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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. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Nominal measurements
methods of least squares
A sampling distribution
Joint distribution
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
Law of Parsimony
Step 2 of a statistical experiment
Variable
3. 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
the population correlation
methods of least squares
The median value
Observational study
4. 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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5. The probability of correctly detecting a false null hypothesis.
Power of a test
Quantitative variable
Independence or Statistical independence
The variance of a random variable
6. 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 population or statistical population
Joint probability
the population cumulants
7. A subjective estimate of probability.
variance of X
Qualitative variable
Credence
The Covariance between two random variables X and Y - with expected values E(X) =
8. 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.
Ratio measurements
That value is the median value
The Range
Correlation coefficient
9. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a
Type I errors & Type II errors
the sample or population mean
Step 2 of a statistical experiment
Joint probability
10. Is denoted by - pronounced 'x bar'.
The Range
The variance of a random variable
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
The sample space
11. Failing to reject a false null hypothesis.
Type 2 Error
Independence or Statistical independence
Type I errors & Type II errors
Law of Parsimony
12. A numerical facsimilie or representation of a real-world phenomenon.
Random variables
A Statistical parameter
Simulation
Kurtosis
13. Is defined as the expected value of random variable (X -
Pairwise independence
Type I errors & Type II errors
The standard deviation
The Covariance between two random variables X and Y - with expected values E(X) =
14. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
A population or statistical population
Individual
Divide the sum by the number of values.
Coefficient of determination
15. A numerical measure that describes an aspect of a sample.
Simulation
covariance of X and Y
Joint distribution
Statistic
16. 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 II errors
f(z) - and its cdf by F(z).
Probability density functions
Bias
17. 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
descriptive statistics
Independence or Statistical independence
Correlation
18. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Simple random sample
observational study
Bias
A statistic
19. 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'
Statistics
Greek letters
Conditional probability
Sampling
20. 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
Variability
hypothesis
Lurking variable
Outlier
21. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Simple random sample
The Mean of a random variable
Prior probability
Parameter
22. Is the probability of two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).
Outlier
Simpson's Paradox
Joint probability
quantitative variables
23. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
s-algebras
Sampling
Inferential
The Range
24.
Type II errors
Ordinal measurements
the population mean
Independent Selection
25. Are usually written in upper case roman letters: X - Y - etc.
Independent Selection
Random variables
Joint distribution
Coefficient of determination
26. Probability of rejecting a true null hypothesis.
Sampling frame
Alpha value (Level of Significance)
the population cumulants
applied statistics
27. 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
Beta value
Probability and statistics
Random variables
The Covariance between two random variables X and Y - with expected values E(X) =
28. Is a function that gives the probability of all elements in a given space: see List of probability distributions
the population correlation
A probability distribution
hypotheses
A data set
29. 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).
experimental studies and observational studies.
An event
f(z) - and its cdf by F(z).
Experimental and observational studies
30. Of a group of numbers is the center point of all those number values.
Statistic
The average - or arithmetic mean
Lurking variable
Particular realizations of a random variable
31. (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
The Expected value
The Mean of a random variable
The average - or arithmetic mean
Joint distribution
32. 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
A sampling distribution
descriptive statistics
Step 1 of a statistical experiment
Standard error
33. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Statistical dispersion
Reliable measure
The median value
Probability density functions
34. The collection of all possible outcomes in an experiment.
Marginal probability
Null hypothesis
Sample space
A random variable
35. 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
Type 1 Error
Parameter - or 'statistical parameter'
observational study
36. Is data that can take only two values - usually represented by 0 and 1.
Statistical inference
f(z) - and its cdf by F(z).
Binary data
Power of a test
37. 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.
Estimator
Independent Selection
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Null hypothesis
38. 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.
A statistic
Seasonal effect
expected value of X
Sampling
39. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Posterior probability
Independence or Statistical independence
A Random vector
The median value
40. Is the length of the smallest interval which contains all the data.
Joint distribution
Credence
The Range
A Distribution function
41. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Trend
An estimate of a parameter
Bias
The average - or arithmetic mean
42. When there is an even number of values...
the population cumulants
Sampling frame
Valid measure
That is the median value
43. Are simply two different terms for the same thing. Add the given values
A data point
Average and arithmetic mean
Experimental and observational studies
the population mean
44. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Probability density functions
Average and arithmetic mean
Residuals
Placebo effect
45. 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.
The median value
Likert scale
Skewness
the population correlation
46. Rejecting a true null hypothesis.
Sampling Distribution
Type 1 Error
Conditional probability
Seasonal effect
47. 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
Experimental and observational studies
Likert scale
Independence or Statistical independence
Step 2 of a statistical experiment
48. 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.
Inferential statistics
Sample space
A random variable
Binomial experiment
49. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Binomial experiment
Correlation
Probability and statistics
Probability
50. 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
Binomial experiment
Joint probability
hypotheses
Probability density