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. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
A data set
Descriptive
The standard deviation
Beta value
2. Is a parameter that indexes a family of probability distributions.
covariance of X and Y
the population correlation
A Statistical parameter
A data set
3.
Variability
The sample space
the population mean
Variable
4. 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.
An event
A sample
the population cumulants
Sampling
5. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Outlier
Particular realizations of a random variable
Count data
A Distribution function
6. (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
Count data
Trend
The Expected value
Type 2 Error
7. Rejecting a true null hypothesis.
Type 1 Error
Coefficient of determination
Step 2 of a statistical experiment
Ratio measurements
8. 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
Step 3 of a statistical experiment
methods of least squares
Valid measure
A data point
9. 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
Type 2 Error
Descriptive statistics
expected value of X
The standard deviation
10. The standard deviation of a sampling distribution.
Null hypothesis
Standard error
Mutual independence
Conditional probability
11. 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
Joint distribution
Mutual independence
Treatment
A data set
12. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
A data set
The standard deviation
Type I errors & Type II errors
13. 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).
Statistical inference
An event
Power of a test
Ordinal measurements
14. Is the length of the smallest interval which contains all the data.
Joint distribution
The Range
Conditional probability
expected value of X
15. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
observational study
Quantitative variable
A Distribution function
A probability density function
16. The collection of all possible outcomes in an experiment.
Sample space
Inferential statistics
Prior probability
Type 1 Error
17. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
A statistic
Prior probability
Joint distribution
Experimental and observational studies
18. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Atomic event
The Mean of a random variable
An estimate of a parameter
A probability distribution
19. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
s-algebras
The Range
Joint distribution
Parameter
20. 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.
The Range
A probability distribution
Seasonal effect
Marginal distribution
21. Is a sample space over which a probability measure has been defined.
Particular realizations of a random variable
Placebo effect
A probability space
An Elementary event
22. A measurement such that the random error is small
Reliable measure
Credence
hypothesis
quantitative variables
23. 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.
Nominal measurements
Independent Selection
Placebo effect
Sampling
24. 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.
Statistical dispersion
Correlation coefficient
The variance of a random variable
Statistic
25. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a
Parameter - or 'statistical parameter'
Count data
Step 2 of a statistical experiment
An Elementary event
26. The proportion of the explained variation by a linear regression model in the total variation.
Binary data
Average and arithmetic mean
Sampling
Coefficient of determination
27. 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
Standard error
A probability space
Probability
Marginal distribution
28. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.
Statistical inference
the population mean
Divide the sum by the number of values.
Count data
29. 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.
Warning
: Invalid argument supplied for foreach() in
/var/www/html/basicversity.com/show_quiz.php
on line
183
30. (cdfs) are denoted by upper case letters - e.g. F(x).
Likert scale
Alpha value (Level of Significance)
Power of a test
Cumulative distribution functions
31. Cov[X - Y] :
Parameter
covariance of X and Y
Dependent Selection
Individual
32. Data are gathered and correlations between predictors and response are investigated.
nominal - ordinal - interval - and ratio
descriptive statistics
Correlation coefficient
observational study
33. Another name for elementary event.
Ordinal measurements
Sampling
Sample space
Atomic event
34. Have no meaningful rank order among values.
Mutual independence
Nominal measurements
Valid measure
Simpson's Paradox
35. A data value that falls outside the overall pattern of the graph.
Outlier
A Probability measure
Kurtosis
The median value
36. 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.
A likelihood function
Marginal probability
Step 3 of a statistical experiment
Residuals
37. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that
Parameter
Block
Null hypothesis
hypothesis
38. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Atomic event
Variable
Likert scale
Greek letters
39. 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
Atomic event
Independence or Statistical independence
Random variables
The median value
40. Uses patterns in the sample data to draw inferences about the population represented - accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing) - estimating numerical characteris
Inferential statistics
The standard deviation
A population or statistical population
The Expected value
41. A numerical measure that assesses the strength of a linear relationship between two variables.
Parameter - or 'statistical parameter'
The Mean of a random variable
Binary data
Correlation coefficient
42. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
nominal - ordinal - interval - and ratio
A Distribution function
Seasonal effect
Parameter
43. Two variables such that their effects on the response variable cannot be distinguished from each other.
Null hypothesis
Confounded variables
Treatment
Count data
44. Is defined as the expected value of random variable (X -
Outlier
Likert scale
The Covariance between two random variables X and Y - with expected values E(X) =
Sampling Distribution
45. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Placebo effect
Outlier
Statistical dispersion
The median value
46. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Simple random sample
Correlation
Placebo effect
47. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
descriptive statistics
The median value
Standard error
48. When you have two or more competing models - choose the simpler of the two models.
An estimate of a parameter
Probability and statistics
Variable
Law of Parsimony
49. Probability of rejecting a true null hypothesis.
The average - or arithmetic mean
Bias
Step 2 of a statistical experiment
Alpha value (Level of Significance)
50. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Correlation
The sample space
categorical variables
A Distribution function