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. 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.
Coefficient of determination
The median value
A probability density function
Marginal probability
2. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Atomic event
Type II errors
Outlier
Prior probability
3. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Statistic
applied statistics
Statistical dispersion
Variability
4. Some commonly used symbols for sample statistics
Bias
A statistic
Binomial experiment
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
5. Statistical methods can be used for summarizing or describing a collection of data; this is called
Type I errors
Conditional distribution
P-value
descriptive statistics
6. Describes the spread in the values of the sample statistic when many samples are taken.
Reliable measure
A probability space
Variability
Seasonal effect
7. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
A sampling distribution
Credence
Sampling
Joint distribution
8. 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
9. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Inferential
Nominal measurements
Treatment
Sampling Distribution
10. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.
Independent Selection
A Statistical parameter
Particular realizations of a random variable
Estimator
11. When there is an even number of values...
Random variables
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Interval measurements
That is the median value
12. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.
Sampling frame
A random variable
Atomic event
Experimental and observational studies
13. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
methods of least squares
Particular realizations of a random variable
Marginal distribution
inferential statistics
14. 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.
A random variable
covariance of X and Y
The standard deviation
Statistics
15. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.
Greek letters
A data point
A population or statistical population
Lurking variable
16. A numerical facsimilie or representation of a real-world phenomenon.
applied statistics
Simulation
Prior probability
Parameter - or 'statistical parameter'
17. Is a parameter that indexes a family of probability distributions.
the population cumulants
Probability density
Law of Parsimony
A Statistical parameter
18. 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
categorical variables
Simulation
A statistic
Descriptive statistics
19.
A sampling distribution
Trend
Qualitative variable
the population mean
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.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Estimator
Conditional distribution
Descriptive
21. Have no meaningful rank order among values.
Block
Nominal measurements
Likert scale
Statistics
22. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.
A Statistical parameter
hypothesis
The sample space
covariance of X and Y
23. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Descriptive statistics
nominal - ordinal - interval - and ratio
Simulation
Dependent Selection
24. 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
Mutual independence
An Elementary event
Ordinal measurements
Variability
25. Is the length of the smallest interval which contains all the data.
the population variance
Probability density functions
The Range
Treatment
26. Gives the probability distribution for a continuous random variable.
Estimator
A probability density function
Block
An event
27. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Binomial experiment
Random variables
quantitative variables
The standard deviation
28. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
An estimate of a parameter
Statistical adjustment
variance of X
the population variance
29. In particular - the pdf of the standard normal distribution is denoted by
A data point
The variance of a random variable
covariance of X and Y
f(z) - and its cdf by F(z).
30. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Treatment
f(z) - and its cdf by F(z).
methods of least squares
Bias
31. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Greek letters
A Distribution function
Inferential
Joint distribution
32. (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
Simulation
The Expected value
Parameter
nominal - ordinal - interval - and ratio
33. Var[X] :
variance of X
observational study
the population variance
Prior probability
34. Is its expected value. The mean (or sample mean of a data set is just the average value.
The Mean of a random variable
The variance of a random variable
Confounded variables
Parameter - or 'statistical parameter'
35. A variable describes an individual by placing the individual into a category or a group.
Qualitative variable
Type I errors & Type II errors
Sampling
Credence
36. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
categorical variables
Interval measurements
Law of Large Numbers
That value is the median value
37. 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
Independence or Statistical independence
A statistic
Treatment
hypotheses
38. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Treatment
The average - or arithmetic mean
Binomial experiment
Statistical inference
39. ?r
the sample or population mean
Lurking variable
the population cumulants
Statistic
40. Cov[X - Y] :
Simulation
covariance of X and Y
Correlation
Correlation coefficient
41. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
s-algebras
Law of Parsimony
variance of X
42. A list of individuals from which the sample is actually selected.
Sampling frame
Independent Selection
nominal - ordinal - interval - and ratio
A sampling distribution
43. Of a group of numbers is the center point of all those number values.
Block
The average - or arithmetic mean
Observational study
A Probability measure
44. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
s-algebras
categorical variables
Credence
Kurtosis
45. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
f(z) - and its cdf by F(z).
Quantitative variable
Probability density
Power of a test
46. 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.
An event
The median value
Nominal measurements
Parameter - or 'statistical parameter'
47. To find the average - or arithmetic mean - of a set of numbers:
Marginal distribution
the population mean
Divide the sum by the number of values.
Variability
48. 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
Statistical inference
hypothesis
Dependent Selection
An experimental study
49. (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.
The Covariance between two random variables X and Y - with expected values E(X) =
experimental studies and observational studies.
nominal - ordinal - interval - and ratio
An Elementary event
50. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Residuals
methods of least squares
nominal - ordinal - interval - and ratio
Sample space