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. Another name for elementary event.
The variance of a random variable
Atomic event
The standard deviation
Descriptive statistics
2. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
covariance of X and Y
Ordinal measurements
quantitative variables
the population correlation
3. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Simulation
Sample space
A probability distribution
Bias
4. Var[X] :
variance of X
quantitative variables
hypothesis
Statistical dispersion
5. A numerical measure that assesses the strength of a linear relationship between two variables.
Law of Large Numbers
Correlation coefficient
s-algebras
Particular realizations of a random variable
6. Is data that can take only two values - usually represented by 0 and 1.
Binary data
hypothesis
Statistical inference
Statistics
7. Is defined as the expected value of random variable (X -
quantitative variables
Binary data
Sampling Distribution
The Covariance between two random variables X and Y - with expected values E(X) =
8. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
categorical variables
Coefficient of determination
Divide the sum by the number of values.
Lurking variable
9. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Seasonal effect
expected value of X
Type 1 Error
nominal - ordinal - interval - and ratio
10. 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
Sampling
Type 2 Error
inferential statistics
hypotheses
11. 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.
A population or statistical population
Random variables
Statistical dispersion
Dependent Selection
12. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistic
Statistical adjustment
A probability distribution
That is the median value
13. 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.
Mutual independence
Independent Selection
That value is the median value
the population correlation
14. 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
hypotheses
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
The Covariance between two random variables X and Y - with expected values E(X) =
Observational study
15. To find the average - or arithmetic mean - of a set of numbers:
Pairwise independence
A likelihood function
Divide the sum by the number of values.
The sample space
16. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Joint probability
Statistics
Reliable measure
17. A numerical measure that describes an aspect of a sample.
Observational study
Statistic
Parameter
Individual
18. 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.
A random variable
hypothesis
That value is the median value
An estimate of a parameter
19. Rejecting a true null hypothesis.
Type 1 Error
A Distribution function
The Covariance between two random variables X and Y - with expected values E(X) =
Inferential
20. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
Type I errors & Type II errors
variance of X
Placebo effect
21. 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
the population cumulants
Bias
Type 1 Error
Step 3 of a statistical experiment
22. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Valid measure
That is the median value
Interval measurements
Statistical dispersion
23. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Treatment
Binary data
Greek letters
24. Probability of accepting a false null hypothesis.
An estimate of a parameter
Beta value
Atomic event
A sampling distribution
25. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
the population mean
Sampling Distribution
Null hypothesis
Type I errors
26. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Statistical inference
s-algebras
Ratio measurements
Residuals
27. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
Step 2 of a statistical experiment
Sample space
Bias
28. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Random variables
Inferential
the population cumulants
Greek letters
29. 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
Prior probability
Alpha value (Level of Significance)
A likelihood function
experimental studies and observational studies.
30. 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
Pairwise independence
Step 2 of a statistical experiment
Random variables
Sampling Distribution
31. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Type I errors & Type II errors
Inferential
Variability
A Random vector
32. A data value that falls outside the overall pattern of the graph.
Nominal measurements
Outlier
inferential statistics
Block
33. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
The Mean of a random variable
the population variance
expected value of X
34. A group of individuals sharing some common features that might affect the treatment.
Qualitative variable
Inferential statistics
An event
Block
35. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Type I errors & Type II errors
Probability and statistics
The standard deviation
Independent Selection
36. Are usually written in upper case roman letters: X - Y - etc.
Random variables
A sampling distribution
s-algebras
The median value
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
Conditional distribution
Descriptive statistics
Variable
Step 2 of a statistical experiment
38. ?
Probability density
Interval measurements
Null hypothesis
the population correlation
39. Data are gathered and correlations between predictors and response are investigated.
descriptive statistics
observational study
Seasonal effect
Joint probability
40. When you have two or more competing models - choose the simpler of the two models.
Simpson's Paradox
Probability density
Conditional probability
Law of Parsimony
41. 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
Law of Large Numbers
The Range
hypothesis
42. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.
Conditional probability
A Statistical parameter
A Distribution function
hypothesis
43. 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.
Lurking variable
Law of Large Numbers
The sample space
Average and arithmetic mean
44. Probability of rejecting a true null hypothesis.
A sample
Alpha value (Level of Significance)
the population variance
The median value
45. Working from a null hypothesis two basic forms of error are recognized:
methods of least squares
Quantitative variable
Variable
Type I errors & Type II errors
46. The probability of correctly detecting a false null hypothesis.
Average and arithmetic mean
Power of a test
P-value
Cumulative distribution functions
47. A measurement such that the random error is small
Reliable measure
A data set
Quantitative variable
Beta value
48. 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'
Sampling frame
Correlation coefficient
Conditional probability
The Expected value
49. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Skewness
Pairwise independence
the population mean
the population variance
50. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Quantitative variable
Bias
applied statistics
hypothesis