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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. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
The average - or arithmetic mean
Average and arithmetic mean
Seasonal effect
2. ?r
Statistic
the population cumulants
the population mean
Treatment
3. 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.
Inferential statistics
Dependent Selection
Seasonal effect
A probability distribution
4. Of a group of numbers is the center point of all those number values.
Cumulative distribution functions
The average - or arithmetic mean
Probability and statistics
Sampling
5. Is a parameter that indexes a family of probability distributions.
Joint probability
The median value
the population cumulants
A Statistical parameter
6. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
An Elementary event
Binary data
Mutual independence
7. A subjective estimate of probability.
observational study
Credence
Sampling Distribution
Probability density
8. 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
Count data
A Random vector
Simple random sample
9. Is defined as the expected value of random variable (X -
The Mean of a random variable
Correlation coefficient
The Covariance between two random variables X and Y - with expected values E(X) =
P-value
10. A variable describes an individual by placing the individual into a category or a group.
A likelihood function
Mutual independence
Coefficient of determination
Qualitative variable
11. To find the average - or arithmetic mean - of a set of numbers:
Reliable measure
Independent Selection
Divide the sum by the number of values.
Descriptive statistics
12. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Pairwise independence
An Elementary event
Variable
Statistical dispersion
13. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
An experimental study
A Random vector
Ordinal measurements
Prior probability
14. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Posterior probability
Joint distribution
An experimental study
Descriptive statistics
15. Is data arising from counting that can take only non-negative integer values.
Count data
Descriptive statistics
Sampling frame
Step 1 of a statistical experiment
16. 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
Experimental and observational studies
Likert scale
Step 3 of a statistical experiment
descriptive statistics
17. 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.
Bias
Standard error
Random variables
Prior probability
18. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Step 3 of a statistical experiment
Sampling
Individual
variance of X
19. 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
Statistics
applied statistics
Parameter
20. 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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21. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P
observational study
variance of X
Correlation
Type 1 Error
22. A data value that falls outside the overall pattern of the graph.
The Mean of a random variable
Outlier
Lurking variable
The variance of a random variable
23. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
P-value
inferential statistics
nominal - ordinal - interval - and ratio
observational study
24. Is its expected value. The mean (or sample mean of a data set is just the average value.
Joint distribution
A likelihood function
The Mean of a random variable
A data point
25. 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.
Simpson's Paradox
Interval measurements
Type I errors
The median value
26. 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).
Descriptive
Power of a test
Joint probability
Conditional distribution
27. S^2
the population variance
Ordinal measurements
Type 1 Error
Confounded variables
28. 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)
The variance of a random variable
Statistical inference
Credence
Interval measurements
29. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Sampling Distribution
methods of least squares
Probability density functions
the population mean
30. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Sampling frame
The median value
Binomial experiment
expected value of X
31. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
A probability density function
Power of a test
Likert scale
A probability space
32. 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
the population cumulants
That is the median value
Descriptive statistics
applied statistics
33. The standard deviation of a sampling distribution.
Standard error
The median value
A data set
Mutual independence
34. Some commonly used symbols for sample statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Inferential
Descriptive statistics
A sampling distribution
35. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
expected value of X
Block
Pairwise independence
Sample space
36. A measurement such that the random error is small
covariance of X and Y
Inferential statistics
Reliable measure
Interval measurements
37. The probability of correctly detecting a false null hypothesis.
Average and arithmetic mean
the population mean
Alpha value (Level of Significance)
Power of a test
38. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.
Divide the sum by the number of values.
The sample space
Sample space
Atomic event
39. Is a sample space over which a probability measure has been defined.
Credence
A probability space
experimental studies and observational studies.
A Distribution function
40. 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
The Mean of a random variable
A statistic
Null hypothesis
Outlier
41. Are usually written in upper case roman letters: X - Y - etc.
Random variables
Seasonal effect
the population cumulants
Null hypothesis
42. Describes a characteristic of an individual to be measured or observed.
Parameter - or 'statistical parameter'
An event
Cumulative distribution functions
Variable
43. (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 Expected value
Marginal probability
The average - or arithmetic mean
Treatment
44. The proportion of the explained variation by a linear regression model in the total variation.
A data set
Conditional probability
Coefficient of determination
Sampling
45. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.
Parameter - or 'statistical parameter'
Simpson's Paradox
A Distribution function
A sample
46. The collection of all possible outcomes in an experiment.
Sample space
Law of Large Numbers
Trend
Type II errors
47. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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48. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Conditional probability
An experimental study
Greek letters
Probability density functions
49. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
Average and arithmetic mean
Individual
Posterior probability
50. 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 I errors & Type II errors
Mutual independence
Coefficient of determination
Count data