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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. 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
Residuals
Step 3 of a statistical experiment
inferential statistics
Ratio measurements
2. 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).
Kurtosis
Parameter
An event
Reliable measure
3. Working from a null hypothesis two basic forms of error are recognized:
Variable
Greek letters
Type I errors & Type II errors
Likert scale
4. The standard deviation of a sampling distribution.
Descriptive
the population variance
Standard error
Type II errors
5. 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
Power of a test
Correlation
Step 3 of a statistical experiment
Observational study
6. Many statistical methods seek to minimize the mean-squared error - and these are called
Descriptive
methods of least squares
Trend
Correlation
7. 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 I errors & Type II errors
Prior probability
Bias
Treatment
8. A measure that is relevant or appropriate as a representation of that property.
Step 1 of a statistical experiment
Valid measure
Random variables
The Range
9. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
s-algebras
Cumulative distribution functions
the sample or population mean
The Mean of a random variable
10. Of a group of numbers is the center point of all those number values.
Probability and statistics
Atomic event
Experimental and observational studies
The average - or arithmetic mean
11. Statistical methods can be used for summarizing or describing a collection of data; this is called
Binomial experiment
descriptive statistics
the population cumulants
The Expected value
12. Gives the probability of events in a probability space.
A population or statistical population
An event
A Probability measure
Reliable measure
13. The collection of all possible outcomes in an experiment.
Sample space
The Expected value
Residuals
Valid measure
14. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Credence
Law of Parsimony
Quantitative variable
Treatment
15. Is its expected value. The mean (or sample mean of a data set is just the average value.
The Mean of a random variable
Block
Binary data
s-algebras
16. 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.
A probability space
the population cumulants
Type I errors
Simple random sample
17. A numerical measure that describes an aspect of a sample.
expected value of X
Probability density functions
Statistic
Random variables
18. (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.
experimental studies and observational studies.
An Elementary event
Greek letters
Kurtosis
19. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Qualitative variable
nominal - ordinal - interval - and ratio
Joint distribution
Type 2 Error
20. Is denoted by - pronounced 'x bar'.
Reliable measure
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Confounded variables
nominal - ordinal - interval - and ratio
21.
the population mean
Correlation
Confounded variables
Coefficient of determination
22. (cdfs) are denoted by upper case letters - e.g. F(x).
A Statistical parameter
Cumulative distribution functions
Valid measure
Type II errors
23. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Law of Large Numbers
quantitative variables
An estimate of a parameter
Greek letters
24. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
the sample or population mean
Dependent Selection
Individual
categorical variables
25. 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
Inferential statistics
Independence or Statistical independence
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Conditional probability
26. Is the probability distribution - under repeated sampling of the population - of a given statistic.
A Distribution function
A sampling distribution
Statistics
the population cumulants
27. Two variables such that their effects on the response variable cannot be distinguished from each other.
Statistic
Confounded variables
Seasonal effect
Likert scale
28. 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'
the population cumulants
Conditional probability
Inferential statistics
Joint probability
29. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Conditional probability
Individual
The variance of a random variable
Simple random sample
30. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.
That value is the median value
Binary data
Conditional distribution
experimental studies and observational studies.
31. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Seasonal effect
Probability density
Bias
covariance of X and Y
32. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
That is the median value
Pairwise independence
Beta value
33. 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
Beta value
Inferential statistics
experimental studies and observational studies.
Probability density
34. 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
variance of X
Power of a test
Inferential statistics
Coefficient of determination
35. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Bias
An estimate of a parameter
Divide the sum by the number of values.
Cumulative distribution functions
36. Rejecting a true null hypothesis.
Mutual independence
Simple random sample
Type 1 Error
Descriptive
37. A data value that falls outside the overall pattern of the graph.
Simpson's Paradox
Outlier
Observational study
nominal - ordinal - interval - and ratio
38. A subjective estimate of probability.
Credence
Type II errors
Kurtosis
Greek letters
39. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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40. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Conditional distribution
Simulation
Pairwise independence
A probability distribution
41. 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
Prior probability
Binary data
The sample space
Descriptive statistics
42. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.
Lurking variable
Pairwise independence
Independent Selection
Type I errors
43. Any specific experimental condition applied to the subjects
A statistic
Greek letters
Independence or Statistical independence
Treatment
44. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.
methods of least squares
Sampling frame
A population or statistical population
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
45. Have imprecise differences between consecutive values - but have a meaningful order to those values
An Elementary event
Bias
Ordinal measurements
nominal - ordinal - interval - and ratio
46. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
s-algebras
Step 2 of a statistical experiment
Nominal measurements
Law of Large Numbers
47. Long-term upward or downward movement over time.
A Probability measure
Trend
The median value
Kurtosis
48. A measurement such that the random error is small
Binary data
Reliable measure
nominal - ordinal - interval - and ratio
Step 2 of a statistical experiment
49. 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.
hypotheses
Skewness
Nominal measurements
Experimental and observational studies
50. Is the length of the smallest interval which contains all the data.
s-algebras
Descriptive statistics
applied statistics
The Range
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