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. 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.
A Random vector
Binary data
Seasonal effect
Trend
2. 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.
Inferential
A Probability measure
Independent Selection
Prior probability
3. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Qualitative variable
the population correlation
the population mean
4. Is defined as the expected value of random variable (X -
Bias
Block
An event
The Covariance between two random variables X and Y - with expected values E(X) =
5. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Outlier
Nominal measurements
Ordinal measurements
nominal - ordinal - interval - and ratio
6. Describes a characteristic of an individual to be measured or observed.
Law of Parsimony
Variable
Simulation
An experimental study
7. A common goal for a statistical research project is to investigate causality - and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response.
Statistic
Estimator
The standard deviation
Experimental and observational studies
8. The proportion of the explained variation by a linear regression model in the total variation.
Seasonal effect
Probability and statistics
Conditional probability
Coefficient of determination
9. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Statistic
the sample or population mean
Credence
Bias
10. 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
11. To find the average - or arithmetic mean - of a set of numbers:
Ratio measurements
The Expected value
Divide the sum by the number of values.
An experimental study
12. 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).
the population variance
Joint probability
The Mean of a random variable
nominal - ordinal - interval - and ratio
13. Is a sample space over which a probability measure has been defined.
the sample or population mean
Ordinal measurements
Valid measure
A probability space
14. 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.
A Statistical parameter
A Distribution function
s-algebras
Simple random sample
15. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe
The Expected value
Skewness
Valid measure
Greek letters
16. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
the population mean
Statistical adjustment
categorical variables
17. 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
Descriptive statistics
Conditional distribution
Type I errors & Type II errors
Ordinal measurements
18. In particular - the pdf of the standard normal distribution is denoted by
Power of a test
Residuals
f(z) - and its cdf by F(z).
A probability density function
19. (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
Law of Parsimony
Kurtosis
The Expected value
descriptive statistics
20. ?
the population correlation
Alpha value (Level of Significance)
the population mean
Ratio measurements
21. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the
Probability
A statistic
the population variance
That value is the median value
22. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Type II errors
Ratio measurements
Trend
Inferential
23. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Posterior probability
P-value
variance of X
A sampling distribution
24. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Descriptive
Inferential
Statistical adjustment
Statistical dispersion
25. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)
Sampling frame
Statistical adjustment
Standard error
Interval measurements
26. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Greek letters
Standard error
Marginal distribution
quantitative variables
27. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
An Elementary event
Valid measure
Probability density functions
s-algebras
28. A list of individuals from which the sample is actually selected.
A Statistical parameter
Coefficient of determination
variance of X
Sampling frame
29. 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.
f(z) - and its cdf by F(z).
That value is the median value
the population cumulants
Dependent Selection
30. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
An estimate of a parameter
Binary data
Power of a test
Placebo effect
31. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
That is the median value
Prior probability
P-value
Probability
32. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Variability
Quantitative variable
Statistic
The Expected value
33.
An Elementary event
Correlation
Experimental and observational studies
the population mean
34. 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
Interval measurements
Step 1 of a statistical experiment
The sample space
35. 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.
Statistics
covariance of X and Y
Atomic event
A likelihood function
36. Have no meaningful rank order among values.
Descriptive
Nominal measurements
experimental studies and observational studies.
f(z) - and its cdf by F(z).
37. Are usually written in upper case roman letters: X - Y - etc.
Sample space
Statistical adjustment
nominal - ordinal - interval - and ratio
Random variables
38. A variable describes an individual by placing the individual into a category or a group.
Qualitative variable
Nominal measurements
Residuals
Bias
39. Probability of rejecting a true null hypothesis.
Power of a test
A statistic
Alpha value (Level of Significance)
A sample
40. Where the null hypothesis is falsely rejected giving a 'false positive'.
Statistical inference
Credence
Type I errors
The average - or arithmetic mean
41. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Sampling Distribution
quantitative variables
That is the median value
Step 2 of a statistical experiment
42. The collection of all possible outcomes in an experiment.
Sample space
Joint distribution
A Distribution function
Law of Large Numbers
43. 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
Sampling
Type II errors
Kurtosis
Mutual independence
44. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
s-algebras
Bias
quantitative variables
Inferential statistics
45. (cdfs) are denoted by upper case letters - e.g. F(x).
covariance of X and Y
A data set
Statistical dispersion
Cumulative distribution functions
46. Statistical methods can be used for summarizing or describing a collection of data; this is called
The average - or arithmetic mean
f(z) - and its cdf by F(z).
descriptive statistics
the population mean
47. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Joint distribution
variance of X
Individual
A data point
48. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a
Parameter
Null hypothesis
Simulation
Independent Selection
49. Some commonly used symbols for population parameters
Independence or Statistical independence
Cumulative distribution functions
the population mean
Individual
50. Rejecting a true null hypothesis.
Experimental and observational studies
Sampling Distribution
Type 1 Error
categorical variables