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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.
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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. Of a group of numbers is the center point of all those number values.
A data set
The average - or arithmetic mean
Correlation
Residuals
2. Data are gathered and correlations between predictors and response are investigated.
Step 3 of a statistical experiment
variance of X
Valid measure
observational study
3. Probability of accepting a false null hypothesis.
A likelihood function
Step 1 of a statistical experiment
Law of Parsimony
Beta value
4. Is a sample space over which a probability measure has been defined.
Ordinal measurements
A probability space
Null hypothesis
A Probability measure
5. Any specific experimental condition applied to the subjects
Joint probability
Atomic event
Simpson's Paradox
Treatment
6. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
The standard deviation
Type 1 Error
Conditional distribution
hypothesis
7. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
The Mean of a random variable
Parameter
Interval measurements
8. 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.
Seasonal effect
experimental studies and observational studies.
Placebo effect
Law of Parsimony
9. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Interval measurements
A data set
Divide the sum by the number of values.
Particular realizations of a random variable
10. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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11. Describes a characteristic of an individual to be measured or observed.
Joint distribution
Trend
Variable
covariance of X and Y
12. Is defined as the expected value of random variable (X -
That value is the median value
The Covariance between two random variables X and Y - with expected values E(X) =
Type 1 Error
Posterior probability
13. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
hypothesis
That is the median value
Type I errors & Type II errors
Individual
14. 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.
Bias
The variance of a random variable
Probability density functions
Variable
15. A numerical measure that assesses the strength of a linear relationship between two variables.
Parameter
Power of a test
Type I errors & Type II errors
Correlation coefficient
16. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Credence
hypotheses
Bias
Step 3 of a statistical experiment
17. 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.
Bias
Outlier
the sample or population mean
An experimental study
18. S^2
Coefficient of determination
the population variance
A probability distribution
Outlier
19. A numerical measure that describes an aspect of a sample.
Law of Parsimony
Likert scale
The Covariance between two random variables X and Y - with expected values E(X) =
Statistic
20. 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.
covariance of X and Y
That value is the median value
Binomial experiment
An estimate of a parameter
21. The proportion of the explained variation by a linear regression model in the total variation.
Observational study
Seasonal effect
Coefficient of determination
Kurtosis
22. Some commonly used symbols for population parameters
A Statistical parameter
the population mean
Greek letters
The average - or arithmetic mean
23. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Atomic event
Probability density functions
Statistical adjustment
Null hypothesis
24. 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
Estimator
Probability density
Simple random sample
Variable
25. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Block
A probability density function
Inferential
The Expected value
26. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl
Qualitative variable
A sampling distribution
Mutual independence
Residuals
27. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
quantitative variables
Correlation coefficient
Type 1 Error
Observational study
28. 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.
Joint distribution
methods of least squares
Sampling
Nominal measurements
29. (cdfs) are denoted by upper case letters - e.g. F(x).
the sample or population mean
Simpson's Paradox
Cumulative distribution functions
covariance of X and Y
30. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Variable
A Random vector
The Range
Prior probability
31. 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
Step 2 of a statistical experiment
An event
Step 3 of a statistical experiment
Greek letters
32. Is the length of the smallest interval which contains all the data.
The Range
Posterior probability
A random variable
inferential statistics
33. A measure that is relevant or appropriate as a representation of that property.
Ordinal measurements
Valid measure
An estimate of a parameter
A Distribution function
34. In particular - the pdf of the standard normal distribution is denoted by
quantitative variables
f(z) - and its cdf by F(z).
Law of Large Numbers
Average and arithmetic mean
35. Gives the probability distribution for a continuous random variable.
Trend
Law of Large Numbers
the population correlation
A probability density function
36. 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
Marginal probability
Descriptive statistics
P-value
applied statistics
37. The probability of correctly detecting a false null hypothesis.
Sampling Distribution
An estimate of a parameter
s-algebras
Power of a test
38. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
s-algebras
Binomial experiment
Step 1 of a statistical experiment
Step 2 of a statistical experiment
39. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
A probability distribution
Binary data
Probability density functions
s-algebras
40. 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.
A random variable
Qualitative variable
The Mean of a random variable
Type 2 Error
41. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe
applied statistics
Individual
Joint distribution
Skewness
42. Cov[X - Y] :
Ratio measurements
Greek letters
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
covariance of X and Y
43. 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).
the population correlation
An event
Particular realizations of a random variable
Coefficient of determination
44. 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.
Statistical dispersion
The median value
Joint distribution
applied statistics
45. Gives the probability of events in a probability space.
Placebo effect
Residuals
Outlier
A Probability measure
46. Are simply two different terms for the same thing. Add the given values
Descriptive
Average and arithmetic mean
Statistical dispersion
nominal - ordinal - interval - and ratio
47. A group of individuals sharing some common features that might affect the treatment.
Confounded variables
P-value
Power of a test
Block
48. Rejecting a true null hypothesis.
applied statistics
Type 2 Error
Atomic event
Type 1 Error
49. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Alpha value (Level of Significance)
Step 2 of a statistical experiment
Bias
Likert scale
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
A Probability measure
the population correlation
Bias
hypotheses
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