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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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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
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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. Is denoted by - pronounced 'x bar'.
Independence or Statistical independence
A Probability measure
Variable
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
2. Working from a null hypothesis two basic forms of error are recognized:
Lurking variable
Seasonal effect
Type I errors & Type II errors
Marginal distribution
3. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
categorical variables
Quantitative variable
An experimental study
The median value
4. The probability of correctly detecting a false null hypothesis.
Correlation coefficient
A probability space
Descriptive
Power of a test
5. 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}.
Joint distribution
observational study
The sample space
Probability density
6. 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
quantitative variables
Beta value
Parameter - or 'statistical parameter'
Skewness
7. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
Independent Selection
Block
Sample space
8. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.
Atomic event
the population mean
Type II errors
The variance of a random variable
9. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.
Nominal measurements
The sample space
A random variable
hypothesis
10. Gives the probability of events in a probability space.
A Probability measure
The Covariance between two random variables X and Y - with expected values E(X) =
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A statistic
11. 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
P-value
Coefficient of determination
hypothesis
The Covariance between two random variables X and Y - with expected values E(X) =
12. 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
covariance of X and Y
Probability density
hypotheses
Inferential
13. Is its expected value. The mean (or sample mean of a data set is just the average value.
A likelihood function
The Mean of a random variable
A data set
Type I errors & Type II errors
14. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Type 1 Error
Type I errors & Type II errors
Prior probability
A statistic
15. 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.
applied statistics
Parameter
Statistics
descriptive statistics
16. Two variables such that their effects on the response variable cannot be distinguished from each other.
covariance of X and Y
Confounded variables
f(z) - and its cdf by F(z).
observational study
17. Cov[X - Y] :
categorical variables
covariance of X and Y
Individual
A sampling distribution
18. 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
inferential statistics
Residuals
Kurtosis
categorical variables
19. Another name for elementary event.
Coefficient of determination
the sample or population mean
Atomic event
f(z) - and its cdf by F(z).
20. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Estimator
Parameter - or 'statistical parameter'
categorical variables
A population or statistical population
21. Many statistical methods seek to minimize the mean-squared error - and these are called
Simple random sample
quantitative variables
Law of Parsimony
methods of least squares
22. 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.
Lurking variable
Descriptive statistics
A Distribution function
A Probability measure
23. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
applied statistics
Conditional distribution
Mutual independence
24. 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.
s-algebras
Ordinal measurements
Conditional distribution
The median value
25. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Cumulative distribution functions
Probability density functions
the population mean
A statistic
26. Probability of rejecting a true null hypothesis.
Alpha value (Level of Significance)
A probability density function
methods of least squares
Residuals
27. 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.
Pairwise independence
Independent Selection
The variance of a random variable
Greek letters
28. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
29. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data
Variability
Step 3 of a statistical experiment
Sampling
Ratio measurements
30. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Particular realizations of a random variable
P-value
the population variance
An estimate of a parameter
31. 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.
Type I errors
inferential statistics
Experimental and observational studies
A data point
32. (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
nominal - ordinal - interval - and ratio
Standard error
The standard deviation
A likelihood function
33. 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
Binary data
Type II errors
hypothesis
34. 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.
Estimator
f(z) - and its cdf by F(z).
Variable
Marginal probability
35. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
the population cumulants
the population mean
Descriptive
Qualitative variable
36. Is a sample space over which a probability measure has been defined.
A data point
Outlier
A probability space
Divide the sum by the number of values.
37. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no
Probability and statistics
the population variance
Parameter - or 'statistical parameter'
The variance of a random variable
38. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Bias
Descriptive statistics
Pairwise independence
the population mean
39.
Atomic event
the population mean
inferential statistics
categorical variables
40. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
An Elementary event
The Mean of a random variable
Probability
41. Some commonly used symbols for sample statistics
Law of Large Numbers
Greek letters
Type II errors
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
42. 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
Descriptive statistics
The Expected value
Step 1 of a statistical experiment
The Mean of a random variable
43. E[X] :
Ordinal measurements
expected value of X
Statistical dispersion
Type I errors
44. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Credence
The standard deviation
variance of X
Cumulative distribution functions
45. A group of individuals sharing some common features that might affect the treatment.
Block
Pairwise independence
Valid measure
Law of Large Numbers
46. In particular - the pdf of the standard normal distribution is denoted by
Correlation
f(z) - and its cdf by F(z).
Quantitative variable
An estimate of a parameter
47. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
observational study
Inferential statistics
Type II errors
Law of Large Numbers
48. 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.
the population correlation
Simple random sample
A probability distribution
Atomic event
49. 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.
The Range
Conditional distribution
Dependent Selection
Step 3 of a statistical experiment
50. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Marginal probability
A data set
Binomial experiment
An event