the sample or population mean
The average - or arithmetic mean
Inferential statistics
46. 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).
Sample space
Simple random sample
That is the median value
An event
47. 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
Lurking variable
Seasonal effect
Independence or Statistical independence
hypothesis
48. S^2
the population variance
Experimental and observational studies
Confounded variables
The average - or arithmetic mean
49. 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
Ordinal measurements
Simpson's Paradox
Inferential statistics
Parameter
50. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.
Correlation coefficient
Probability density functions
An experimental study
Simple random sample