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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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Subjects
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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
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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.
An estimate of a parameter
Placebo effect
Atomic event
Type 2 Error
2. A variable describes an individual by placing the individual into a category or a group.
That is the median value
A likelihood function
Qualitative variable
Coefficient of determination
3. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
Ratio measurements
Bias
Step 3 of a statistical experiment
4. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
observational study
Type I errors & Type II errors
Individual
categorical variables
5. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
A statistic
Probability
applied statistics
quantitative variables
6. Is the length of the smallest interval which contains all the data.
Quantitative variable
hypothesis
A Random vector
The Range
7. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Cumulative distribution functions
Variability
the population mean
8. Gives the probability of events in a probability space.
covariance of X and Y
Probability and statistics
A Probability measure
A random variable
9. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
A statistic
Sampling Distribution
Type II errors
the sample or population mean
10. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P
Correlation
Descriptive statistics
Binary data
Likert scale
11. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
variance of X
nominal - ordinal - interval - and ratio
Marginal distribution
Parameter
12. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
Ordinal measurements
Ratio measurements
Parameter - or 'statistical parameter'
13. To find the average - or arithmetic mean - of a set of numbers:
Nominal measurements
hypotheses
Sample space
Divide the sum by the number of values.
14. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
Nominal measurements
Confounded variables
The Covariance between two random variables X and Y - with expected values E(X) =
15. Var[X] :
Step 3 of a statistical experiment
A population or statistical population
variance of X
Joint probability
16. Is defined as the expected value of random variable (X -
The Mean of a random variable
the population mean
nominal - ordinal - interval - and ratio
The Covariance between two random variables X and Y - with expected values E(X) =
17. 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
Residuals
Lurking variable
nominal - ordinal - interval - and ratio
Mutual independence
18. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
expected value of X
Probability density functions
Statistical inference
That is the median value
19. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Step 2 of a statistical experiment
Valid measure
Statistics
20. Two variables such that their effects on the response variable cannot be distinguished from each other.
hypothesis
inferential statistics
Coefficient of determination
Confounded variables
21. A numerical measure that describes an aspect of a sample.
Bias
Bias
Qualitative variable
Statistic
22. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Kurtosis
A Random vector
A probability space
Binomial experiment
23. Probability of accepting a false null hypothesis.
Beta value
Descriptive
Null hypothesis
variance of X
24. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Estimator
A data point
Prior probability
Posterior probability
25. 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
experimental studies and observational studies.
A sample
hypothesis
Binomial experiment
26. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
An event
hypotheses
27. The proportion of the explained variation by a linear regression model in the total variation.
Skewness
Coefficient of determination
Conditional probability
Variable
28. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.
Cumulative distribution functions
Sampling
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Mutual independence
29. 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.
s-algebras
Simple random sample
Sampling frame
Confounded variables
30. 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 standard deviation
Dependent Selection
The Range
Statistics
31. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co
Step 2 of a statistical experiment
Greek letters
Step 1 of a statistical experiment
A Random vector
32. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
the population mean
Atomic event
Likert scale
An event
33. ?
the population correlation
The variance of a random variable
Trend
Dependent Selection
34. Rejecting a true null hypothesis.
Probability density functions
methods of least squares
Greek letters
Type 1 Error
35. Statistical methods can be used for summarizing or describing a collection of data; this is called
Step 3 of a statistical experiment
Inferential
Power of a test
descriptive statistics
36. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
Prior probability
the population variance
Ordinal measurements
37.
descriptive statistics
A data point
hypothesis
the population mean
38. 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.
Dependent Selection
Observational study
Bias
Sample space
39. 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 Distribution function
A probability density function
s-algebras
Qualitative variable
40. When you have two or more competing models - choose the simpler of the two models.
A probability density function
Estimator
Law of Parsimony
Ratio measurements
41. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.
Mutual independence
A probability space
An Elementary event
Simulation
42. The collection of all possible outcomes in an experiment.
The Covariance between two random variables X and Y - with expected values E(X) =
Reliable measure
Sample space
A Distribution function
43. Is denoted by - pronounced 'x bar'.
Observational study
Residuals
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Conditional probability
44. Failing to reject a false null hypothesis.
experimental studies and observational studies.
Type 2 Error
An event
Valid measure
45. 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.
An estimate of a parameter
inferential statistics
Bias
A population or statistical population
46. (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
covariance of X and Y
f(z) - and its cdf by F(z).
The median value
A likelihood function
47. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.
A probability space
Conditional distribution
Type I errors
Step 2 of a statistical experiment
48. 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
Experimental and observational studies
Cumulative distribution functions
Ratio measurements
hypotheses
49. 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 data set
Seasonal effect
Sampling
covariance of X and Y
50. Cov[X - Y] :
A Probability measure
observational study
Treatment
covariance of X and Y