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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. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
quantitative variables
Conditional distribution
Particular realizations of a random variable
Statistics
2. 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
An Elementary event
Statistic
Parameter - or 'statistical parameter'
3. 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.
The median value
Alpha value (Level of Significance)
Null hypothesis
A random variable
4. 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)
Conditional distribution
Interval measurements
Quantitative variable
An event
5. Var[X] :
the sample or population mean
Correlation coefficient
Ratio measurements
variance of X
6. 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.
Greek letters
Experimental and observational studies
Particular realizations of a random variable
Mutual independence
7. 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
A sample
The standard deviation
Step 3 of a statistical experiment
Skewness
8. 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.
categorical variables
A population or statistical population
Posterior probability
Observational study
9. 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.
Observational study
Lurking variable
P-value
An experimental study
10. Gives the probability distribution for a continuous random variable.
A probability density function
nominal - ordinal - interval - and ratio
Ordinal measurements
Observational study
11. 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
A probability density function
Step 2 of a statistical experiment
Correlation
Standard error
12. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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13. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
A Random vector
Simple random sample
Lurking variable
Particular realizations of a random variable
14. In particular - the pdf of the standard normal distribution is denoted by
Count data
Conditional probability
f(z) - and its cdf by F(z).
Type I errors
15. 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.
hypothesis
Ordinal measurements
Divide the sum by the number of values.
Marginal distribution
16. A group of individuals sharing some common features that might affect the treatment.
Beta value
Marginal distribution
Block
That value is the median value
17. Is data arising from counting that can take only non-negative integer values.
Count data
Random variables
nominal - ordinal - interval - and ratio
Nominal measurements
18. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Inferential statistics
An estimate of a parameter
Beta value
A Random vector
19. A numerical measure that describes an aspect of a population.
Step 1 of a statistical experiment
A probability density function
An Elementary event
Parameter
20. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Joint distribution
Sampling frame
Joint probability
Greek letters
21. 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.
Probability
f(z) - and its cdf by F(z).
Statistic
Statistical inference
22. Probability of rejecting a true null hypothesis.
Step 3 of a statistical experiment
Alpha value (Level of Significance)
Outlier
observational study
23. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Variable
A data set
Likert scale
Inferential statistics
24. 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
Correlation coefficient
Mutual independence
Marginal probability
Descriptive statistics
25. Is a sample and the associated data points.
Statistic
Observational study
A data set
The Expected value
26. Is its expected value. The mean (or sample mean of a data set is just the average value.
observational study
Greek letters
The Mean of a random variable
inferential statistics
27. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
A data set
Variable
An experimental study
28. 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
Mutual independence
hypotheses
Random variables
The average - or arithmetic mean
29. Any specific experimental condition applied to the subjects
Treatment
quantitative variables
A Probability measure
Bias
30. 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
A probability distribution
Particular realizations of a random variable
Ratio measurements
Variable
31. Probability of accepting a false null hypothesis.
Beta value
Experimental and observational studies
Credence
Kurtosis
32. 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.
A probability distribution
Sampling frame
A Statistical parameter
A Distribution function
33. Another name for elementary event.
P-value
Particular realizations of a random variable
Atomic event
Sample space
34. Working from a null hypothesis two basic forms of error are recognized:
Variable
Type I errors & Type II errors
Individual
The standard deviation
35. Is that part of a population which is actually observed.
A sample
Type II errors
Sampling
Particular realizations of a random variable
36. Many statistical methods seek to minimize the mean-squared error - and these are called
Sampling Distribution
the population mean
methods of least squares
Type I errors & Type II errors
37. 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
inferential statistics
nominal - ordinal - interval - and ratio
Random variables
The Expected value
38. Are simply two different terms for the same thing. Add the given values
Law of Parsimony
Average and arithmetic mean
categorical variables
Placebo effect
39. The collection of all possible outcomes in an experiment.
Null hypothesis
descriptive statistics
Sample space
nominal - ordinal - interval - and ratio
40. 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.
Seasonal effect
Type I errors & Type II errors
Conditional probability
An estimate of a parameter
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.
Alpha value (Level of Significance)
The Mean of a random variable
An experimental study
Divide the sum by the number of values.
42. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Bias
Credence
nominal - ordinal - interval - and ratio
Power of a test
43. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
A Distribution function
Type II errors
Correlation coefficient
Posterior probability
44. ?r
the population cumulants
The Expected value
Beta value
the population variance
45. Is defined as the expected value of random variable (X -
Power of a test
The Range
The Covariance between two random variables X and Y - with expected values E(X) =
nominal - ordinal - interval - and ratio
46. 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
Statistics
Simple random sample
Count data
experimental studies and observational studies.
47. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
An Elementary event
Binomial experiment
Probability and statistics
Type I errors
48.
Trend
the population mean
Bias
hypotheses
49. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Estimator
Marginal probability
The variance of a random variable
A statistic
50. Is the length of the smallest interval which contains all the data.
The Range
A probability space
Independence or Statistical independence
the population mean