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. Are simply two different terms for the same thing. Add the given values
quantitative variables
s-algebras
Average and arithmetic mean
Correlation coefficient
2. Cov[X - Y] :
hypotheses
Cumulative distribution functions
Null hypothesis
covariance of X and Y
3. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Sampling Distribution
Ordinal measurements
applied statistics
Joint distribution
4. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
A likelihood function
An event
Qualitative variable
applied statistics
5. 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.
experimental studies and observational studies.
Dependent Selection
The variance of a random variable
Average and arithmetic mean
6. Any specific experimental condition applied to the subjects
A random variable
Ratio measurements
hypotheses
Treatment
7. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
Average and arithmetic mean
The average - or arithmetic mean
A Statistical parameter
8. Some commonly used symbols for sample statistics
Conditional distribution
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Average and arithmetic mean
Null hypothesis
9. Failing to reject a false null hypothesis.
methods of least squares
Parameter - or 'statistical parameter'
Divide the sum by the number of values.
Type 2 Error
10. E[X] :
A sample
expected value of X
Individual
Statistical adjustment
11. 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).
A probability space
A random variable
Statistical dispersion
An event
12. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Probability
Sampling Distribution
Descriptive statistics
The standard deviation
13. Gives the probability distribution for a continuous random variable.
A sampling distribution
Step 2 of a statistical experiment
Trend
A probability density function
14. 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.
The Mean of a random variable
Statistical inference
Seasonal effect
Independence or Statistical independence
15. A numerical measure that assesses the strength of a linear relationship between two variables.
Parameter
Correlation coefficient
Observational study
Conditional distribution
16. Statistical methods can be used for summarizing or describing a collection of data; this is called
Sample space
A data point
hypothesis
descriptive statistics
17. 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
Parameter
Individual
inferential statistics
Correlation
18. S^2
the sample or population mean
Type 1 Error
Seasonal effect
the population variance
19. Describes a characteristic of an individual to be measured or observed.
A statistic
Variable
expected value of X
Sample space
20. 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 sample
The median value
Average and arithmetic mean
Ordinal measurements
21. Have imprecise differences between consecutive values - but have a meaningful order to those values
Average and arithmetic mean
the population mean
Treatment
Ordinal measurements
22. 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
The Expected value
variance of X
Probability density functions
23. (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
the population mean
Kurtosis
Parameter - or 'statistical parameter'
The Expected value
24. Another name for elementary event.
Atomic event
variance of X
The sample space
observational study
25. Is that part of a population which is actually observed.
Sample space
A sample
Qualitative variable
Credence
26. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
P-value
Cumulative distribution functions
Placebo effect
Simpson's Paradox
27. 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
Descriptive
Step 2 of a statistical experiment
Sample space
Probability
28. 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
29. To find the average - or arithmetic mean - of a set of numbers:
observational study
P-value
Statistics
Divide the sum by the number of values.
30. 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.
Variable
Descriptive
That value is the median value
Statistical adjustment
31. Var[X] :
variance of X
Valid measure
Marginal probability
Statistical dispersion
32. 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
Dependent Selection
Lurking variable
Step 2 of a statistical experiment
33. Is a sample space over which a probability measure has been defined.
A probability space
A data point
Kurtosis
quantitative variables
34. Data are gathered and correlations between predictors and response are investigated.
observational study
Correlation
Correlation coefficient
Statistical inference
35. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
The Range
Type I errors & Type II errors
Sampling frame
36. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Sampling Distribution
Correlation
Confounded variables
Step 3 of a statistical experiment
37. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
Marginal probability
That value is the median value
Atomic event
38. Is its expected value. The mean (or sample mean of a data set is just the average value.
Credence
Estimator
The Mean of a random variable
Posterior probability
39. Describes the spread in the values of the sample statistic when many samples are taken.
An estimate of a parameter
Sampling
A sampling distribution
Variability
40. 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'
observational study
A Random vector
inferential statistics
Conditional probability
41. 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
Estimator
Sampling Distribution
A data set
experimental studies and observational studies.
42. 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
categorical variables
the population mean
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
hypothesis
43. 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.
Credence
A probability distribution
A Distribution function
Bias
44. 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.
Treatment
methods of least squares
Estimator
Mutual independence
45. A data value that falls outside the overall pattern of the graph.
A statistic
Ratio measurements
Outlier
The sample space
46. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Outlier
Greek letters
Marginal distribution
Kurtosis
47. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then
Experimental and observational studies
Binomial experiment
A likelihood function
Alpha value (Level of Significance)
48. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Residuals
A statistic
A random variable
Binomial experiment
49.
the population mean
Individual
A Random vector
categorical variables
50. Long-term upward or downward movement over time.
The sample space
Trend
Type I errors
The Covariance between two random variables X and Y - with expected values E(X) =