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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.
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. Is that part of a population which is actually observed.
Statistical adjustment
Random variables
A sample
That is the median value
2. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
A Random vector
The Expected value
Marginal probability
Particular realizations of a random variable
3. Statistical methods can be used for summarizing or describing a collection of data; this is called
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Cumulative distribution functions
descriptive statistics
Bias
4. 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
A Probability measure
Individual
Skewness
Mutual independence
5. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
the population correlation
A sampling distribution
Bias
Dependent Selection
6. 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
Step 3 of a statistical experiment
Reliable measure
Interval measurements
7. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Valid measure
Inferential statistics
Variability
Placebo effect
8. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
A probability space
The Covariance between two random variables X and Y - with expected values E(X) =
Trend
9. The standard deviation of a sampling distribution.
Inferential
Sample space
Individual
Standard error
10. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
Statistic
Step 1 of a statistical experiment
A sample
11. A variable describes an individual by placing the individual into a category or a group.
Experimental and observational studies
Qualitative variable
Posterior probability
categorical variables
12. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Cumulative distribution functions
Pairwise independence
The standard deviation
13. Gives the probability distribution for a continuous random variable.
A probability density function
hypothesis
Law of Large Numbers
Variable
14. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.
Sampling
A probability space
Statistical inference
Sampling Distribution
15. 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
Ratio measurements
methods of least squares
Standard error
Coefficient of determination
16. Failing to reject a false null hypothesis.
Type 2 Error
Conditional distribution
Step 3 of a statistical experiment
Average and arithmetic mean
17. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
The average - or arithmetic mean
Individual
That is the median value
the population mean
18. The proportion of the explained variation by a linear regression model in the total variation.
quantitative variables
expected value of X
nominal - ordinal - interval - and ratio
Coefficient of determination
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.
Probability
P-value
Experimental and observational studies
Sampling Distribution
20. 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.
Binary data
Conditional distribution
Pairwise independence
Statistical dispersion
21. 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)
Interval measurements
Individual
Observational study
An estimate of a parameter
22. ?r
Qualitative variable
Independent Selection
Binary data
the population cumulants
23. 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
Bias
hypotheses
Parameter - or 'statistical parameter'
Sampling
24. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Type II errors
Correlation
hypothesis
Probability density functions
25.
Cumulative distribution functions
Pairwise independence
the population mean
Treatment
26. A subjective estimate of probability.
Cumulative distribution functions
Binomial experiment
Residuals
Credence
27. Probability of rejecting a true null hypothesis.
Lurking variable
Alpha value (Level of Significance)
Inferential statistics
Block
28. Rejecting a true null hypothesis.
Observational study
quantitative variables
An experimental study
Type 1 Error
29. 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
Valid measure
Null hypothesis
Parameter
That is the median value
30. (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.
Statistic
Lurking variable
An Elementary event
Credence
31. 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 Distribution function
P-value
Cumulative distribution functions
Pairwise independence
32. Cov[X - Y] :
covariance of X and Y
s-algebras
Marginal distribution
Type II errors
33. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Probability
A statistic
Marginal distribution
Marginal probability
34. 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
An estimate of a parameter
Probability density functions
Probability
Standard error
35. A measurement such that the random error is small
Individual
Reliable measure
A probability distribution
Placebo effect
36. 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
Statistical dispersion
An experimental study
A sample
37. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
Law of Large Numbers
Parameter
Marginal distribution
s-algebras
38. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
Type I errors & Type II errors
Type II errors
An estimate of a parameter
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.
A data point
Type I errors & Type II errors
variance of X
A Random vector
40. Are simply two different terms for the same thing. Add the given values
Prior probability
Average and arithmetic mean
An Elementary event
Kurtosis
41. 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 Expected value
Dependent Selection
Lurking variable
Skewness
42. (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
Credence
The Expected value
Interval measurements
Cumulative distribution functions
43. 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.
Marginal probability
Sampling frame
Confounded variables
Estimator
44. Describes a characteristic of an individual to be measured or observed.
methods of least squares
Variable
Sampling frame
Binomial experiment
45. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Interval measurements
Seasonal effect
Residuals
Skewness
46. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Descriptive
Inferential
inferential statistics
Ordinal measurements
47. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
That is the median value
observational study
An estimate of a parameter
P-value
48. 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.
That is the median value
Random variables
An experimental study
A data point
49. Is data arising from counting that can take only non-negative integer values.
Count data
Statistics
the population mean
covariance of X and Y
50. Var[X] :
Credence
variance of X
Greek letters
Average and arithmetic mean