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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. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
Independent Selection
Divide the sum by the number of values.
s-algebras
2. Another name for elementary event.
Block
Step 1 of a statistical experiment
A sample
Atomic event
3. A list of individuals from which the sample is actually selected.
Treatment
A random variable
Sampling frame
variance of X
4. 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.
A Distribution function
Correlation coefficient
Estimator
Skewness
5. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.
hypotheses
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Qualitative variable
Kurtosis
6. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Residuals
Bias
Placebo effect
nominal - ordinal - interval - and ratio
7. A variable describes an individual by placing the individual into a category or a group.
Skewness
Qualitative variable
Marginal probability
A Distribution function
8. Is a sample space over which a probability measure has been defined.
Lurking variable
Descriptive
A probability space
Sampling Distribution
9. Is data arising from counting that can take only non-negative integer values.
Sampling Distribution
An experimental study
Count data
Credence
10. 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
hypothesis
A probability space
Bias
Ratio measurements
11. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Law of Parsimony
Count data
Bias
categorical variables
12. Are simply two different terms for the same thing. Add the given values
Coefficient of determination
Kurtosis
Particular realizations of a random variable
Average and arithmetic mean
13. (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
Variability
Alpha value (Level of Significance)
Independent Selection
14. Are usually written in upper case roman letters: X - Y - etc.
Random variables
Joint probability
Nominal measurements
Law of Parsimony
15. Some commonly used symbols for population parameters
Standard error
the population mean
Sampling
Random variables
16. Data are gathered and correlations between predictors and response are investigated.
observational study
Trend
Joint probability
That value is the median value
17. 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
Trend
Type 2 Error
Step 1 of a statistical experiment
Placebo effect
18. (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
A likelihood function
Coefficient of determination
Prior probability
The Mean of a random variable
19. The probability of correctly detecting a false null hypothesis.
Power of a test
Binary data
Simple random sample
Independent Selection
20. 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
Particular realizations of a random variable
Null hypothesis
Statistical dispersion
The variance of a random variable
21. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
An event
Joint distribution
Valid measure
Seasonal effect
22. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
categorical variables
A Statistical parameter
A statistic
hypotheses
23. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.
Statistical dispersion
A data set
An experimental study
Standard error
24. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
Likert scale
Joint distribution
Interval measurements
25. Rejecting a true null hypothesis.
Type 1 Error
Marginal probability
A probability space
That value is the median value
26. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
inferential statistics
The standard deviation
Ordinal measurements
27. 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
A Probability measure
experimental studies and observational studies.
Parameter
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
28. Is the length of the smallest interval which contains all the data.
Probability density functions
The Range
Lurking variable
Statistical adjustment
29. 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.
Independence or Statistical independence
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Outlier
Statistics
30. Failing to reject a false null hypothesis.
the population correlation
A random variable
Descriptive
Type 2 Error
31. When there is an even number of values...
the sample or population mean
Bias
Cumulative distribution functions
That is the median value
32. 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
Valid measure
Step 2 of a statistical experiment
A likelihood function
The average - or arithmetic mean
33. A subjective estimate of probability.
Skewness
observational study
Credence
Probability density functions
34. Statistical methods can be used for summarizing or describing a collection of data; this is called
Parameter - or 'statistical parameter'
descriptive statistics
Kurtosis
Credence
35. To find the average - or arithmetic mean - of a set of numbers:
Step 2 of a statistical experiment
Step 1 of a statistical experiment
Divide the sum by the number of values.
Nominal measurements
36. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
Variable
Sampling
expected value of X
37. 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)
Correlation
Interval measurements
A Statistical parameter
An event
38. 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.
applied statistics
Dependent Selection
The variance of a random variable
Probability density functions
39. 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.
Joint distribution
Parameter
Experimental and observational studies
Type I errors
40. Is a parameter that indexes a family of probability distributions.
Mutual independence
A sampling distribution
A Statistical parameter
Power of a test
41. 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.
A data point
Independent Selection
That is the median value
Estimator
42. In number theory - scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns - which may then lead to
hypotheses
The median value
A data point
Inferential
43. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Type 2 Error
An estimate of a parameter
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Random variables
44. A measurement such that the random error is small
Conditional distribution
Reliable measure
Probability density functions
Statistical adjustment
45. 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
Mutual independence
Reliable measure
Residuals
Particular realizations of a random variable
46. Is its expected value. The mean (or sample mean of a data set is just the average value.
Simple random sample
A likelihood function
The Mean of a random variable
The variance of a random variable
47. Gives the probability of events in a probability space.
the population cumulants
A Probability measure
A statistic
Type 1 Error
48. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
A Probability measure
A likelihood function
Skewness
Descriptive
49. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Cumulative distribution functions
Marginal probability
Beta value
Binomial experiment
50. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Placebo effect
Qualitative variable
Sampling frame
Law of Large Numbers