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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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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. 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
Lurking variable
hypotheses
Correlation
Step 2 of a statistical experiment
2. 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.
Placebo effect
quantitative variables
Seasonal effect
Residuals
3. Is data that can take only two values - usually represented by 0 and 1.
Binary data
the population mean
Ordinal measurements
Variable
4. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
A Statistical parameter
Observational study
Residuals
Type 1 Error
5. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive statistics
The Range
Descriptive
hypothesis
6. Gives the probability distribution for a continuous random variable.
categorical variables
The median value
A probability density function
Kurtosis
7. 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.
That value is the median value
Estimator
Quantitative variable
Correlation coefficient
8. Cov[X - Y] :
Type 1 Error
covariance of X and Y
Skewness
the sample or population mean
9. (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.
Independence or Statistical independence
Correlation coefficient
An Elementary event
Law of Large Numbers
10. (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
A probability density function
The Expected value
Statistical adjustment
11. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Probability density
Statistical inference
Binomial experiment
An estimate of a parameter
12. 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
Sampling Distribution
Conditional distribution
A population or statistical population
13. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
nominal - ordinal - interval - and ratio
A random variable
A Probability measure
Seasonal effect
14. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
The average - or arithmetic mean
Statistical inference
A data set
A statistic
15. 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
Nominal measurements
Simulation
Step 1 of a statistical experiment
16. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Probability density
applied statistics
Descriptive statistics
Bias
17. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Random vector
inferential statistics
experimental studies and observational studies.
Block
18. 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.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Skewness
A Random vector
Lurking variable
19. 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.
An Elementary event
Dependent Selection
Coefficient of determination
Binomial experiment
20. ?r
Bias
methods of least squares
the population cumulants
covariance of X and Y
21. 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).
An event
Parameter - or 'statistical parameter'
Divide the sum by the number of values.
A likelihood function
22. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
Marginal distribution
Skewness
Type I errors
23. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.
Type I errors
Average and arithmetic mean
An experimental study
expected value of X
24. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Marginal probability
Statistic
Bias
Greek letters
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.
Nominal measurements
P-value
An estimate of a parameter
26. 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
Average and arithmetic mean
Ratio measurements
Parameter
hypotheses
27. 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.
Statistics
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Independent Selection
Simple random sample
28. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Sample space
An Elementary event
Placebo effect
Skewness
29. A numerical measure that describes an aspect of a population.
Parameter
Alpha value (Level of Significance)
Average and arithmetic mean
the population mean
30. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit
A Random vector
Pairwise independence
Parameter
Probability density
31. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. 4. Further examining the data set in secondary analyses - to suggest new hypotheses for future study. 5. Documenting and present
Statistical dispersion
Step 3 of a statistical experiment
Inferential
Probability
32. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
P-value
Pairwise independence
A population or statistical population
the population mean
33. 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.
Posterior probability
A data point
the population correlation
Independence or Statistical independence
34. Two variables such that their effects on the response variable cannot be distinguished from each other.
The Range
expected value of X
Confounded variables
the population correlation
35. A numerical facsimilie or representation of a real-world phenomenon.
nominal - ordinal - interval - and ratio
Simulation
P-value
Pairwise independence
36. A subjective estimate of probability.
Prior probability
The average - or arithmetic mean
applied statistics
Credence
37. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Sample space
A sample
Descriptive statistics
Statistical dispersion
38. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.
Kurtosis
Placebo effect
Statistical dispersion
The Range
39. 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.
the population cumulants
Experimental and observational studies
the population mean
Statistic
40. Is its expected value. The mean (or sample mean of a data set is just the average value.
Alpha value (Level of Significance)
The Mean of a random variable
A probability space
Skewness
41. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
the population variance
Parameter - or 'statistical parameter'
Probability and statistics
42. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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43. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
Prior probability
A Random vector
inferential statistics
44. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Joint distribution
Quantitative variable
Placebo effect
The Range
45. Is that part of a population which is actually observed.
A Distribution function
A sample
Interval measurements
Descriptive statistics
46. When you have two or more competing models - choose the simpler of the two models.
Descriptive
Dependent Selection
Law of Parsimony
Step 1 of a statistical experiment
47. Probability of rejecting a true null hypothesis.
Alpha value (Level of Significance)
Bias
A data set
Power of a test
48. Data are gathered and correlations between predictors and response are investigated.
covariance of X and Y
observational study
Statistic
methods of least squares
49. Failing to reject a false null hypothesis.
Sampling
Atomic event
Type 2 Error
Prior probability
50. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
methods of least squares
P-value
inferential statistics
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