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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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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. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Posterior probability
A probability distribution
hypothesis
Step 1 of a statistical experiment
2. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Inferential
Bias
Particular realizations of a random variable
A sample
3. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
Joint distribution
Simple random sample
Coefficient of determination
4. A subjective estimate of probability.
Step 3 of a statistical experiment
Credence
The average - or arithmetic mean
Probability density
5. A numerical measure that describes an aspect of a sample.
Probability density
the population mean
Law of Parsimony
Statistic
6. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Joint distribution
quantitative variables
Power of a test
experimental studies and observational studies.
7. 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.
Standard error
Dependent Selection
Sampling frame
covariance of X and Y
8. Is that part of a population which is actually observed.
methods of least squares
A sampling distribution
A sample
Descriptive
9. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Alpha value (Level of Significance)
Trend
Lurking variable
Greek letters
10. 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.
Probability density
A data point
A probability space
Random variables
11. 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.
Probability density
inferential statistics
A Distribution function
methods of least squares
12. 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.
A Statistical parameter
Qualitative variable
Lurking variable
Individual
13. A numerical measure that describes an aspect of a population.
Parameter
Average and arithmetic mean
Inferential
Simpson's Paradox
14. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co
The median value
Statistics
Sampling
Step 1 of a statistical experiment
15. 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
Descriptive
Observational study
Sampling frame
categorical variables
16. Is denoted by - pronounced 'x bar'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Power of a test
Type I errors
Ratio measurements
17. Two variables such that their effects on the response variable cannot be distinguished from each other.
Reliable measure
A probability distribution
Sample space
Confounded variables
18. 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 median value
Descriptive statistics
Quantitative variable
Conditional probability
19. In particular - the pdf of the standard normal distribution is denoted by
A data set
The variance of a random variable
Independence or Statistical independence
f(z) - and its cdf by F(z).
20. 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).
Type I errors & Type II errors
Step 1 of a statistical experiment
Joint probability
Ordinal measurements
21. The proportion of the explained variation by a linear regression model in the total variation.
variance of X
Coefficient of determination
The Covariance between two random variables X and Y - with expected values E(X) =
Atomic event
22. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
A likelihood function
A random variable
An estimate of a parameter
hypothesis
23. Have imprecise differences between consecutive values - but have a meaningful order to those values
A Random vector
An Elementary event
Correlation coefficient
Ordinal measurements
24. 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.
Conditional probability
An estimate of a parameter
Marginal distribution
Bias
25. Is its expected value. The mean (or sample mean of a data set is just the average value.
Type 2 Error
the population mean
The Mean of a random variable
Residuals
26. 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
Treatment
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
The sample space
experimental studies and observational studies.
27. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Binomial experiment
The Covariance between two random variables X and Y - with expected values E(X) =
Joint distribution
the population cumulants
28. (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.
Simpson's Paradox
An Elementary event
Correlation coefficient
Sampling Distribution
29. 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.
Descriptive statistics
s-algebras
An estimate of a parameter
Statistics
30. Some commonly used symbols for population parameters
Law of Parsimony
A data set
Standard error
the population mean
31. Long-term upward or downward movement over time.
A probability density function
Sample space
Step 2 of a statistical experiment
Trend
32. Statistical methods can be used for summarizing or describing a collection of data; this is called
Credence
Lurking variable
That value is the median value
descriptive statistics
33. Is a sample space over which a probability measure has been defined.
A Statistical parameter
Interval measurements
A probability space
Observational study
34. 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 Expected value
Sampling
Experimental and observational studies
Statistics
35. 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'
Conditional probability
Binary data
expected value of X
Variable
36. (cdfs) are denoted by upper case letters - e.g. F(x).
Confounded variables
experimental studies and observational studies.
An Elementary event
Cumulative distribution functions
37. Probability of accepting a false null hypothesis.
Marginal probability
Posterior probability
experimental studies and observational studies.
Beta value
38. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.
Variability
A data point
Quantitative variable
The sample space
39. 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
P-value
Block
Descriptive statistics
40. ?r
the population mean
the population cumulants
The Mean of a random variable
A data set
41. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
Standard error
Sampling frame
Correlation coefficient
42. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
quantitative variables
the population mean
the population correlation
Random variables
43. S^2
Alpha value (Level of Significance)
Nominal measurements
Trend
the population variance
44. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Seasonal effect
applied statistics
A sampling distribution
Atomic event
45. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Conditional distribution
Likert scale
Ratio measurements
A Probability measure
46. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Correlation
Inferential
Statistical dispersion
hypothesis
47. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
applied statistics
Joint distribution
Statistic
Valid measure
48. Is the length of the smallest interval which contains all the data.
The Range
A Probability measure
Random variables
f(z) - and its cdf by F(z).
49. 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
Joint distribution
Step 3 of a statistical experiment
Count data
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
50. 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
Mutual independence
the population cumulants
Confounded variables
s-algebras