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. 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
Parameter - or 'statistical parameter'
Mutual independence
The Range
The median value
2. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
categorical variables
Observational study
Trend
The Covariance between two random variables X and Y - with expected values E(X) =
3. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.
Simple random sample
Descriptive statistics
Statistical inference
A data set
4. 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
Quantitative variable
Independence or Statistical independence
observational study
A population or statistical population
5. Two variables such that their effects on the response variable cannot be distinguished from each other.
Treatment
Statistics
Confounded variables
Kurtosis
6. Is a sample space over which a probability measure has been defined.
observational study
variance of X
Treatment
A probability space
7. Is data arising from counting that can take only non-negative integer values.
That is the median value
Atomic event
inferential statistics
Count data
8. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
Sampling frame
Inferential statistics
Probability
9. A measure that is relevant or appropriate as a representation of that property.
Marginal distribution
Valid measure
Null hypothesis
The standard deviation
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 Distribution
hypotheses
Qualitative variable
Likert scale
11. A numerical measure that assesses the strength of a linear relationship between two variables.
Descriptive statistics
Pairwise independence
A data set
Correlation coefficient
12. Is denoted by - pronounced 'x bar'.
Simulation
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Independent Selection
Type 1 Error
13. The standard deviation of a sampling distribution.
Probability and statistics
Experimental and observational studies
Type 2 Error
Standard error
14. 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
15. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Confounded variables
A data point
A statistic
Coefficient of determination
16.
Parameter - or 'statistical parameter'
Null hypothesis
the population mean
Power of a test
17. A list of individuals from which the sample is actually selected.
f(z) - and its cdf by F(z).
Sampling frame
nominal - ordinal - interval - and ratio
the population mean
18. 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.
Reliable measure
That value is the median value
The standard deviation
A likelihood function
19. Have imprecise differences between consecutive values - but have a meaningful order to those values
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Ordinal measurements
Count data
A statistic
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.
nominal - ordinal - interval - and ratio
The variance of a random variable
A probability density function
Seasonal 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
A Statistical parameter
Statistic
Step 3 of a statistical experiment
Alpha value (Level of Significance)
22. The collection of all possible outcomes in an experiment.
observational study
Sample space
Quantitative variable
Inferential statistics
23. Describes the spread in the values of the sample statistic when many samples are taken.
Cumulative distribution functions
Joint distribution
Greek letters
Variability
24. 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
hypothesis
Null hypothesis
the sample or population mean
experimental studies and observational studies.
25. Where the null hypothesis is falsely rejected giving a 'false positive'.
Reliable measure
the population variance
Type I errors & Type II errors
Type I errors
26. The proportion of the explained variation by a linear regression model in the total variation.
the population variance
Coefficient of determination
Credence
The Mean of a random variable
27. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Law of Parsimony
hypothesis
quantitative variables
Inferential
28. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Particular realizations of a random variable
Average and arithmetic mean
Parameter
applied statistics
29. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
Type 2 Error
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Valid measure
30. 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
Seasonal effect
The Expected value
inferential statistics
A probability distribution
31. 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
Skewness
Bias
Conditional distribution
hypothesis
32. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Statistical dispersion
Divide the sum by the number of values.
Likert scale
applied statistics
33. E[X] :
A probability distribution
expected value of X
Lurking variable
Marginal probability
34. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Statistic
Bias
Sampling Distribution
Quantitative variable
35. ?r
Nominal measurements
the population cumulants
Experimental and observational studies
The median value
36. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
That is the median value
A sampling distribution
The standard deviation
the population mean
37. Var[X] :
Qualitative variable
observational study
variance of X
Power of a test
38. 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.
Interval measurements
A statistic
Greek letters
Marginal probability
39. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Marginal distribution
A Statistical parameter
Prior probability
Variable
40. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Atomic event
Trend
inferential statistics
Probability density functions
41. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
The variance of a random variable
A Random vector
Independence or Statistical independence
Type II errors
42. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Residuals
The Covariance between two random variables X and Y - with expected values E(X) =
Joint probability
variance of X
43. Have no meaningful rank order among values.
An experimental study
categorical variables
Nominal measurements
the population mean
44. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
The median value
That value is the median value
Inferential
A probability distribution
45. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Random vector
A statistic
Conditional probability
Simulation
46. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Prior probability
Inferential statistics
the population cumulants
47. 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
Step 2 of a statistical experiment
Simpson's Paradox
Statistics
Conditional distribution
48. 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}.
experimental studies and observational studies.
The sample space
A Random vector
covariance of X and Y
49. Probability of rejecting a true null hypothesis.
Joint probability
Average and arithmetic mean
Likert scale
Alpha value (Level of Significance)
50. Is its expected value. The mean (or sample mean of a data set is just the average value.
Statistical inference
Dependent Selection
The Mean of a random variable
inferential statistics