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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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clep
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math
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. Describes a characteristic of an individual to be measured or observed.
Variable
Law of Large Numbers
Divide the sum by the number of values.
Qualitative variable
2. A subjective estimate of probability.
Credence
Marginal probability
Residuals
descriptive statistics
3. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Sampling frame
Dependent Selection
Random variables
A Random vector
4. A variable describes an individual by placing the individual into a category or a group.
Sampling
Qualitative variable
Independence or Statistical independence
experimental studies and observational studies.
5. Any specific experimental condition applied to the subjects
Independent Selection
Treatment
A random variable
Probability density
6. 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
the sample or population mean
Step 1 of a statistical experiment
Correlation
Observational study
7. Is a parameter that indexes a family of probability distributions.
Inferential statistics
Type I errors & Type II errors
Likert scale
A Statistical parameter
8. 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
Placebo effect
Probability density
Count data
Sample space
9.
Statistical dispersion
An experimental study
A Probability measure
the population mean
10. 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
Sampling frame
Step 3 of a statistical experiment
An event
inferential statistics
11. Of a group of numbers is the center point of all those number values.
experimental studies and observational studies.
The average - or arithmetic mean
Independent Selection
Posterior probability
12. 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
Step 3 of a statistical experiment
Binary data
Placebo effect
A probability distribution
13. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Marginal distribution
P-value
A sampling distribution
Variability
14. 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
Simple random sample
Skewness
The standard deviation
Credence
15. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
Standard error
Probability density
Sample space
16. Gives the probability of events in a probability space.
Valid measure
categorical variables
A Probability measure
Independent Selection
17. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the
Greek letters
A Statistical parameter
Probability
Law of Large Numbers
18. 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
Coefficient of determination
A probability distribution
Credence
Step 2 of a statistical experiment
19. Probability of accepting a false null hypothesis.
Beta value
Statistic
Descriptive
A likelihood function
20. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
descriptive statistics
Bias
A statistic
Nominal measurements
21. Is its expected value. The mean (or sample mean of a data set is just the average value.
Marginal distribution
The Mean of a random variable
Average and arithmetic mean
Type I errors & Type II errors
22. Failing to reject a false null hypothesis.
Count data
A Statistical parameter
quantitative variables
Type 2 Error
23. Another name for elementary event.
Type I errors
Pairwise independence
Atomic event
categorical variables
24. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
A statistic
Probability and statistics
Lurking variable
Law of Large Numbers
25. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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26. The probability of correctly detecting a false null hypothesis.
Power of a test
Statistical inference
Prior probability
Type 2 Error
27. 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.
Reliable measure
the population mean
Kurtosis
the population mean
28. 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.
expected value of X
Estimator
A probability density function
Binary data
29. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re
The Range
The Expected value
A likelihood function
variance of X
30. Gives the probability distribution for a continuous random variable.
Ordinal measurements
Variable
A probability density function
Law of Parsimony
31. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are
Descriptive statistics
Type I errors & Type II errors
The median value
Law of Parsimony
32. 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.
A data set
A random variable
s-algebras
Lurking variable
33. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Confounded variables
The standard deviation
Conditional probability
34. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
quantitative variables
Variability
A likelihood function
Power of a test
35. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Confounded variables
Inferential
Sample space
the population cumulants
36. 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
Simple random sample
Trend
A probability distribution
37. ?r
the sample or population mean
Law of Large Numbers
the population cumulants
Individual
38. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
A likelihood function
The Range
Trend
Joint distribution
39. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
Parameter
The Covariance between two random variables X and Y - with expected values E(X) =
Placebo effect
A data point
40. 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
Bias
Independence or Statistical independence
applied statistics
Atomic event
41. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
A Distribution function
variance of X
Probability
Probability density functions
42. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Simpson's Paradox
Ordinal measurements
Dependent Selection
Statistical adjustment
43. 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.
The variance of a random variable
Binary data
Joint distribution
Kurtosis
44. Are usually written in upper case roman letters: X - Y - etc.
Random variables
Residuals
P-value
the population correlation
45. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
the population cumulants
A probability distribution
Pairwise independence
46. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
A probability space
Ordinal measurements
Placebo effect
Marginal probability
47. Is a sample space over which a probability measure has been defined.
The median value
A probability space
Prior probability
Type II errors
48. 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.
variance of X
inferential statistics
Cumulative distribution functions
49. 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.
The median value
covariance of X and Y
A probability space
Dependent Selection
50. 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
Null hypothesis
Simple random sample
Nominal measurements
Joint distribution
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