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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.
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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. 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.
the population variance
A sample
s-algebras
The variance of a random variable
2. 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.
Statistics
Treatment
Dependent Selection
variance of X
3. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.
Type 1 Error
Statistics
An event
Bias
4. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
An experimental study
Particular realizations of a random variable
Correlation
An estimate of a parameter
5. Failing to reject a false null hypothesis.
categorical variables
That is the median value
Type 2 Error
Individual
6. 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).
Sampling frame
Type I errors & Type II errors
A data point
An event
7. 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.
A random variable
Descriptive
Marginal distribution
Nominal measurements
8. 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
A statistic
A likelihood function
Inferential statistics
Confounded variables
9. 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
The Mean of a random variable
Standard error
Ratio measurements
Statistical adjustment
10. A numerical facsimilie or representation of a real-world phenomenon.
Random variables
A random variable
A likelihood function
Simulation
11. S^2
the population variance
Qualitative variable
Law of Parsimony
Seasonal effect
12. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Dependent Selection
A probability density function
A population or statistical population
Quantitative variable
13. Some commonly used symbols for sample statistics
Correlation coefficient
Statistical adjustment
nominal - ordinal - interval - and ratio
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
14. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Statistic
Sampling Distribution
That value is the median value
Simulation
15. Have imprecise differences between consecutive values - but have a meaningful order to those values
Credence
The standard deviation
applied statistics
Ordinal measurements
16. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
the population correlation
The median value
Ordinal measurements
Posterior probability
17. 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.
descriptive statistics
Reliable measure
Binomial experiment
Experimental and observational studies
18. The proportion of the explained variation by a linear regression model in the total variation.
An experimental study
Confounded variables
Marginal distribution
Coefficient of determination
19. 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.
Nominal measurements
Sampling
Bias
The Mean of a random variable
20. Gives the probability of events in a probability space.
Dependent Selection
A Probability measure
Statistical dispersion
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
21. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Statistics
methods of least squares
Marginal distribution
nominal - ordinal - interval - and ratio
22. Is data that can take only two values - usually represented by 0 and 1.
Binary data
A statistic
The Mean of a random variable
Standard error
23. Data are gathered and correlations between predictors and response are investigated.
Joint distribution
A likelihood function
observational study
An experimental study
24. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Random variables
Divide the sum by the number of values.
The sample space
categorical variables
25. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
the sample or population mean
Sampling frame
Ordinal measurements
quantitative variables
26. E[X] :
The Mean of a random variable
expected value of X
The Expected value
A probability distribution
27. 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
Reliable measure
Descriptive statistics
Qualitative variable
applied statistics
28. The probability of correctly detecting a false null hypothesis.
Simpson's Paradox
hypotheses
variance of X
Power of a test
29. Of a group of numbers is the center point of all those number values.
Sampling
The average - or arithmetic mean
A Statistical parameter
A data point
30. ?
Lurking variable
the population correlation
Pairwise independence
A statistic
31. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
descriptive statistics
An experimental study
Quantitative variable
32. The collection of all possible outcomes in an experiment.
expected value of X
A probability density function
quantitative variables
Sample space
33. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
A Statistical parameter
inferential statistics
Type II errors
Average and arithmetic mean
34. (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.
A Random vector
The average - or arithmetic mean
Prior probability
An Elementary event
35. Var[X] :
A probability distribution
Lurking variable
nominal - ordinal - interval - and ratio
variance of X
36. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Step 2 of a statistical experiment
Sample space
Quantitative variable
37. Is its expected value. The mean (or sample mean of a data set is just the average value.
Type I errors
The Mean of a random variable
The median value
Descriptive
38. Probability of rejecting a true null hypothesis.
Binary data
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Alpha value (Level of Significance)
Type I errors
39. 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.
The sample space
Marginal probability
expected value of X
P-value
40. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
P-value
s-algebras
f(z) - and its cdf by F(z).
An event
41. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
The average - or arithmetic mean
A population or statistical population
Statistics
42. 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 population or statistical population
The median value
Posterior probability
Probability density functions
43. 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.
covariance of X and Y
A population or statistical population
hypotheses
Type II errors
44. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
the sample or population mean
Valid measure
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Likert scale
45. A variable describes an individual by placing the individual into a category or a group.
Qualitative variable
Ordinal measurements
the population correlation
the population mean
46. 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.
P-value
Descriptive statistics
Statistical adjustment
Statistical inference
47. Describes the spread in the values of the sample statistic when many samples are taken.
hypotheses
Credence
Variability
Nominal measurements
48. Another name for elementary event.
Step 3 of a statistical experiment
Divide the sum by the number of values.
Atomic event
The Mean of a random variable
49. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
That value is the median value
quantitative variables
Treatment
Probability density functions
50. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Reliable measure
Greek letters
P-value
Kurtosis