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. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
covariance of X and Y
Sampling frame
Inferential
Individual
2. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Probability density functions
Step 2 of a statistical experiment
Estimator
Cumulative distribution functions
3. A list of individuals from which the sample is actually selected.
The sample space
Likert scale
Sampling frame
A random variable
4. Rejecting a true null hypothesis.
Descriptive
Alpha value (Level of Significance)
Simple random sample
Type 1 Error
5. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
inferential statistics
Type 2 Error
Simpson's Paradox
6. The probability of correctly detecting a false null hypothesis.
Step 3 of a statistical experiment
Likert scale
Power of a test
methods of least squares
7. 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
A random variable
Probability and statistics
An estimate of a parameter
The Mean of a random variable
8. Gives the probability of events in a probability space.
A Probability measure
Credence
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Step 2 of a statistical experiment
9. Cov[X - Y] :
covariance of X and Y
The average - or arithmetic mean
variance of X
s-algebras
10.
the population correlation
An estimate of a parameter
A sample
the population mean
11. Long-term upward or downward movement over time.
Statistical dispersion
The standard deviation
Bias
Trend
12. The standard deviation of a sampling distribution.
Particular realizations of a random variable
Standard error
inferential statistics
Beta value
13. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
A sample
Interval measurements
Quantitative variable
Marginal probability
14. ?
the population correlation
Statistical dispersion
the population variance
Valid measure
15. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Seasonal effect
Greek letters
Independence or Statistical independence
Residuals
16. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Greek letters
Block
Descriptive
Null hypothesis
17. Is the length of the smallest interval which contains all the data.
The Range
That is the median value
Sampling Distribution
Probability density functions
18. A measurement such that the random error is small
Reliable measure
Variability
Power of a test
Likert scale
19. 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
Likert scale
Statistical adjustment
Probability
Pairwise independence
20. Is data arising from counting that can take only non-negative integer values.
Sampling Distribution
the population mean
That is the median value
Count data
21. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'
Law of Large Numbers
Simple random sample
quantitative variables
Conditional probability
22. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Null hypothesis
Type 2 Error
Qualitative variable
the sample or population mean
23. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Statistical dispersion
applied statistics
the population correlation
Ordinal measurements
24. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Inferential
hypotheses
Likert scale
Correlation
25. 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).
Type I errors & Type II errors
Random variables
Average and arithmetic mean
Joint probability
26. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
Warning
: Invalid argument supplied for foreach() in
/var/www/html/basicversity.com/show_quiz.php
on line
183
27. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Simulation
Statistic
Joint distribution
Inferential statistics
28. 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
f(z) - and its cdf by F(z).
Binomial experiment
Bias
experimental studies and observational studies.
29. Is a parameter that indexes a family of probability distributions.
Marginal distribution
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Probability and statistics
A Statistical parameter
30. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
quantitative variables
Kurtosis
A statistic
Bias
31. 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
Variability
An estimate of a parameter
methods of least squares
32. Have imprecise differences between consecutive values - but have a meaningful order to those values
Type I errors & Type II errors
Confounded variables
Ordinal measurements
Binomial experiment
33. 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.
Joint distribution
Estimator
The Covariance between two random variables X and Y - with expected values E(X) =
Sampling
34. 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
Bias
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Observational study
Cumulative distribution functions
35. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.
quantitative variables
The variance of a random variable
The sample space
Simple random sample
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).
Type II errors
observational study
Greek letters
An event
37. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are
Descriptive statistics
hypotheses
Conditional distribution
Qualitative variable
38. Another name for elementary event.
Atomic event
Probability density functions
A probability distribution
Average and arithmetic mean
39. 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.
methods of least squares
Independent Selection
f(z) - and its cdf by F(z).
Sampling Distribution
40. Probability of accepting a false null hypothesis.
Beta value
A probability space
the population cumulants
Valid measure
41. Some commonly used symbols for sample statistics
Probability density
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
the population mean
the sample or population mean
42. S^2
the population variance
observational study
categorical variables
Average and arithmetic mean
43. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
the population cumulants
Random variables
Simulation
44. A numerical measure that describes an aspect of a sample.
A likelihood function
Statistic
Type II errors
Null hypothesis
45. (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
A sample
A statistic
Block
A likelihood function
46. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
Probability density functions
Simpson's Paradox
Qualitative variable
47. 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.
f(z) - and its cdf by F(z).
The median value
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
An Elementary event
48. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.
Marginal distribution
Type II errors
Type 2 Error
A Random vector
49. A subjective estimate of probability.
descriptive statistics
Credence
Standard error
hypotheses
50. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
hypothesis
An estimate of a parameter
Confounded variables
Probability and statistics