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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. 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.
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2. 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
That is the median value
Likert scale
Ratio measurements
A data point
3. Rejecting a true null hypothesis.
Treatment
descriptive statistics
Binomial experiment
Type 1 Error
4. A measure that is relevant or appropriate as a representation of that property.
Placebo effect
Treatment
Type I errors & Type II errors
Valid measure
5. 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.
Outlier
A probability density function
Statistics
Coefficient of determination
6. Is data that can take only two values - usually represented by 0 and 1.
The Expected value
Binary data
Type 2 Error
Parameter - or 'statistical parameter'
7. 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.
Type 1 Error
Conditional distribution
Sample space
Prior probability
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.
Greek letters
Type I errors
Estimator
The variance of a random variable
9. 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
Seasonal effect
Inferential statistics
hypotheses
Prior probability
10. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Marginal distribution
Type 1 Error
A Random vector
A sampling distribution
11. Failing to reject a false null hypothesis.
Kurtosis
Type 2 Error
A Distribution function
Law of Parsimony
12. 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 sample
f(z) - and its cdf by F(z).
An event
Statistical dispersion
13. Var[X] :
variance of X
An estimate of a parameter
Descriptive statistics
Null hypothesis
14. The probability of correctly detecting a false null hypothesis.
Power of a test
Sampling
Observational study
Correlation coefficient
15. Is data arising from counting that can take only non-negative integer values.
Descriptive
Parameter
Count data
Reliable measure
16. 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.
Confounded variables
Marginal distribution
Power of a test
Divide the sum by the number of values.
17. Some commonly used symbols for sample statistics
A population or statistical population
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Beta value
Seasonal effect
18. 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'
Conditional probability
Joint distribution
hypothesis
the population correlation
19. 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.
quantitative variables
f(z) - and its cdf by F(z).
Simpson's Paradox
20. Have imprecise differences between consecutive values - but have a meaningful order to those values
Sample space
Posterior probability
Ordinal measurements
P-value
21. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
The Covariance between two random variables X and Y - with expected values E(X) =
P-value
Marginal distribution
22. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Cumulative distribution functions
Type II errors
The Expected value
Outlier
23. A list of individuals from which the sample is actually selected.
Pairwise independence
Sampling frame
the population variance
Statistical dispersion
24. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Particular realizations of a random variable
Independent Selection
Cumulative distribution functions
A probability density function
25. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Random variables
descriptive statistics
nominal - ordinal - interval - and ratio
Type I errors & Type II errors
26. Gives the probability distribution for a continuous random variable.
The Expected value
the population cumulants
A probability density function
An experimental study
27. E[X] :
Interval measurements
expected value of X
A Probability measure
descriptive statistics
28. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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29. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Quantitative variable
Statistical adjustment
Statistical dispersion
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
30. Data are gathered and correlations between predictors and response are investigated.
Independence or Statistical independence
Count data
hypotheses
observational study
31. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
A Random vector
Statistical dispersion
An estimate of a parameter
Kurtosis
32. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
nominal - ordinal - interval - and ratio
An Elementary event
A statistic
33. Is the probability distribution - under repeated sampling of the population - of a given statistic.
the population correlation
Nominal measurements
Sample space
A sampling distribution
34. 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}.
Descriptive
Trend
The sample space
An Elementary event
35. Is its expected value. The mean (or sample mean of a data set is just the average value.
The Mean of a random variable
A data point
Treatment
A probability space
36. 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
hypothesis
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A Statistical parameter
The Mean of a random variable
37. Probability of rejecting a true null hypothesis.
Alpha value (Level of Significance)
Binary data
the population mean
Ordinal measurements
38. Is that part of a population which is actually observed.
A sample
Posterior probability
the population cumulants
A sampling distribution
39. In particular - the pdf of the standard normal distribution is denoted by
Seasonal effect
A random variable
f(z) - and its cdf by F(z).
A Probability measure
40. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o
Alpha value (Level of Significance)
Ratio measurements
Inferential statistics
Observational study
41. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
An Elementary event
Probability density functions
Descriptive
Type I errors & Type II errors
42. Describes a characteristic of an individual to be measured or observed.
Ratio measurements
A Distribution function
Step 1 of a statistical experiment
Variable
43. 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
Ratio measurements
Outlier
A probability density function
Correlation
44. Working from a null hypothesis two basic forms of error are recognized:
Block
Credence
Statistics
Type I errors & Type II errors
45. Is the length of the smallest interval which contains all the data.
Residuals
Type II errors
Standard error
The Range
46. Is a sample and the associated data points.
Divide the sum by the number of values.
variance of X
A data set
Alpha value (Level of Significance)
47. Describes the spread in the values of the sample statistic when many samples are taken.
Law of Parsimony
Variability
applied statistics
hypotheses
48. A numerical measure that describes an aspect of a population.
Parameter
Mutual independence
An experimental study
Estimator
49. (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
Greek letters
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Skewness
The Expected value
50. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
Correlation coefficient
An event
The Range
A data point