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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. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
categorical variables
Qualitative variable
Ordinal measurements
2. E[X] :
Particular realizations of a random variable
expected value of X
Descriptive statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
3. Is data that can take only two values - usually represented by 0 and 1.
covariance of X and Y
Coefficient of determination
Marginal probability
Binary data
4. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
nominal - ordinal - interval - and ratio
Conditional probability
Alpha value (Level of Significance)
Pairwise independence
5. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
Type II errors
Quantitative variable
Variability
6. 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.
Simulation
Trend
Count data
Kurtosis
7. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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8. Is its expected value. The mean (or sample mean of a data set is just the average value.
The Mean of a random variable
Independence or Statistical independence
Ratio measurements
A random variable
9. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Sample space
Coefficient of determination
A sampling distribution
Interval measurements
10. 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.
A sampling distribution
Observational study
s-algebras
Sampling
11. 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.
hypotheses
Bias
An estimate of a parameter
variance of X
12. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Simulation
Cumulative distribution functions
A data point
13. 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.
the population correlation
the population mean
An experimental study
Correlation
14. 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
Treatment
Conditional distribution
Descriptive statistics
A sampling distribution
15. 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.
Binary data
Quantitative variable
Type 1 Error
That value is the median value
16. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Conditional probability
The Range
Step 1 of a statistical experiment
Joint distribution
17. 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
Binomial experiment
Power of a test
A Distribution function
18. Is the length of the smallest interval which contains all the data.
The Range
Qualitative variable
Cumulative distribution functions
A probability space
19. 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
Step 3 of a statistical experiment
A Probability measure
Simple random sample
Kurtosis
20. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data
Simpson's Paradox
Sampling frame
Ratio measurements
the population cumulants
21. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
Bias
Descriptive
Confounded variables
22. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
Nominal measurements
That value is the median value
methods of least squares
23. 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
Skewness
Inferential
The average - or arithmetic mean
Bias
24. S^2
Sampling
the population variance
Independent Selection
expected value of X
25. Another name for elementary event.
The Mean of a random variable
Atomic event
A Statistical parameter
Simpson's Paradox
26. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.
Step 3 of a statistical experiment
Block
Marginal distribution
Statistical inference
27. 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
experimental studies and observational studies.
A Distribution function
Type I errors
28. Some commonly used symbols for population parameters
the population mean
Null hypothesis
Power of a test
Sampling frame
29. 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
Reliable measure
inferential statistics
the population correlation
Average and arithmetic mean
30. 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.
Outlier
Null hypothesis
variance of X
Seasonal effect
31. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies
A random variable
Step 1 of a statistical experiment
the population mean
experimental studies and observational studies.
32. In particular - the pdf of the standard normal distribution is denoted by
The variance of a random variable
f(z) - and its cdf by F(z).
the population cumulants
Inferential
33. Where the null hypothesis is falsely rejected giving a 'false positive'.
Posterior probability
the sample or population mean
Seasonal effect
Type I errors
34. Have no meaningful rank order among values.
Parameter - or 'statistical parameter'
Binomial experiment
Nominal measurements
Reliable measure
35. A numerical measure that describes an aspect of a population.
Prior probability
A Statistical parameter
Variable
Parameter
36. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Dependent Selection
An estimate of a parameter
Residuals
Skewness
37. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Particular realizations of a random variable
A data point
A probability distribution
Nominal measurements
38. The probability of correctly detecting a false null hypothesis.
Independence or Statistical independence
Sample space
Step 3 of a statistical experiment
Power of a test
39. 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 Covariance between two random variables X and Y - with expected values E(X) =
Ordinal measurements
Residuals
Observational study
40. 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
Null hypothesis
Valid measure
That is the median value
Ordinal measurements
41. Describes a characteristic of an individual to be measured or observed.
Variable
The Range
the population mean
Step 3 of a statistical experiment
42. 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
That is the median value
Binomial experiment
A probability distribution
hypotheses
43. A subjective estimate of probability.
Nominal measurements
Credence
Confounded variables
Law of Parsimony
44. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.
Marginal distribution
The Covariance between two random variables X and Y - with expected values E(X) =
A population or statistical population
Simulation
45. Any specific experimental condition applied to the subjects
Individual
A probability distribution
Count data
Treatment
46. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
categorical variables
Statistical inference
A Probability measure
Cumulative distribution functions
47. Gives the probability distribution for a continuous random variable.
expected value of X
A probability density function
Count data
Quantitative variable
48. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Qualitative variable
Inferential
Simple random sample
Likert scale
49. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
covariance of X and Y
Law of Large Numbers
Variability
Simple random sample
50. Gives the probability of events in a probability space.
Dependent Selection
Inferential statistics
Descriptive
A Probability measure