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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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Study First
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. ?r
Seasonal effect
Step 3 of a statistical experiment
Type I errors
the population cumulants
2. The proportion of the explained variation by a linear regression model in the total variation.
Placebo effect
Null hypothesis
f(z) - and its cdf by F(z).
Coefficient of determination
3. 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).
Law of Parsimony
Joint probability
Prior probability
The variance of a random variable
4. A group of individuals sharing some common features that might affect the treatment.
Credence
Block
A Distribution function
Parameter
5. 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).
inferential statistics
Joint distribution
An event
Descriptive statistics
6. A numerical measure that assesses the strength of a linear relationship between two variables.
inferential statistics
s-algebras
An Elementary event
Correlation coefficient
7. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Qualitative variable
Type II errors
Statistical dispersion
Treatment
8. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Credence
Bias
Simpson's Paradox
hypothesis
9. Another name for elementary event.
Correlation
Random variables
Inferential statistics
Atomic event
10. 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
Sampling
Ratio measurements
expected value of X
11. Probability of rejecting a true null hypothesis.
Alpha value (Level of Significance)
An event
hypotheses
Variability
12. Have imprecise differences between consecutive values - but have a meaningful order to those values
The standard deviation
the sample or population mean
Ordinal measurements
Alpha value (Level of Significance)
13. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
nominal - ordinal - interval - and ratio
Greek letters
Cumulative distribution functions
Lurking variable
14. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Parameter
A random variable
Type 1 Error
15. 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
applied statistics
Standard error
hypothesis
Sampling Distribution
16. Long-term upward or downward movement over time.
Sampling frame
Trend
Outlier
P-value
17. Failing to reject a false null hypothesis.
Simulation
Type 2 Error
The sample space
Step 1 of a statistical experiment
18. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
Step 2 of a statistical experiment
The Covariance between two random variables X and Y - with expected values E(X) =
A probability density function
19. Var[X] :
Conditional distribution
variance of X
Marginal probability
Pairwise independence
20. The collection of all possible outcomes in an experiment.
Observational study
the population mean
Joint distribution
Sample space
21. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Prior probability
A sampling distribution
The Covariance between two random variables X and Y - with expected values E(X) =
inferential statistics
22. Have no meaningful rank order among values.
descriptive statistics
Experimental and observational studies
Conditional probability
Nominal measurements
23. To find the average - or arithmetic mean - of a set of numbers:
Kurtosis
Observational study
Divide the sum by the number of values.
the population variance
24. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
A data set
Particular realizations of a random variable
Type I errors & Type II errors
The Range
25. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
That value is the median value
covariance of X and Y
Correlation coefficient
P-value
26. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
Type 2 Error
Random variables
A probability density function
27. 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
categorical variables
Probability density
Standard error
quantitative variables
28. (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
Qualitative variable
Individual
The Expected value
Type I errors
29. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
Mutual independence
Greek letters
Correlation coefficient
30. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Probability density
Parameter
Interval measurements
the sample or population mean
31. When you have two or more competing models - choose the simpler of the two models.
Cumulative distribution functions
the population correlation
inferential statistics
Law of Parsimony
32. Describes the spread in the values of the sample statistic when many samples are taken.
P-value
Variability
Type II errors
Parameter - or 'statistical parameter'
33. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
categorical variables
Descriptive
the sample or population mean
Probability and statistics
34. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
methods of least squares
Joint distribution
Statistical dispersion
Joint probability
35. 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.
Inferential
Marginal probability
Power of a test
Cumulative distribution functions
36. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.
Alpha value (Level of Significance)
the population mean
Nominal measurements
Conditional distribution
37. S^2
Joint distribution
A sampling distribution
Ordinal measurements
the population variance
38. A numerical measure that describes an aspect of a population.
Parameter
Sampling frame
expected value of X
Individual
39. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Type I errors & Type II errors
A probability distribution
Interval measurements
Parameter
40. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
41. Some commonly used symbols for population parameters
Sampling Distribution
Nominal measurements
the population mean
A data set
42. A list of individuals from which the sample is actually selected.
Sampling frame
inferential statistics
Mutual independence
Seasonal effect
43. Is denoted by - pronounced 'x bar'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Trend
the population correlation
Statistical adjustment
44. Some commonly used symbols for sample statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Coefficient of determination
An Elementary event
Variability
45. Is data that can take only two values - usually represented by 0 and 1.
Probability density
the population correlation
Binary data
The median value
46. 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.
A data point
Null hypothesis
Experimental and observational studies
Conditional distribution
47. 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
Probability
An event
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Step 1 of a statistical experiment
48. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
A random variable
The variance of a random variable
Likert scale
Step 1 of a statistical experiment
49. Are simply two different terms for the same thing. Add the given values
Qualitative variable
the population correlation
Probability density
Average and arithmetic mean
50. Rejecting a true null hypothesis.
inferential statistics
Type 1 Error
The Mean of a random variable
P-value