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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. 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.
Experimental and observational studies
hypothesis
Step 3 of a statistical experiment
Seasonal effect
2. 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
Power of a test
Correlation
Binary data
A Statistical parameter
3. A data value that falls outside the overall pattern of the graph.
observational study
Outlier
Simple random sample
That value is the median value
4. Is its expected value. The mean (or sample mean of a data set is just the average value.
The Mean of a random variable
Dependent Selection
Pairwise independence
the population mean
5. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Sampling Distribution
Type 1 Error
the population correlation
Sampling
6. 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
The sample space
Coefficient of determination
An Elementary event
7. A measurement such that the random error is small
Simple random sample
Reliable measure
methods of least squares
the population mean
8. 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
Interval measurements
Observational study
Descriptive
Probability density
9. 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
hypotheses
A Distribution function
Sample space
Sampling
10. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Block
Greek letters
the population correlation
Law of Parsimony
11. ?
Particular realizations of a random variable
Cumulative distribution functions
the population correlation
A data set
12. 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
Statistical adjustment
Step 1 of a statistical experiment
Law of Parsimony
Conditional probability
13. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
Nominal measurements
Seasonal effect
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
14. A list of individuals from which the sample is actually selected.
Type 1 Error
Ordinal measurements
Residuals
Sampling frame
15. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
quantitative variables
Pairwise independence
The standard deviation
A statistic
16. 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
Sampling Distribution
Outlier
Parameter - or 'statistical parameter'
17. 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).
Statistics
Beta value
An event
Law of Parsimony
18. A numerical facsimilie or representation of a real-world phenomenon.
Skewness
Kurtosis
Treatment
Simulation
19. (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
categorical variables
The Expected value
hypothesis
Null hypothesis
20. 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
Step 3 of a statistical experiment
Likert scale
Inferential statistics
the population mean
21. A numerical measure that describes an aspect of a population.
Variability
Particular realizations of a random variable
Confounded variables
Parameter
22. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
The Range
Simulation
Inferential
Law of Parsimony
23. 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.
A sampling distribution
the sample or population mean
Marginal probability
categorical variables
24. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.
Atomic event
Lurking variable
Posterior probability
Parameter
25. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Trend
A sampling distribution
Alpha value (Level of Significance)
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
26. 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
observational study
The average - or arithmetic mean
the population variance
27. Have imprecise differences between consecutive values - but have a meaningful order to those values
Law of Parsimony
Residuals
Ordinal measurements
Binomial experiment
28. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)
Interval measurements
Bias
quantitative variables
Sampling frame
29. Is data arising from counting that can take only non-negative integer values.
The sample space
Coefficient of determination
Count data
Marginal distribution
30. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.
Inferential statistics
Estimator
Independence or Statistical independence
experimental studies and observational studies.
31. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Random variables
Posterior probability
Standard error
Step 3 of a statistical experiment
32. Is denoted by - pronounced 'x bar'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Parameter - or 'statistical parameter'
Ratio measurements
The Range
33. Probability of rejecting a true null hypothesis.
Lurking variable
A sampling distribution
Individual
Alpha value (Level of Significance)
34. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
A probability distribution
categorical variables
Sampling
descriptive statistics
35. 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
Outlier
Probability density
Likert scale
Ratio measurements
36. 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
s-algebras
inferential statistics
Binary data
Type I errors & Type II errors
37. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Lurking variable
A Random vector
Statistical inference
Statistics
38. (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.
The standard deviation
Observational study
An Elementary event
A probability space
39. Is the length of the smallest interval which contains all the data.
Marginal distribution
categorical variables
Divide the sum by the number of values.
The Range
40. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
categorical variables
Lurking variable
Pairwise independence
A random variable
41. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Bias
Lurking variable
Type I errors & Type II errors
Quantitative variable
42. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Random variables
That value is the median value
Ratio measurements
Placebo effect
43. E[X] :
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
expected value of X
A data point
Inferential
44. The proportion of the explained variation by a linear regression model in the total variation.
Reliable measure
Coefficient of determination
Count data
Alpha value (Level of Significance)
45. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
the population mean
Probability and statistics
experimental studies and observational studies.
A statistic
46. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
the sample or population mean
Parameter - or 'statistical parameter'
Null hypothesis
the population variance
47. When you have two or more competing models - choose the simpler of the two models.
Posterior probability
Law of Parsimony
Correlation coefficient
Step 1 of a statistical experiment
48. Another name for elementary event.
Atomic event
the population variance
A population or statistical population
Beta value
49. The probability of correctly detecting a false null hypothesis.
Power of a test
An event
The standard deviation
A data point
50. 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.
An event
Correlation coefficient
The variance of a random variable
An estimate of a parameter