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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. Is that part of a population which is actually observed.
covariance of X and Y
A sample
An Elementary event
Probability density
2. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
An event
Particular realizations of a random variable
Null hypothesis
Law of Large Numbers
3. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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4. 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.
Marginal probability
the population correlation
Alpha value (Level of Significance)
variance of X
5. Failing to reject a false null hypothesis.
Inferential
Type 2 Error
Binomial experiment
hypothesis
6. (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
the population mean
A random variable
7. 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
A Statistical parameter
Alpha value (Level of Significance)
Step 1 of a statistical experiment
The Mean of a random variable
8. 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.
The Mean of a random variable
Treatment
Divide the sum by the number of values.
That value is the median value
9. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Law of Large Numbers
Sample space
Statistics
10. Is the probability distribution - under repeated sampling of the population - of a given statistic.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Type 1 Error
A sampling distribution
Joint distribution
11. E[X] :
expected value of X
Parameter - or 'statistical parameter'
Type 1 Error
the sample or population mean
12. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
hypotheses
Divide the sum by the number of values.
applied statistics
The Range
13. 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.
The Range
the population mean
Bias
Null hypothesis
14. Long-term upward or downward movement over time.
Interval measurements
experimental studies and observational studies.
A Probability measure
Trend
15. Is a sample space over which a probability measure has been defined.
Statistics
A probability space
Type I errors
Variable
16. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
That is the median value
Likert scale
A Distribution function
The standard deviation
17. A variable describes an individual by placing the individual into a category or a group.
The standard deviation
Sampling
A statistic
Qualitative variable
18. 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
Trend
A statistic
Null hypothesis
categorical variables
19. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Average and arithmetic mean
A data set
Placebo effect
Statistical dispersion
20. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
P-value
Average and arithmetic mean
methods of least squares
21. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
variance of X
Random variables
Quantitative variable
quantitative variables
22. In particular - the pdf of the standard normal distribution is denoted by
A Statistical parameter
f(z) - and its cdf by F(z).
hypothesis
Ordinal measurements
23. 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.
Mutual independence
Estimator
The median value
Law of Parsimony
24. Probability of rejecting a true null hypothesis.
A statistic
Divide the sum by the number of values.
Coefficient of determination
Alpha value (Level of Significance)
25. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.
Independent Selection
Step 3 of a statistical experiment
Simple random sample
Treatment
26. ?
Residuals
the population correlation
Kurtosis
Conditional probability
27. 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.
Experimental and observational studies
Conditional distribution
Cumulative distribution functions
Probability density functions
28. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Bias
Posterior probability
Parameter
Prior probability
29. 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.
Variable
Coefficient of determination
Statistical inference
30. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.
Statistical dispersion
Qualitative variable
Descriptive
Estimator
31. 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
Pairwise independence
Correlation
Independence or Statistical independence
the population correlation
32. Statistical methods can be used for summarizing or describing a collection of data; this is called
The Covariance between two random variables X and Y - with expected values E(X) =
Sampling Distribution
descriptive statistics
P-value
33. The probability of correctly detecting a false null hypothesis.
Joint probability
Inferential
the population cumulants
Power of a test
34. 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.
hypotheses
the population variance
A sample
Statistics
35. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Marginal distribution
Sampling Distribution
Ratio measurements
A probability density function
36. 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.
Dependent Selection
A data set
Probability density functions
A probability distribution
37. Data are gathered and correlations between predictors and response are investigated.
A random variable
categorical variables
The Covariance between two random variables X and Y - with expected values E(X) =
observational study
38. Is defined as the expected value of random variable (X -
Binary data
A probability density function
Parameter
The Covariance between two random variables X and Y - with expected values E(X) =
39. A data value that falls outside the overall pattern of the graph.
Nominal measurements
Outlier
Interval measurements
f(z) - and its cdf by F(z).
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
Count data
Inferential
hypothesis
Individual
41. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
A data point
the population mean
Individual
The Expected value
42. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
Pairwise independence
Statistical dispersion
Cumulative distribution functions
43. 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
Divide the sum by the number of values.
Observational study
the population mean
Beta value
44. 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
Type 2 Error
A probability distribution
A statistic
Mutual independence
45. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
the sample or population mean
Parameter - or 'statistical parameter'
Interval measurements
Step 3 of a statistical experiment
46. 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).
Joint probability
Estimator
Inferential
A Probability measure
47. Of a group of numbers is the center point of all those number values.
Average and arithmetic mean
Quantitative variable
An experimental study
The average - or arithmetic mean
48. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
the population mean
Average and arithmetic mean
methods of least squares
49. 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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50. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
descriptive statistics
Treatment
A Random vector
Confounded variables