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. 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
Type 1 Error
Alpha value (Level of Significance)
Independence or Statistical independence
Law of Large Numbers
2. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
Dependent Selection
A data point
Beta value
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.
The average - or arithmetic mean
s-algebras
That value is the median value
An Elementary event
4. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Experimental and observational studies
the sample or population mean
Simpson's Paradox
Sampling
5. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Valid measure
Seasonal effect
Skewness
Statistical adjustment
6. Working from a null hypothesis two basic forms of error are recognized:
descriptive statistics
Experimental and observational studies
Divide the sum by the number of values.
Type I errors & Type II errors
7. When there is an even number of values...
Skewness
Reliable measure
That is the median value
Probability
8. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
A random variable
Marginal probability
s-algebras
Likert scale
9. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Quantitative variable
Reliable measure
Step 2 of a statistical experiment
An estimate of a parameter
10. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P
Correlation
Ratio measurements
Block
A probability space
11. Is data arising from counting that can take only non-negative integer values.
Variable
Count data
Type 2 Error
quantitative variables
12. 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.
A data set
Posterior probability
Conditional distribution
Sampling
13. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Simulation
A Random vector
expected value of X
Divide the sum by the number of values.
14. 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.
A sample
hypotheses
Bias
categorical variables
15. Describes the spread in the values of the sample statistic when many samples are taken.
Valid measure
Standard error
Variability
covariance of X and Y
16. The probability of correctly detecting a false null hypothesis.
Sampling
Sampling Distribution
the population mean
Power of a test
17. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Random variables
Simpson's Paradox
Conditional probability
Descriptive
18. Rejecting a true null hypothesis.
The Covariance between two random variables X and Y - with expected values E(X) =
Mutual independence
Type 1 Error
Joint probability
19. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
categorical variables
Residuals
Pairwise independence
Trend
20. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Coefficient of determination
Trend
quantitative variables
Step 3 of a statistical experiment
21. 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
Credence
Inferential statistics
22. The standard deviation of a sampling distribution.
Sampling Distribution
Probability
Simple random sample
Standard error
23. E[X] :
expected value of X
Law of Large Numbers
Inferential statistics
variance of X
24. Is defined as the expected value of random variable (X -
An estimate of a parameter
Variability
The Covariance between two random variables X and Y - with expected values E(X) =
Statistic
25. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Valid measure
nominal - ordinal - interval - and ratio
Inferential
Simulation
26. Have no meaningful rank order among values.
Bias
Law of Parsimony
f(z) - and its cdf by F(z).
Nominal measurements
27. Var[X] :
Ratio measurements
Independent Selection
variance of X
hypotheses
28. 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
inferential statistics
categorical variables
Probability and statistics
Block
29. Another name for elementary event.
Outlier
Step 3 of a statistical experiment
Statistic
Atomic event
30. Is a sample and the associated data points.
A data set
Kurtosis
P-value
A probability density function
31. S^2
Probability
The average - or arithmetic mean
A data point
the population variance
32.
Mutual independence
the sample or population mean
the population mean
Ordinal measurements
33. In particular - the pdf of the standard normal distribution is denoted by
f(z) - and its cdf by F(z).
A likelihood function
Outlier
Null hypothesis
34. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
the sample or population mean
A random variable
An event
Statistical dispersion
35. Probability of accepting a false null hypothesis.
Probability density
Beta value
Inferential statistics
Credence
36. 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
Greek letters
Probability density
An event
Residuals
37. (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
the population mean
Probability density
The Expected value
Variable
38. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Joint distribution
Greek letters
Trend
Variable
39. A measure that is relevant or appropriate as a representation of that property.
Variability
Coefficient of determination
Valid measure
Statistical dispersion
40. 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
Statistical adjustment
Joint probability
Conditional probability
hypotheses
41. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Probability and statistics
Correlation
Placebo effect
the population correlation
42. 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.
Step 2 of a statistical experiment
Sampling
Skewness
An experimental study
43. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.
A Probability measure
Seasonal effect
A likelihood function
Inferential statistics
44. Some commonly used symbols for population parameters
the population mean
The median value
An event
categorical variables
45. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Statistics
The Range
A statistic
Joint distribution
46. 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.
Marginal distribution
Cumulative distribution functions
A data point
Inferential statistics
47. 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
Null hypothesis
Seasonal effect
quantitative variables
A data point
48. ?
the population mean
the population correlation
hypotheses
the sample or population mean
49. Data are gathered and correlations between predictors and response are investigated.
Coefficient of determination
Variable
observational study
Binomial experiment
50. Probability of rejecting a true null hypothesis.
Alpha value (Level of Significance)
Step 2 of a statistical experiment
Joint probability
Kurtosis