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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
Start Test
Study First
Subjects
:
clep
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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. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
the population correlation
Statistical inference
Simulation
2. Some commonly used symbols for population parameters
The Expected value
Step 1 of a statistical experiment
observational study
the population mean
3. 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
Correlation coefficient
Sampling frame
Beta value
Descriptive statistics
4. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Sampling frame
Residuals
Qualitative variable
Correlation coefficient
5. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
descriptive statistics
Ordinal measurements
Seasonal effect
quantitative variables
6. 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.
Estimator
Independent Selection
Statistical inference
Seasonal effect
7. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.
inferential statistics
Joint distribution
Correlation
A population or statistical population
8. Is data that can take only two values - usually represented by 0 and 1.
Inferential statistics
Statistical dispersion
Parameter - or 'statistical parameter'
Binary data
9. 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.
the population correlation
Descriptive statistics
Law of Large Numbers
Statistics
10. Var[X] :
Correlation
Statistic
variance of X
A data set
11. E[X] :
Parameter
expected value of X
Conditional probability
An event
12. Is a sample and the associated data points.
Likert scale
A data set
Reliable measure
Interval measurements
13. 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'
A sampling distribution
Estimator
P-value
Conditional probability
14. Two variables such that their effects on the response variable cannot be distinguished from each other.
Random variables
Marginal distribution
Count data
Confounded variables
15. Is the probability distribution - under repeated sampling of the population - of a given statistic.
the population mean
A sampling distribution
An event
Sampling
16. In particular - the pdf of the standard normal distribution is denoted by
The sample space
f(z) - and its cdf by F(z).
Sampling
hypothesis
17. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A sample
Seasonal effect
A statistic
Individual
18. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.
Marginal distribution
An estimate of a parameter
Dependent Selection
Greek letters
19. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
A data set
Law of Parsimony
Outlier
20. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Binomial experiment
Type 1 Error
Inferential statistics
Independent Selection
21. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Experimental and observational studies
Placebo effect
Likert scale
The Expected value
22. Probability of rejecting a true null hypothesis.
the population correlation
Alpha value (Level of Significance)
Type I errors
Nominal measurements
23. Is defined as the expected value of random variable (X -
Skewness
Divide the sum by the number of values.
A probability space
The Covariance between two random variables X and Y - with expected values E(X) =
24. Cov[X - Y] :
The Expected value
An estimate of a parameter
expected value of X
covariance of X and Y
25. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
Sample space
Marginal distribution
Type II errors
26. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
descriptive statistics
The Covariance between two random variables X and Y - with expected values E(X) =
The standard deviation
Individual
27. 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).
observational study
Reliable measure
An event
The sample space
28. 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.
hypotheses
A probability density function
Inferential statistics
A Distribution function
29. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
the population cumulants
An experimental study
Beta value
30. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
nominal - ordinal - interval - and ratio
An event
Credence
variance of X
31. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Probability
Block
A probability distribution
Random variables
32. Failing to reject a false null hypothesis.
the population correlation
Type I errors & Type II errors
Type 2 Error
Type 1 Error
33. 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.
An experimental study
Dependent Selection
An Elementary event
f(z) - and its cdf by F(z).
34. The probability of correctly detecting a false null hypothesis.
A Probability measure
Power of a test
inferential statistics
Experimental and observational studies
35. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
Step 1 of a statistical experiment
Qualitative variable
the population correlation
36. A numerical facsimilie or representation of a real-world phenomenon.
Statistic
Bias
Statistical inference
Simulation
37. ?
That is the median value
Sampling
Type I errors & Type II errors
the population correlation
38. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
categorical variables
Joint probability
Descriptive statistics
Probability density functions
39. 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.
That value is the median value
P-value
Divide the sum by the number of values.
Inferential
40. When there is an even number of values...
applied statistics
Step 2 of a statistical experiment
That is the median value
Sampling frame
41. Where the null hypothesis is falsely rejected giving a 'false positive'.
Skewness
Statistical dispersion
Type I errors
Simulation
42. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
Law of Large Numbers
An estimate of a parameter
Dependent Selection
43. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
A Probability measure
Prior probability
Descriptive statistics
Marginal probability
44. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Statistical dispersion
Inferential
Statistics
Ratio measurements
45. 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
Simple random sample
hypothesis
Reliable measure
The standard deviation
46. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Probability density functions
Law of Large Numbers
the population cumulants
Estimator
47. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
the sample or population mean
Conditional distribution
Joint distribution
Statistical inference
48. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Pairwise independence
Sampling Distribution
A probability density function
Conditional distribution
49. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
An Elementary event
Posterior probability
Probability
the population mean
50. The collection of all possible outcomes in an experiment.
Block
Simple random sample
Type I errors & Type II errors
Sample space