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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. Data are gathered and correlations between predictors and response are investigated.
Independence or Statistical independence
observational study
Probability density
experimental studies and observational studies.
2. Is a parameter that indexes a family of probability distributions.
Independence or Statistical independence
The Expected value
A Statistical parameter
A likelihood function
3. Cov[X - Y] :
covariance of X and Y
An Elementary event
Qualitative variable
the population mean
4. Some commonly used symbols for population parameters
Sample space
the population mean
Law of Large Numbers
Observational study
5. Gives the probability distribution for a continuous random variable.
Simulation
Descriptive statistics
Ordinal measurements
A probability density function
6. Rejecting a true null hypothesis.
Power of a test
Binomial experiment
Placebo effect
Type 1 Error
7. 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.
Confounded variables
nominal - ordinal - interval - and ratio
The median value
A Probability measure
8. Of a group of numbers is the center point of all those number values.
Sampling Distribution
the population variance
The average - or arithmetic mean
Step 2 of a statistical experiment
9. Where the null hypothesis is falsely rejected giving a 'false positive'.
s-algebras
Type I errors
That is the median value
Statistical adjustment
10. Many statistical methods seek to minimize the mean-squared error - and these are called
Individual
methods of least squares
categorical variables
inferential statistics
11. Some commonly used symbols for sample statistics
Ordinal measurements
the population mean
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
An event
12. Gives the probability of events in a probability space.
A Probability measure
Valid measure
Experimental and observational studies
Type 1 Error
13. 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.
Confounded variables
the population cumulants
expected value of X
Bias
14. Is denoted by - pronounced 'x bar'.
The Range
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Descriptive statistics
Credence
15. ?r
Outlier
The Covariance between two random variables X and Y - with expected values E(X) =
the population cumulants
Prior probability
16. 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
Particular realizations of a random variable
Credence
Experimental and observational studies
17. 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.
the population mean
That value is the median value
Posterior probability
Independence or Statistical independence
18. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
Placebo effect
Lurking variable
Prior probability
19. ?
Null hypothesis
the population correlation
Average and arithmetic mean
Variability
20. When you have two or more competing models - choose the simpler of the two models.
Confounded variables
the sample or population mean
A Statistical parameter
Law of Parsimony
21. Is a sample and the associated data points.
A data set
Type II errors
Step 3 of a statistical experiment
Statistical inference
22. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A statistic
Experimental and observational studies
Inferential statistics
hypothesis
23. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
nominal - ordinal - interval - and ratio
That value is the median value
expected value of X
24. Probability of accepting a false null hypothesis.
hypotheses
A Distribution function
Beta value
A data set
25. 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
the population cumulants
That is the median value
A Distribution function
26. A measurement such that the random error is small
the sample or population mean
Individual
P-value
Reliable measure
27. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
hypothesis
categorical variables
Step 1 of a statistical experiment
Ratio measurements
28. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
The sample space
Statistical dispersion
A random variable
Bias
29. 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 variance of a random variable
Step 3 of a statistical experiment
the sample or population mean
The median value
30. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Independent Selection
Correlation coefficient
Descriptive
descriptive statistics
31. Describes a characteristic of an individual to be measured or observed.
Law of Parsimony
Outlier
Correlation
Variable
32. 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
Statistical inference
The Expected value
inferential statistics
Bias
33. Are simply two different terms for the same thing. Add the given values
Simple random sample
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
the population correlation
Average and arithmetic mean
34. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a
An event
Null hypothesis
Step 1 of a statistical experiment
A Statistical parameter
35. 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
Placebo effect
Independent Selection
Observational study
nominal - ordinal - interval - and ratio
36. E[X] :
expected value of X
Credence
A probability space
Law of Large Numbers
37. 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.
Dependent Selection
Step 3 of a statistical experiment
A Distribution function
Binomial experiment
38. 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.
The Covariance between two random variables X and Y - with expected values E(X) =
observational study
Sampling
Count data
39. The proportion of the explained variation by a linear regression model in the total variation.
Step 3 of a statistical experiment
Average and arithmetic mean
Marginal distribution
Coefficient of determination
40. 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.
Binomial experiment
Seasonal effect
Experimental and observational studies
Mutual independence
41. 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
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
An experimental study
Average and arithmetic mean
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.
Law of Parsimony
Treatment
Independent Selection
Parameter - or 'statistical parameter'
43. 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 Covariance between two random variables X and Y - with expected values E(X) =
Coefficient of determination
An experimental study
44. Probability of rejecting a true null hypothesis.
observational study
Alpha value (Level of Significance)
The Covariance between two random variables X and Y - with expected values E(X) =
Posterior probability
45. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the
Binomial experiment
Placebo effect
quantitative variables
Probability
46. 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
Joint distribution
f(z) - and its cdf by F(z).
Mutual independence
Inferential
47. When there is an even number of values...
That is the median value
Type I errors
Probability density
Power of a test
48. 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
Statistic
A population or statistical population
Step 2 of a statistical experiment
49. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Statistical dispersion
Sampling Distribution
Interval measurements
the population cumulants
50. A group of individuals sharing some common features that might affect the treatment.
Block
A Statistical parameter
Skewness
Individual
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