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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. 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
The average - or arithmetic mean
Type II errors
Mutual independence
A random variable
2. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Step 2 of a statistical experiment
Type 2 Error
Posterior probability
A Distribution function
3. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
A probability space
Conditional distribution
categorical variables
Sampling frame
4. A list of individuals from which the sample is actually selected.
Valid measure
Independent Selection
Step 1 of a statistical experiment
Sampling frame
5. Any specific experimental condition applied to the subjects
Conditional probability
Treatment
Prior probability
Power of a test
6. Is a sample space over which a probability measure has been defined.
Greek letters
Pairwise independence
A probability space
The Expected value
7. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Type I errors & Type II errors
That value is the median value
Residuals
Variable
8. The probability of correctly detecting a false null hypothesis.
Power of a test
The Covariance between two random variables X and Y - with expected values E(X) =
That value is the median value
the population mean
9. 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
inferential statistics
Trend
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
A Distribution function
10. To find the average - or arithmetic mean - of a set of numbers:
The Range
Dependent Selection
Divide the sum by the number of values.
Simpson's Paradox
11. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
Descriptive
That value is the median value
Statistical inference
12. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
The variance of a random variable
Probability density functions
observational study
Beta value
13. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Reliable measure
Conditional probability
expected value of X
A Random vector
14. Is the length of the smallest interval which contains all the data.
The Range
Ordinal measurements
An event
Count data
15. 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}.
Statistical inference
Joint probability
The sample space
Type I errors
16. Of a group of numbers is the center point of all those number values.
Conditional probability
Inferential
Sampling Distribution
The average - or arithmetic mean
17. A numerical facsimilie or representation of a real-world phenomenon.
Quantitative variable
Experimental and observational studies
Coefficient of determination
Simulation
18. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
That is the median value
applied statistics
Skewness
19. Is the exact middle value of a set of numbers Arrange the numbers in numerical order. Find the value in the middle of the list.
Interval measurements
The median value
Likert scale
Pairwise independence
20. Is a parameter that indexes a family of probability distributions.
The average - or arithmetic mean
variance of X
Statistic
A Statistical parameter
21. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A statistic
s-algebras
An event
A probability space
22. S^2
the population variance
A Probability measure
A probability density function
Type I errors
23. The proportion of the explained variation by a linear regression model in the total variation.
Statistic
Average and arithmetic mean
Coefficient of determination
Statistics
24. Gives the probability distribution for a continuous random variable.
Marginal probability
Step 2 of a statistical experiment
A probability density function
Observational study
25. 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)
quantitative variables
The standard deviation
Interval measurements
A probability density function
26. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Random variables
A Probability measure
Pairwise independence
the population mean
27. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
the population variance
Simple random sample
Bias
That is the median value
28. Data are gathered and correlations between predictors and response are investigated.
Alpha value (Level of Significance)
Sampling frame
observational study
Sampling Distribution
29. 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.
Observational study
The variance of a random variable
Binary data
A probability space
30. (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.
Law of Large Numbers
Divide the sum by the number of values.
A probability space
An Elementary event
31. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Particular realizations of a random variable
Pairwise independence
Type I errors & Type II errors
Confounded variables
32. A measurement such that the random error is small
Reliable measure
Sample space
Treatment
Sampling frame
33. 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).
descriptive statistics
Posterior probability
An event
Joint probability
34. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
A statistic
the sample or population mean
Probability density
Alpha value (Level of Significance)
35. 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
applied statistics
Treatment
Observational study
That value is the median value
36. The collection of all possible outcomes in an experiment.
Qualitative variable
Sample space
Posterior probability
A likelihood function
37. 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
Correlation
Lurking variable
The Expected value
Descriptive statistics
38. ?r
Type I errors
covariance of X and Y
Sample space
the population cumulants
39. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
A data set
quantitative variables
Ordinal measurements
expected value of X
40. 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.
Statistical inference
the population cumulants
Probability and statistics
Marginal distribution
41. Is that part of a population which is actually observed.
Greek letters
applied statistics
Marginal distribution
A sample
42. 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
applied statistics
Dependent Selection
Parameter - or 'statistical parameter'
43. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Trend
variance of X
The standard deviation
Inferential
44. 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.
Seasonal effect
Placebo effect
Step 1 of a statistical experiment
Correlation coefficient
45. 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
Ratio measurements
Type I errors
A Probability measure
Statistical inference
46. 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.
Individual
Standard error
applied statistics
Bias
47. 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.
Sampling frame
Inferential statistics
Lurking variable
An estimate of a parameter
48. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Law of Parsimony
Lurking variable
applied statistics
An experimental study
49. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Pairwise independence
Qualitative variable
Simpson's Paradox
A sampling distribution
50. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
Count data
Outlier
s-algebras
Descriptive statistics