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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. A numerical measure that assesses the strength of a linear relationship between two variables.
Descriptive
Correlation coefficient
Marginal distribution
the population mean
2. 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.
Alpha value (Level of Significance)
Estimator
Ordinal measurements
Trend
3. Is its expected value. The mean (or sample mean of a data set is just the average value.
Variable
Residuals
A Random vector
The Mean of a random variable
4. 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
nominal - ordinal - interval - and ratio
A probability space
Binary data
Independence or Statistical independence
5. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Null hypothesis
Lurking variable
Binomial experiment
Residuals
6. Is data arising from counting that can take only non-negative integer values.
Nominal measurements
Count data
hypothesis
Sampling Distribution
7. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
hypotheses
Individual
Observational study
the population correlation
8. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.
observational study
categorical variables
Greek letters
A Distribution function
9. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
s-algebras
Probability density functions
the population variance
A sampling distribution
10. 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.
Marginal distribution
Block
Joint probability
Simple random sample
11. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Quantitative variable
Experimental and observational studies
Residuals
Bias
12. Statistical methods can be used for summarizing or describing a collection of data; this is called
Quantitative variable
Inferential
Cumulative distribution functions
descriptive statistics
13. ?r
A Distribution function
An experimental study
the population cumulants
Random variables
14. Are usually written in upper case roman letters: X - Y - etc.
Random variables
Law of Large Numbers
A random variable
f(z) - and its cdf by F(z).
15. Two variables such that their effects on the response variable cannot be distinguished from each other.
Treatment
Power of a test
Likert scale
Confounded variables
16. 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
Greek letters
Coefficient of determination
Atomic event
Observational study
17. Is the length of the smallest interval which contains all the data.
Joint distribution
hypothesis
Descriptive statistics
The Range
18. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
The standard deviation
Lurking variable
Independence or Statistical independence
Particular realizations of a random variable
19. 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
Probability density
The sample space
Lurking variable
Type 1 Error
20. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
Reliable measure
Type 2 Error
A sampling distribution
21. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
hypothesis
Bias
The Mean of a random variable
A statistic
22. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Greek letters
An event
Ratio measurements
quantitative variables
23. 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
the population variance
Mutual independence
quantitative variables
Step 3 of a statistical experiment
24. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Statistical dispersion
Residuals
Step 1 of a statistical experiment
Null hypothesis
25. A list of individuals from which the sample is actually selected.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Ratio measurements
Sampling frame
experimental studies and observational studies.
26. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Random variables
Sampling
Bias
Residuals
27. Is denoted by - pronounced 'x bar'.
Mutual independence
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Estimator
The variance of a random variable
28. S^2
Step 1 of a statistical experiment
the population variance
Probability density functions
Descriptive
29. 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
methods of least squares
Probability and statistics
The Expected value
Law of Parsimony
30. A numerical measure that describes an aspect of a sample.
Null hypothesis
Statistic
the population correlation
Simpson's Paradox
31. Var[X] :
A probability density function
Mutual independence
variance of X
A probability space
32. 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
Correlation
Qualitative variable
Step 2 of a statistical experiment
Residuals
33. (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.
An estimate of a parameter
Inferential statistics
An Elementary event
Cumulative distribution functions
34. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
the population mean
quantitative variables
The Mean of a random variable
Ordinal measurements
35. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
Inferential
Likert scale
A data point
36. 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}.
descriptive statistics
The sample space
Statistic
the population variance
37. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Bias
Statistical adjustment
Step 1 of a statistical experiment
Greek letters
38. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
Qualitative variable
Alpha value (Level of Significance)
Likert scale
39. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
A sample
nominal - ordinal - interval - and ratio
observational study
s-algebras
40. 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
Qualitative variable
Inferential statistics
the population correlation
Sample space
41. A measurement such that the random error is small
Reliable measure
The average - or arithmetic mean
hypothesis
Block
42. A subjective estimate of probability.
Sampling frame
A probability space
Credence
Joint probability
43. 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.
Dependent Selection
Conditional distribution
Greek letters
the population mean
44. A numerical facsimilie or representation of a real-world phenomenon.
Block
Power of a test
Simulation
Probability and statistics
45. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Residuals
Sampling Distribution
methods of least squares
Joint distribution
46. Gives the probability of events in a probability space.
Kurtosis
A Probability measure
methods of least squares
applied statistics
47. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
Coefficient of determination
Probability density
The Covariance between two random variables X and Y - with expected values E(X) =
48. Where the null hypothesis is falsely rejected giving a 'false positive'.
A sampling distribution
Statistical adjustment
Posterior probability
Type I errors
49. Are simply two different terms for the same thing. Add the given values
A Probability measure
Power of a test
Average and arithmetic mean
The Covariance between two random variables X and Y - with expected values E(X) =
50. 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
Standard error
Coefficient of determination
Mutual independence
Type 2 Error