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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. Long-term upward or downward movement over time.
Placebo effect
Trend
An Elementary event
Prior probability
2. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
Skewness
Coefficient of determination
Pairwise independence
3. 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.
applied statistics
inferential statistics
Experimental and observational studies
hypothesis
4. The standard deviation of a sampling distribution.
That is the median value
Standard error
A probability space
Inferential statistics
5. 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.
Step 1 of a statistical experiment
Statistical adjustment
Credence
A random variable
6. 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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7. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Type 1 Error
Individual
Parameter
8. 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
Probability density
Probability density functions
Inferential statistics
Average and arithmetic mean
9. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Type I errors
Law of Large Numbers
Kurtosis
Correlation
10. Probability of accepting a false null hypothesis.
That value is the median value
Beta value
A random variable
Ratio measurements
11. In particular - the pdf of the standard normal distribution is denoted by
f(z) - and its cdf by F(z).
The variance of a random variable
Qualitative variable
The average - or arithmetic mean
12. ?
The average - or arithmetic mean
Sampling Distribution
the population correlation
Observational study
13. Is data arising from counting that can take only non-negative integer values.
Count data
Simple random sample
An Elementary event
the population cumulants
14. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.
Type 1 Error
Valid measure
Statistics
Credence
15. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
The median value
Greek letters
Correlation coefficient
Standard error
16. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
An event
Descriptive
Sample space
Particular realizations of a random variable
17. E[X] :
Pairwise independence
experimental studies and observational studies.
An estimate of a parameter
expected value of X
18. Is a parameter that indexes a family of probability distributions.
Marginal probability
A Random vector
A Statistical parameter
Probability
19. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Type I errors
Type 2 Error
s-algebras
A sampling distribution
20. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then
A sample
A likelihood function
Observational study
A population or statistical population
21. The probability of correctly detecting a false null hypothesis.
Power of a test
hypothesis
f(z) - and its cdf by F(z).
Marginal probability
22. A measure that is relevant or appropriate as a representation of that property.
Type 1 Error
Valid measure
categorical variables
An event
23. 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.
Inferential
Inferential statistics
Type I errors
Seasonal effect
24. Gives the probability of events in a probability space.
Experimental and observational studies
A sampling distribution
A Probability measure
Lurking variable
25. 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
Joint probability
Independence or Statistical independence
Type II errors
26. 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
Joint probability
Ordinal measurements
experimental studies and observational studies.
That is the median value
27. 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
Bias
The average - or arithmetic mean
Probability density
Pairwise independence
28. Are usually written in upper case roman letters: X - Y - etc.
Atomic event
Type II errors
quantitative variables
Random variables
29. S^2
Parameter
Confounded variables
the population variance
Interval measurements
30. The proportion of the explained variation by a linear regression model in the total variation.
Power of a test
Standard error
Individual
Coefficient of determination
31. Is the length of the smallest interval which contains all the data.
The Range
That is the median value
Bias
Probability
32. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co
Beta value
Conditional distribution
Correlation coefficient
Step 1 of a statistical experiment
33. Of a group of numbers is the center point of all those number values.
Cumulative distribution functions
Block
Pairwise independence
The average - or arithmetic mean
34. A list of individuals from which the sample is actually selected.
Bias
Sampling frame
A data set
The variance of a random variable
35. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.
The Mean of a random variable
Law of Large Numbers
Residuals
Lurking variable
36. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
The standard deviation
Marginal probability
categorical variables
Placebo effect
37. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Sampling Distribution
Descriptive
Simple random sample
Alpha value (Level of Significance)
38. 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.
A data point
Cumulative distribution functions
Correlation coefficient
Ordinal measurements
39. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Mutual independence
Statistical inference
Inferential
Likert scale
40. Rejecting a true null hypothesis.
Simple random sample
Type 1 Error
Inferential
Dependent Selection
41. Is denoted by - pronounced 'x bar'.
Bias
The Range
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
A Distribution function
42. 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}.
An event
The sample space
s-algebras
covariance of X and Y
43. Var[X] :
variance of X
Observational study
Individual
the population mean
44. Is data that can take only two values - usually represented by 0 and 1.
Binary data
Prior probability
Power of a test
the sample or population mean
45. A numerical measure that describes an aspect of a population.
Law of Parsimony
A probability space
Parameter
Statistical dispersion
46. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
A Probability measure
Type I errors & Type II errors
descriptive statistics
47. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
A probability density function
Interval measurements
Inferential
Residuals
48. 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
Law of Large Numbers
Credence
Block
Probability and statistics
49. 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
Block
Ratio measurements
Residuals
the population variance
50. Where the null hypothesis is falsely rejected giving a 'false positive'.
A Distribution function
Sampling
Type I errors
The Expected value
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