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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.
If you are not ready to take this test, you can
study here
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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. 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 Probability measure
experimental studies and observational studies.
Estimator
The Range
2. 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
Independence or Statistical independence
quantitative variables
Coefficient of determination
Residuals
3. 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
Sample space
Binary data
Correlation
inferential statistics
4. Are simply two different terms for the same thing. Add the given values
Statistical inference
Individual
Simpson's Paradox
Average and arithmetic mean
5. 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
Binomial experiment
The average - or arithmetic mean
hypotheses
An estimate of a parameter
6. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
quantitative variables
Joint probability
Standard error
An event
7. 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 Covariance between two random variables X and Y - with expected values E(X) =
Treatment
covariance of X and Y
An event
8. A group of individuals sharing some common features that might affect the treatment.
Type 2 Error
A statistic
Block
Mutual independence
9. Can be a population parameter - a distribution parameter - an unobserved parameter (with different shades of meaning). In statistics - this is often a quantity to be estimated.
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10. 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.
experimental studies and observational studies.
inferential statistics
Marginal distribution
That value is the median value
11.
Statistical adjustment
Residuals
Law of Large Numbers
the population mean
12. 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.
covariance of X and Y
Cumulative distribution functions
Sampling
A random variable
13. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Divide the sum by the number of values.
Qualitative variable
Descriptive
That is the median value
14. Have no meaningful rank order among values.
the population mean
Valid measure
Nominal measurements
Law of Large Numbers
15. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Estimator
The Range
A population or statistical population
Placebo effect
16. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
A random variable
Binomial experiment
Step 2 of a statistical experiment
Individual
17. Describes a characteristic of an individual to be measured or observed.
Variable
Probability density
An event
A data point
18. ?r
Statistical inference
the population cumulants
Qualitative variable
A probability density function
19. Is the probability of two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).
Placebo effect
Probability and statistics
Joint probability
Reliable measure
20. The standard deviation of a sampling distribution.
Skewness
Experimental and observational studies
That value is the median value
Standard error
21. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Residuals
Nominal measurements
Step 1 of a statistical experiment
Pairwise independence
22. 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 median value
Atomic event
A data set
Sampling
23. 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.
Experimental and observational studies
Reliable measure
Observational study
Statistics
24. Gives the probability distribution for a continuous random variable.
Quantitative variable
A probability density function
observational study
Ratio measurements
25. A numerical facsimilie or representation of a real-world phenomenon.
Variable
Marginal distribution
variance of X
Simulation
26. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Particular realizations of a random variable
Lurking variable
quantitative variables
hypotheses
27. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Null hypothesis
applied statistics
Correlation
That value is the median value
28. Is a function that gives the probability of all elements in a given space: see List of probability distributions
hypotheses
A probability distribution
categorical variables
The median value
29. Is a sample and the associated data points.
the population cumulants
Cumulative distribution functions
A data set
An estimate of a parameter
30. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
An event
hypothesis
Quantitative variable
Probability and statistics
31. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Reliable measure
A Random vector
Probability density
Type I errors
32. 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.
Interval measurements
Estimator
Type 1 Error
Marginal distribution
33. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Atomic event
f(z) - and its cdf by F(z).
Type II errors
nominal - ordinal - interval - and ratio
34. A measure that is relevant or appropriate as a representation of that property.
Joint probability
The Mean of a random variable
Probability and statistics
Valid measure
35. 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.
Statistical inference
Seasonal effect
nominal - ordinal - interval - and ratio
Outlier
36. A data value that falls outside the overall pattern of the graph.
Probability and statistics
Outlier
The variance of a random variable
Type I errors & Type II errors
37. 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.
Reliable measure
Variability
Kurtosis
Observational study
38. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
Type I errors & Type II errors
s-algebras
Statistics
Independence or Statistical independence
39. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
Mutual independence
Conditional distribution
the population mean
40. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)
Individual
An estimate of a parameter
Interval measurements
Greek letters
41. 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.
Qualitative variable
hypothesis
A data point
expected value of X
42. 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
Experimental and observational studies
Inferential statistics
Count data
Particular realizations of a random variable
43. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Estimator
The sample space
The standard deviation
Experimental and observational studies
44. Failing to reject a false null hypothesis.
Type 2 Error
The variance of a random variable
Individual
Nominal measurements
45. 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.
Independent Selection
inferential statistics
Credence
expected value of X
46. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Random variables
Sampling Distribution
Type 1 Error
Statistic
47. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Quantitative variable
Likert scale
Simple random sample
Law of Parsimony
48. A variable describes an individual by placing the individual into a category or a group.
The median value
Qualitative variable
Correlation coefficient
Sampling frame
49. ?
the population correlation
Independent Selection
Bias
An Elementary event
50. Of a group of numbers is the center point of all those number values.
Sample space
The variance of a random variable
Block
The average - or arithmetic mean