SUBJECTS
|
BROWSE
|
CAREER CENTER
|
POPULAR
|
JOIN
|
LOGIN
Business Skills
|
Soft Skills
|
Basic Literacy
|
Certifications
About
|
Help
|
Privacy
|
Terms
|
Email
Search
Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
Start Test
Study First
Subjects
:
clep
,
math
Instructions:
Answer 50 questions in 15 minutes.
If you are not ready to take this test, you can
study here
.
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. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Law of Large Numbers
Statistical dispersion
Alpha value (Level of Significance)
Binomial experiment
2. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
inferential statistics
nominal - ordinal - interval - and ratio
covariance of X and Y
The standard deviation
3. 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.
Law of Parsimony
Step 3 of a statistical experiment
Bias
Divide the sum by the number of values.
4. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
That value is the median value
Independent Selection
The Mean of a random variable
5. 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.
the population cumulants
Marginal probability
A probability density function
Reliable measure
6. A list of individuals from which the sample is actually selected.
the population correlation
The Mean of a random variable
Qualitative variable
Sampling frame
7. Var[X] :
Dependent Selection
Statistical inference
variance of X
Bias
8. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A probability distribution
A Random vector
Experimental and observational studies
Inferential
9. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then
Prior probability
Null hypothesis
A likelihood function
Cumulative distribution functions
10. (cdfs) are denoted by upper case letters - e.g. F(x).
Bias
Cumulative distribution functions
A data set
Step 2 of a statistical experiment
11. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Type I errors & Type II errors
Quantitative variable
A Random vector
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
12. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.
The standard deviation
Statistics
Greek letters
Variable
13. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
An estimate of a parameter
The Expected value
Beta value
Inferential
14. A numerical facsimilie or representation of a real-world phenomenon.
P-value
Simulation
Bias
Posterior probability
15. Rejecting a true null hypothesis.
P-value
Binomial experiment
Type 1 Error
A population or statistical population
16. 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
nominal - ordinal - interval - and ratio
Null hypothesis
Inferential
Qualitative variable
17. 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
Statistical inference
Beta value
Valid measure
experimental studies and observational studies.
18. (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
Type II errors
Joint probability
Joint distribution
The Expected value
19. 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.
Step 2 of a statistical experiment
Beta value
A random variable
A data point
20. 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
Sampling Distribution
Probability density
Simple random sample
Marginal distribution
21. 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
Outlier
Type II errors
Parameter - or 'statistical parameter'
Step 1 of a statistical experiment
22. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
The Expected value
Binomial experiment
An Elementary event
23. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
the sample or population mean
Dependent Selection
nominal - ordinal - interval - and ratio
Parameter
24. Working from a null hypothesis two basic forms of error are recognized:
A data point
Qualitative variable
Correlation
Type I errors & Type II errors
25. 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.
Descriptive statistics
An event
Statistic
That value is the median value
26. 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.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Conditional probability
Experimental and observational studies
An experimental study
27. 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.
Power of a test
Statistical inference
The median value
Law of Parsimony
28. A measure that is relevant or appropriate as a representation of that property.
Likert scale
descriptive statistics
Valid measure
That is the median value
29. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no
Step 3 of a statistical experiment
Probability and statistics
The variance of a random variable
observational study
30. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
Posterior probability
descriptive statistics
the population cumulants
31. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
The average - or arithmetic mean
Joint probability
Pairwise independence
The standard deviation
32. 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
Probability density functions
The standard deviation
Step 3 of a statistical experiment
Nominal measurements
33. A group of individuals sharing some common features that might affect the treatment.
Atomic event
Simulation
Placebo effect
Block
34. Is a function that gives the probability of all elements in a given space: see List of probability distributions
The Covariance between two random variables X and Y - with expected values E(X) =
The Expected value
A probability distribution
Nominal measurements
35. Is a sample space over which a probability measure has been defined.
A probability space
Estimator
Posterior probability
Independence or Statistical independence
36. Is a sample and the associated data points.
A data set
the population variance
f(z) - and its cdf by F(z).
hypothesis
37. Long-term upward or downward movement over time.
s-algebras
The Mean of a random variable
expected value of X
Trend
38. To find the average - or arithmetic mean - of a set of numbers:
the population variance
expected value of X
Divide the sum by the number of values.
A probability space
39. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
nominal - ordinal - interval - and ratio
Correlation coefficient
Trend
40. (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.
Confounded variables
An Elementary event
A Probability measure
Inferential
41. 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 Elementary event
Probability
The Covariance between two random variables X and Y - with expected values E(X) =
Simulation
42. 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.
Confounded variables
Independent Selection
Ordinal measurements
Prior probability
43. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A statistic
Parameter - or 'statistical parameter'
Inferential statistics
Lurking variable
44. 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
Standard error
inferential statistics
Nominal measurements
An experimental study
45. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
A population or statistical population
Law of Large Numbers
the population variance
Step 3 of a statistical experiment
46. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
the population correlation
Ratio measurements
Inferential statistics
s-algebras
47. When there is an even number of values...
That is the median value
Conditional distribution
Type I errors & Type II errors
Posterior probability
48. 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).
Residuals
An event
Descriptive statistics
A Statistical parameter
49. 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 experimental study
Descriptive
methods of least squares
50. Is data that can take only two values - usually represented by 0 and 1.
Conditional distribution
Step 2 of a statistical experiment
That is the median value
Binary data