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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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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. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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2. ?
Pairwise independence
The Covariance between two random variables X and Y - with expected values E(X) =
Standard error
the population correlation
3. 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.
Valid measure
Inferential statistics
Sampling
Variable
4. Is a parameter that indexes a family of probability distributions.
the population cumulants
A Statistical parameter
Sampling frame
Type 1 Error
5. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Simple random sample
categorical variables
A random variable
experimental studies and observational studies.
6. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Estimator
Binary data
Count data
An estimate of a parameter
7. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
the population variance
A population or statistical population
Bias
8. A numerical measure that describes an aspect of a sample.
Ordinal measurements
Particular realizations of a random variable
Statistic
A probability density function
9. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Marginal probability
A sample
Block
Probability density functions
10. 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.
Probability
A statistic
Reliable measure
Lurking variable
11. A subjective estimate of probability.
That value is the median value
A sample
Probability density
Credence
12. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
Alpha value (Level of Significance)
That is the median value
Lurking variable
13. When there is an even number of values...
Sampling
Step 2 of a statistical experiment
Estimator
That is the median value
14. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
s-algebras
The standard deviation
Confounded variables
15. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Cumulative distribution functions
Bias
Prior probability
Independent Selection
16. 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.
s-algebras
The median value
Type 1 Error
A data point
17. Another name for elementary event.
The median value
Skewness
Type 1 Error
Atomic event
18. In particular - the pdf of the standard normal distribution is denoted by
the sample or population mean
Quantitative variable
Simpson's Paradox
f(z) - and its cdf by F(z).
19. 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
Outlier
Mutual independence
The Covariance between two random variables X and Y - with expected values E(X) =
Inferential statistics
20. S^2
Alpha value (Level of Significance)
Kurtosis
Probability and statistics
the population variance
21. 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}.
Divide the sum by the number of values.
The sample space
Joint distribution
Ratio measurements
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
Parameter
the population mean
Type 2 Error
Step 3 of a statistical experiment
23. A measurement such that the random error is small
Reliable measure
Average and arithmetic mean
Seasonal effect
the population cumulants
24. A data value that falls outside the overall pattern of the graph.
Alpha value (Level of Significance)
Outlier
Sampling
Bias
25. A variable describes an individual by placing the individual into a category or a group.
Conditional probability
Bias
Qualitative variable
Descriptive statistics
26. 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
Prior probability
Experimental and observational studies
methods of least squares
Step 1 of a statistical experiment
27. 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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28. 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.
f(z) - and its cdf by F(z).
Dependent Selection
A sampling distribution
Experimental and observational studies
29. Describes a characteristic of an individual to be measured or observed.
Variable
Bias
Null hypothesis
Posterior probability
30. 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.
the population mean
Independent Selection
Type I errors & Type II errors
Average and arithmetic mean
31. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data
Bias
Nominal measurements
The Range
Ratio measurements
32. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
Statistics
Statistical dispersion
A statistic
33. Is a sample and the associated data points.
Parameter - or 'statistical parameter'
A data set
Beta value
Power of a test
34. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
f(z) - and its cdf by F(z).
hypotheses
A likelihood function
35. 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
Independence or Statistical independence
applied statistics
The Covariance between two random variables X and Y - with expected values E(X) =
36. (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
variance of X
Skewness
hypothesis
The Expected value
37. Are usually written in upper case roman letters: X - Y - etc.
Type I errors
Particular realizations of a random variable
Residuals
Random variables
38. Some commonly used symbols for sample statistics
A Distribution function
An estimate of a parameter
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
methods of least squares
39. A group of individuals sharing some common features that might affect the treatment.
Block
Individual
the population cumulants
Pairwise independence
40. Describes the spread in the values of the sample statistic when many samples are taken.
Power of a test
Variability
Count data
observational study
41. Some commonly used symbols for population parameters
the population mean
Descriptive statistics
A Random vector
A random variable
42. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.
Step 1 of a statistical experiment
Ordinal measurements
Seasonal effect
Descriptive statistics
43. 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.
Coefficient of determination
A probability space
A population or statistical population
The Expected value
44. (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
methods of least squares
Nominal measurements
A likelihood function
Particular realizations of a random variable
45. Var[X] :
covariance of X and Y
variance of X
A probability density function
Law of Parsimony
46. The standard deviation of a sampling distribution.
the population mean
Correlation
Individual
Standard error
47. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no
Probability and statistics
A probability distribution
Trend
A Random vector
48. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)
Interval measurements
Type 2 Error
f(z) - and its cdf by F(z).
Simple random sample
49. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
observational study
Probability density functions
P-value
50. Gives the probability distribution for a continuous random variable.
A probability density function
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
hypotheses
Beta value