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. 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
inferential statistics
Parameter
Law of Large Numbers
the population correlation
2. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
An event
the population cumulants
Ratio measurements
3. A numerical measure that describes an aspect of a sample.
An estimate of a parameter
Statistic
Cumulative distribution functions
Marginal distribution
4. Some commonly used symbols for population parameters
Simpson's Paradox
Qualitative variable
Dependent Selection
the population mean
5. Is a sample and the associated data points.
P-value
A data set
Variable
Skewness
6. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Probability density
Quantitative variable
the population cumulants
Descriptive statistics
7. 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.
Step 2 of a statistical experiment
Lurking variable
Simulation
Quantitative variable
8. 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 statistic
A Probability measure
Marginal probability
A Statistical parameter
9. A measure that is relevant or appropriate as a representation of that property.
the population cumulants
Valid measure
Ratio measurements
experimental studies and observational studies.
10. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Type II errors
Variability
Qualitative variable
A sampling distribution
11. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
A data set
nominal - ordinal - interval - and ratio
Step 2 of a statistical experiment
f(z) - and its cdf by F(z).
12. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
Correlation coefficient
methods of least squares
Individual
13. The collection of all possible outcomes in an experiment.
Sample space
Block
Confounded variables
Parameter
14. Where the null hypothesis is falsely rejected giving a 'false positive'.
hypotheses
Type I errors
Nominal measurements
A Statistical parameter
15. A variable describes an individual by placing the individual into a category or a group.
A Distribution function
A Statistical parameter
Posterior probability
Qualitative variable
16. Is denoted by - pronounced 'x bar'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Observational study
Type 1 Error
Bias
17. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Parameter - or 'statistical parameter'
Prior probability
Sampling frame
Beta value
18. 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
Correlation coefficient
Sampling
Probability density
Null hypothesis
19. Failing to reject a false null hypothesis.
Simple random sample
Standard error
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Type 2 Error
20. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Nominal measurements
An Elementary event
Qualitative variable
21. Probability of rejecting a true null hypothesis.
nominal - ordinal - interval - and ratio
The Range
Alpha value (Level of Significance)
Lurking variable
22. 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.
Likert scale
Bias
Estimator
Type II errors
23. 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
24. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Sampling Distribution
Probability density functions
covariance of X and Y
Bias
25. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
the population cumulants
The sample space
Conditional probability
categorical variables
26. 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
Cumulative distribution functions
The sample space
Independence or Statistical independence
27. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
the population mean
Standard error
Statistical dispersion
28. 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
variance of X
hypotheses
Count data
Estimator
29. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Correlation
Null hypothesis
A statistic
Law of Parsimony
30. 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.
A likelihood function
Sampling Distribution
Likert scale
Independent Selection
31. Have imprecise differences between consecutive values - but have a meaningful order to those values
Statistical adjustment
Divide the sum by the number of values.
Trend
Ordinal measurements
32. Probability of accepting a false null hypothesis.
A probability distribution
Conditional probability
Marginal distribution
Beta value
33. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
A population or statistical population
Probability
Statistical adjustment
34. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
That value is the median value
Random variables
A sample
Likert scale
35. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
quantitative variables
Statistical dispersion
Outlier
Independent Selection
36. Cov[X - Y] :
The Covariance between two random variables X and Y - with expected values E(X) =
Independent Selection
covariance of X and Y
A probability distribution
37. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data
The Range
Seasonal effect
Ratio measurements
Simulation
38. A group of individuals sharing some common features that might affect the treatment.
the sample or population mean
Alpha value (Level of Significance)
Block
Inferential
39. When there is an even number of values...
The median value
That is the median value
Sampling
The variance of a random variable
40. 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.
Posterior probability
A probability density function
Sample space
That value is the median value
41. A numerical measure that describes an aspect of a population.
Parameter
That value is the median value
Experimental and observational studies
s-algebras
42. E[X] :
expected value of X
A data set
Sampling frame
The variance of a random variable
43. 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
44. Is that part of a population which is actually observed.
A sample
nominal - ordinal - interval - and ratio
The variance of a random variable
A population or statistical population
45. A data value that falls outside the overall pattern of the graph.
the population mean
Skewness
Outlier
The median value
46. 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
Independence or Statistical independence
Step 1 of a statistical experiment
Variability
hypothesis
47. 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
inferential statistics
Quantitative variable
Correlation
f(z) - and its cdf by F(z).
48. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
A data set
Individual
A population or statistical population
A Distribution function
49. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.
Experimental and observational studies
Simpson's Paradox
Kurtosis
Type 2 Error
50. Describes a characteristic of an individual to be measured or observed.
Statistical dispersion
Variable
Step 3 of a statistical experiment
Probability density functions