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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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clep
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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. Many statistical methods seek to minimize the mean-squared error - and these are called
An Elementary event
the population mean
methods of least squares
Qualitative variable
2. Any specific experimental condition applied to the subjects
quantitative variables
The Mean of a random variable
covariance of X and Y
Treatment
3. 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.
covariance of X and Y
Step 3 of a statistical experiment
The variance of a random variable
Treatment
4. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
Trend
Descriptive statistics
s-algebras
Residuals
5. 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 median value
Type 1 Error
Independent Selection
Skewness
6. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Simpson's Paradox
A Random vector
categorical variables
hypothesis
7. 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
Inferential
Block
A Distribution function
Observational study
8. 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
experimental studies and observational studies.
hypothesis
Standard error
Variability
9. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a
A statistic
Step 2 of a statistical experiment
the population variance
Alpha value (Level of Significance)
10. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
inferential statistics
Statistical adjustment
Particular realizations of a random variable
Seasonal effect
11. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
Mutual independence
A Statistical parameter
Conditional probability
12. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Statistical adjustment
variance of X
A probability distribution
methods of least squares
13. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Block
Particular realizations of a random variable
The standard deviation
Marginal distribution
14. A variable describes an individual by placing the individual into a category or a group.
Qualitative variable
Probability and statistics
quantitative variables
Trend
15. 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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16. 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
Independence or Statistical independence
Placebo effect
The average - or arithmetic mean
applied statistics
17. Another name for elementary event.
Simple random sample
A statistic
Atomic event
descriptive statistics
18. 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
Kurtosis
Correlation
Inferential statistics
Type 1 Error
19. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Confounded variables
Pairwise independence
Conditional distribution
The variance of a random variable
20. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
The Mean of a random variable
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Count data
21. The probability of correctly detecting a false null hypothesis.
Lurking variable
Treatment
Marginal probability
Power of a test
22. Is that part of a population which is actually observed.
The Expected value
Variable
A sample
inferential statistics
23. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.
Valid measure
That value is the median value
That is the median value
descriptive statistics
24. 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
The sample space
Skewness
Inferential
Probability and statistics
25. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
Seasonal effect
inferential statistics
Pairwise independence
26. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Statistical dispersion
Sampling frame
Law of Parsimony
Interval measurements
27. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
Sample space
Parameter
Statistic
28. 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
A Distribution function
Individual
Mutual independence
A population or statistical population
29. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
expected value of X
The sample space
Greek letters
Null hypothesis
30. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Type I errors
A statistic
Law of Large Numbers
hypotheses
31. 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).
Independent Selection
Average and arithmetic mean
descriptive statistics
An event
32. Of a group of numbers is the center point of all those number values.
Cumulative distribution functions
The average - or arithmetic mean
Type 1 Error
Seasonal effect
33. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a
Type I errors & Type II errors
Type I errors
Null hypothesis
Divide the sum by the number of values.
34. To find the average - or arithmetic mean - of a set of numbers:
A probability space
A probability density function
Qualitative variable
Divide the sum by the number of values.
35. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Experimental and observational studies
Type II errors
Ordinal measurements
36. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Type 1 Error
Binary data
Sampling frame
A sampling distribution
37. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
A probability distribution
Statistical adjustment
An experimental study
the population cumulants
38. Is its expected value. The mean (or sample mean of a data set is just the average value.
Parameter
Mutual independence
Probability and statistics
The Mean of a random variable
39. (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.
Type 2 Error
An Elementary event
Step 1 of a statistical experiment
Sampling Distribution
40. 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.
Sample space
Particular realizations of a random variable
Statistical dispersion
Bias
41. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Independence or Statistical independence
Marginal probability
Descriptive statistics
Individual
42. Is the length of the smallest interval which contains all the data.
the sample or population mean
The Range
An event
applied statistics
43. The collection of all possible outcomes in an experiment.
Sample space
Residuals
Step 3 of a statistical experiment
A probability distribution
44. A numerical facsimilie or representation of a real-world phenomenon.
Variable
Simulation
the population mean
A probability density function
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).
quantitative variables
Pairwise independence
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Joint probability
46. A subjective estimate of probability.
The average - or arithmetic mean
Inferential statistics
Credence
Atomic event
47. Have imprecise differences between consecutive values - but have a meaningful order to those values
categorical variables
Likert scale
Ordinal measurements
Independence or Statistical independence
48. In particular - the pdf of the standard normal distribution is denoted by
the population variance
Type I errors & Type II errors
f(z) - and its cdf by F(z).
The Expected value
49. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.
That value is the median value
A random variable
Descriptive
Dependent Selection
50. A data value that falls outside the overall pattern of the graph.
Type I errors
Outlier
Inferential
Treatment