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 two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).
Trend
hypotheses
Joint probability
Cumulative distribution functions
2. Is the probability distribution - under repeated sampling of the population - of a given statistic.
A sampling distribution
Law of Parsimony
An estimate of a parameter
The sample space
3. A numerical measure that describes an aspect of a sample.
A probability space
Statistic
A probability distribution
Count data
4. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
The standard deviation
Beta value
Individual
5. Describes the spread in the values of the sample statistic when many samples are taken.
That is the median value
Variability
f(z) - and its cdf by F(z).
Experimental and observational studies
6. A numerical facsimilie or representation of a real-world phenomenon.
A statistic
Trend
Simulation
Count data
7. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Joint distribution
the sample or population mean
Interval measurements
Inferential statistics
8. Some commonly used symbols for sample statistics
Estimator
A probability density function
Type 2 Error
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
9. A data value that falls outside the overall pattern of the graph.
Outlier
The variance of a random variable
A probability density function
Cumulative distribution functions
10. Are simply two different terms for the same thing. Add the given values
Qualitative variable
Average and arithmetic mean
Mutual independence
variance of X
11. Is a parameter that indexes a family of probability distributions.
Average and arithmetic mean
A Statistical parameter
Statistical dispersion
Marginal probability
12. 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.
Descriptive statistics
Inferential
Conditional distribution
Divide the sum by the number of values.
13. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
A sampling distribution
Qualitative variable
Type II errors
Inferential
14. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Inferential
Simpson's Paradox
Individual
Law of Parsimony
15. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.
Dependent Selection
Simpson's Paradox
A Distribution function
Variable
16. 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
Statistic
Cumulative distribution functions
Step 3 of a statistical experiment
Coefficient of determination
17. 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.
A Random vector
The Expected value
A data set
The median value
18. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Trend
Sampling Distribution
Qualitative variable
A Random vector
19. 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
the population mean
The sample space
Inferential statistics
Atomic event
20. Is its expected value. The mean (or sample mean of a data set is just the average value.
A data set
Pairwise independence
The Mean of a random variable
descriptive statistics
21. A numerical measure that assesses the strength of a linear relationship between two variables.
s-algebras
Simulation
Greek letters
Correlation coefficient
22. Gives the probability of events in a probability space.
Pairwise independence
A Probability measure
hypotheses
Average and arithmetic 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
Null hypothesis
Type 2 Error
Valid measure
24. Is a sample space over which a probability measure has been defined.
A data point
Mutual independence
A probability space
Interval measurements
25. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
A probability distribution
Outlier
expected value of X
Quantitative variable
26. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
Type I errors & Type II errors
covariance of X and Y
P-value
Simpson's Paradox
27. Is a function that gives the probability of all elements in a given space: see List of probability distributions
the population variance
A probability distribution
Power of a test
Sample space
28. 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
Quantitative variable
The Mean of a random variable
An experimental study
29. 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
Bias
Step 2 of a statistical experiment
Inferential
Skewness
30. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Law of Large Numbers
Statistical dispersion
Sampling Distribution
experimental studies and observational studies.
31. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Lurking variable
A Random vector
A population or statistical population
An estimate of a parameter
32. The standard deviation of a sampling distribution.
Average and arithmetic mean
Standard error
Sample space
Conditional distribution
33. Is data arising from counting that can take only non-negative integer values.
Credence
Count data
Pairwise independence
Kurtosis
34. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
A probability density function
Probability and statistics
nominal - ordinal - interval - and ratio
Descriptive
35. Where the null hypothesis is falsely rejected giving a 'false positive'.
Seasonal effect
Type I errors
Confounded variables
Skewness
36. 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.
Reliable measure
Statistical inference
Credence
descriptive statistics
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
hypothesis
An event
Coefficient of determination
Marginal probability
38. 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
Trend
Observational study
experimental studies and observational studies.
Cumulative distribution functions
39. Is denoted by - pronounced 'x bar'.
The median value
Individual
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
categorical variables
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
hypotheses
A data point
the sample or population mean
A probability density function
41. Working from a null hypothesis two basic forms of error are recognized:
Trend
hypotheses
Type I errors & Type II errors
Sampling frame
42. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
categorical variables
Trend
Independent Selection
Binomial experiment
43. E[X] :
variance of X
Law of Large Numbers
expected value of X
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
44. Is a sample and the associated data points.
Ratio measurements
Divide the sum by the number of values.
A data set
Step 1 of a statistical experiment
45. Var[X] :
variance of X
Probability
Inferential statistics
Skewness
46. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
Probability
Variable
Conditional probability
47. Describes a characteristic of an individual to be measured or observed.
Conditional probability
Correlation
Reliable measure
Variable
48. The collection of all possible outcomes in an experiment.
Type I errors
Null hypothesis
Sample space
The average - or arithmetic mean
49. 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
Observational study
Ratio measurements
Residuals
Cumulative distribution functions
50. 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.
Descriptive statistics
An estimate of a parameter
Seasonal effect
categorical variables