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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. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Atomic event
That value is the median value
hypotheses
2. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Qualitative variable
categorical variables
Statistics
A sample
3. (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.
The Mean of a random variable
Simple random sample
Joint probability
An Elementary event
4. Statistical methods can be used for summarizing or describing a collection of data; this is called
Particular realizations of a random variable
descriptive statistics
Random variables
Experimental and observational studies
5. The probability of correctly detecting a false null hypothesis.
the population mean
Inferential statistics
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Power of a test
6. Is that part of a population which is actually observed.
A sample
Cumulative distribution functions
Posterior probability
the population mean
7. Probability of accepting a false null hypothesis.
Random variables
That value is the median value
Conditional probability
Beta value
8. Are usually written in upper case roman letters: X - Y - etc.
An event
Credence
Random variables
Sampling Distribution
9. In particular - the pdf of the standard normal distribution is denoted by
Skewness
Probability
P-value
f(z) - and its cdf by F(z).
10. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Marginal probability
Statistical inference
Sample space
11. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Alpha value (Level of Significance)
Probability density functions
Ordinal measurements
Estimator
12. 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.
expected value of X
Independence or Statistical independence
The median value
Descriptive
13. When there is an even number of values...
Marginal distribution
the population correlation
applied statistics
That is the median value
14. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Sampling Distribution
Random variables
the sample or population mean
A probability distribution
15. Is a sample space over which a probability measure has been defined.
Estimator
A probability space
Variability
Seasonal effect
16. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Probability and statistics
Binomial experiment
Statistical inference
Statistical adjustment
17. 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).
Estimator
Joint probability
applied statistics
Parameter
18. 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.
Inferential statistics
Outlier
Statistics
nominal - ordinal - interval - and ratio
19. 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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20. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.
That value is the median value
Statistical inference
Credence
Qualitative variable
21. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit
Probability density
Independent Selection
Step 2 of a statistical experiment
Probability
22. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
Observational study
Joint probability
Individual
23. Two variables such that their effects on the response variable cannot be distinguished from each other.
That value is the median value
Conditional probability
experimental studies and observational studies.
Confounded variables
24. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Lurking variable
Simpson's Paradox
Statistical dispersion
Experimental and observational studies
25. The proportion of the explained variation by a linear regression model in the total variation.
descriptive statistics
Divide the sum by the number of values.
Statistic
Coefficient of determination
26. To find the average - or arithmetic mean - of a set of numbers:
Divide the sum by the number of values.
A sampling distribution
Sampling frame
Greek letters
27. 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.
Independent Selection
inferential statistics
Simple random sample
Seasonal effect
28. 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.
Inferential statistics
The variance of a random variable
Marginal distribution
Binary data
29. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Atomic event
A likelihood function
Inferential
Skewness
30. 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.
A data set
Dependent Selection
Posterior probability
That value is the median value
31. Long-term upward or downward movement over time.
Trend
The median value
Random variables
inferential statistics
32. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Block
Binomial experiment
A sample
The median value
33. 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
Marginal probability
Statistical inference
Step 3 of a statistical experiment
Step 1 of a statistical experiment
34. Var[X] :
The sample space
Cumulative distribution functions
Probability and statistics
variance of X
35. A numerical measure that describes an aspect of a sample.
Probability
Probability density functions
Statistic
Bias
36. 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
Observational study
Confounded variables
covariance of X and Y
Interval measurements
37. 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.
Independent Selection
Random variables
Probability density
A population or statistical population
38. (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
Lurking variable
Type I errors & Type II errors
A likelihood function
the population mean
39. Is the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.
Residuals
Marginal probability
Law of Large Numbers
Statistical inference
40. 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
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Observational study
the sample or population mean
hypothesis
41. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Greek letters
The sample space
covariance of X and Y
A sample
42. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
Statistical adjustment
The average - or arithmetic mean
s-algebras
Count data
43. S^2
Quantitative variable
applied statistics
Confounded variables
the population variance
44. A measure that is relevant or appropriate as a representation of that property.
Interval measurements
Valid measure
Count data
methods of least squares
45. Are simply two different terms for the same thing. Add the given values
Experimental and observational studies
Type 1 Error
Average and arithmetic mean
Sampling frame
46. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Joint distribution
Outlier
A Random vector
A probability space
47. 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
The Mean of a random variable
Sample space
Trend
Inferential statistics
48. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Likert scale
Random variables
A population or statistical population
Quantitative variable
49. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
Step 3 of a statistical experiment
The standard deviation
A probability space
50. Another name for elementary event.
Atomic event
the population mean
the population cumulants
Reliable measure