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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. Are usually written in upper case roman letters: X - Y - etc.
Random variables
Standard error
Lurking variable
Probability density
2. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
An experimental study
Parameter - or 'statistical parameter'
Particular realizations of a random variable
3. 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.
Statistical dispersion
Average and arithmetic mean
That value is the median value
A likelihood function
4. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
the population mean
An estimate of a parameter
Statistic
s-algebras
5. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Simulation
Type I errors
the sample or population mean
Bias
6. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
A probability density function
variance of X
Joint distribution
Atomic event
7. Have imprecise differences between consecutive values - but have a meaningful order to those values
Variability
Binary data
A Probability measure
Ordinal measurements
8. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
categorical variables
Individual
A likelihood function
A sampling distribution
9. To find the average - or arithmetic mean - of a set of numbers:
quantitative variables
A Distribution function
Valid measure
Divide the sum by the number of values.
10. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
Null hypothesis
Alpha value (Level of Significance)
A probability density function
11. 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
Outlier
Pairwise independence
Parameter - or 'statistical parameter'
Independence or Statistical independence
12. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
nominal - ordinal - interval - and ratio
Parameter
Pairwise independence
experimental studies and observational studies.
13. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Parameter
Count data
Quantitative variable
quantitative variables
14. Var[X] :
Nominal measurements
variance of X
Type 1 Error
Simulation
15. A numerical measure that describes an aspect of a sample.
Statistic
Cumulative distribution functions
Power of a test
Nominal measurements
16. Is that part of a population which is actually observed.
Residuals
Marginal distribution
A sample
Step 1 of a statistical experiment
17. Some commonly used symbols for sample statistics
The standard deviation
methods of least squares
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Joint distribution
18. 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
Simple random sample
An estimate of a parameter
Observational study
Step 3 of a statistical experiment
19. ?
An estimate of a parameter
the population correlation
hypotheses
Marginal probability
20. 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
Descriptive
Skewness
Step 1 of a statistical experiment
Ordinal measurements
21. Is data arising from counting that can take only non-negative integer values.
Count data
Step 1 of a statistical experiment
Type II errors
descriptive statistics
22. Are simply two different terms for the same thing. Add the given values
Residuals
The average - or arithmetic mean
Average and arithmetic mean
the population mean
23. Is denoted by - pronounced 'x bar'.
Statistical inference
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
A random variable
Divide the sum by the number of values.
24. Is data that can take only two values - usually represented by 0 and 1.
Binary data
Parameter
categorical variables
Quantitative variable
25.
the population mean
f(z) - and its cdf by F(z).
Parameter - or 'statistical parameter'
An estimate of a parameter
26. A list of individuals from which the sample is actually selected.
the population variance
expected value of X
Sampling frame
Credence
27. The proportion of the explained variation by a linear regression model in the total variation.
Coefficient of determination
methods of least squares
descriptive statistics
Marginal distribution
28. Another name for elementary event.
Lurking variable
Atomic event
Block
Type 1 Error
29. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A population or statistical population
Binomial experiment
A Random vector
Treatment
30. 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
An estimate of a parameter
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
hypotheses
observational study
31. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
A Statistical parameter
the population mean
Inferential statistics
Residuals
32. Any specific experimental condition applied to the subjects
Atomic event
the sample or population mean
Treatment
The average - or arithmetic mean
33. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
applied statistics
covariance of X and Y
Sample space
Dependent Selection
34. Long-term upward or downward movement over time.
Variability
Skewness
Trend
Quantitative variable
35. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
A Random vector
Sampling Distribution
Confounded variables
Binary data
36. Two variables such that their effects on the response variable cannot be distinguished from each other.
Particular realizations of a random variable
Confounded variables
hypotheses
Probability
37. 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
experimental studies and observational studies.
Simulation
Null hypothesis
inferential statistics
38. The standard deviation of a sampling distribution.
Bias
Ratio measurements
Standard error
That value is the median value
39. 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)
Binary data
Inferential statistics
Greek letters
Interval measurements
40. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A statistic
Type 2 Error
Beta value
Law of Parsimony
41. Many statistical methods seek to minimize the mean-squared error - and these are called
Credence
Law of Large Numbers
Bias
methods of least squares
42. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Quantitative variable
That is the median value
Inferential statistics
Probability density functions
43. Data are gathered and correlations between predictors and response are investigated.
The sample space
Treatment
observational study
Statistics
44. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
applied statistics
P-value
quantitative variables
Greek letters
45. 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
Descriptive statistics
Variability
A Statistical parameter
46. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Statistical adjustment
Qualitative variable
Inferential
Marginal probability
47. S^2
the population variance
descriptive statistics
Type I errors & Type II errors
the population cumulants
48. (cdfs) are denoted by upper case letters - e.g. F(x).
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Cumulative distribution functions
Ordinal measurements
Independence or Statistical independence
49. 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
Mutual independence
Correlation coefficient
Law of Parsimony
50. E[X] :
s-algebras
Conditional probability
expected value of X
The variance of a random variable