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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.
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. 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.
Law of Large Numbers
Dependent Selection
The Range
Likert scale
2. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
An estimate of a parameter
An Elementary event
Probability
Sampling Distribution
3. A group of individuals sharing some common features that might affect the treatment.
A statistic
Simpson's Paradox
Variability
Block
4. Many statistical methods seek to minimize the mean-squared error - and these are called
Correlation coefficient
methods of least squares
Reliable measure
Bias
5. S^2
Null hypothesis
the population variance
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Statistical dispersion
6. Gives the probability distribution for a continuous random variable.
A probability density function
The Expected value
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
observational study
7. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Null hypothesis
The median value
Block
the sample or population mean
8. The collection of all possible outcomes in an experiment.
Sample space
Probability and statistics
the population mean
Probability density
9. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Variability
Joint probability
Valid measure
nominal - ordinal - interval - and ratio
10. The standard deviation of a sampling distribution.
Type 1 Error
Type 2 Error
Standard error
Independent Selection
11. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
methods of least squares
Descriptive
quantitative variables
That value is the median value
12. 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}.
the population correlation
covariance of X and Y
Outlier
The sample space
13. Failing to reject a false null hypothesis.
Type 2 Error
The variance of a random variable
Marginal distribution
Dependent Selection
14. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
applied statistics
Variability
Ratio measurements
the population variance
15. Is the probability distribution - under repeated sampling of the population - of a given statistic.
A likelihood function
A sampling distribution
The standard deviation
Coefficient of determination
16. Some commonly used symbols for sample statistics
Average and arithmetic mean
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Marginal probability
Type I errors & Type II errors
17. 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.
A Random vector
A Distribution function
Interval measurements
A data point
18. 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.
A data point
Joint probability
Step 2 of a statistical experiment
Ratio measurements
19. A subjective estimate of probability.
Sampling frame
The Covariance between two random variables X and Y - with expected values E(X) =
Credence
experimental studies and observational studies.
20. The proportion of the explained variation by a linear regression model in the total variation.
Parameter
Ordinal measurements
Coefficient of determination
A Statistical parameter
21. Is data arising from counting that can take only non-negative integer values.
Law of Parsimony
Count data
A statistic
An estimate of a parameter
22. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
The standard deviation
An estimate of a parameter
Trend
the population variance
23. Probability of accepting a false null hypothesis.
Beta value
Probability density functions
A Random vector
Step 2 of a statistical experiment
24. Of a group of numbers is the center point of all those number values.
Descriptive
The average - or arithmetic mean
Probability density
Average and arithmetic mean
25. 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
A data set
Inferential
Probability
Dependent Selection
26. 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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27. 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 random variable
Experimental and observational studies
Credence
The variance of a random variable
28. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
The average - or arithmetic mean
Sampling frame
Residuals
An estimate of a parameter
29. 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).
Sampling
Ratio measurements
Experimental and observational studies
Joint probability
30. 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.
An experimental study
Descriptive statistics
Residuals
Posterior probability
31. 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.
That is the median value
inferential statistics
A Distribution function
32. ?
An event
applied statistics
the population correlation
P-value
33. 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.
Independent Selection
Experimental and observational studies
Block
Variability
34. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Probability density functions
The average - or arithmetic mean
Confounded variables
Estimator
35. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
A statistic
Atomic event
Placebo effect
The average - or arithmetic mean
36. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Particular realizations of a random variable
Descriptive
Greek letters
A population or statistical population
37. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.
Type I errors & Type II errors
the population cumulants
An Elementary event
the population mean
38. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
A population or statistical population
Bias
Parameter
39. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
The Range
Correlation coefficient
A statistic
40. 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.
A probability distribution
The standard deviation
A probability space
Kurtosis
41. Is the length of the smallest interval which contains all the data.
Joint probability
Ratio measurements
The Range
A Distribution function
42. A measurement such that the random error is small
the population mean
Interval measurements
Reliable measure
Individual
43. 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
Conditional probability
Count data
Step 1 of a statistical experiment
A sampling distribution
44. 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).
An event
An Elementary event
Probability density functions
Power of a test
45. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
The variance of a random variable
Alpha value (Level of Significance)
Seasonal effect
46. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data
Alpha value (Level of Significance)
Treatment
inferential statistics
Ratio measurements
47. Any specific experimental condition applied to the subjects
covariance of X and Y
descriptive statistics
Treatment
An event
48. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
A Statistical parameter
A random variable
covariance of X and Y
Individual
49. To find the average - or arithmetic mean - of a set of numbers:
Bias
Null hypothesis
Parameter
Divide the sum by the number of values.
50. 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
Estimator
Interval measurements
categorical variables