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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 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).
An event
Statistical inference
Count data
Probability
2. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Independence or Statistical independence
A Random vector
A probability space
Coefficient of determination
3. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'
Conditional probability
Qualitative variable
Variable
A Random vector
4. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Law of Parsimony
Inferential
Dependent Selection
Ordinal measurements
5. 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
Interval measurements
the population mean
Placebo effect
Descriptive statistics
6. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.
A data set
Step 1 of a statistical experiment
A random variable
Estimator
7. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
Type I errors & Type II errors
A probability space
Nominal measurements
8. ?r
Type I errors
A Distribution function
P-value
the population cumulants
9. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Statistical adjustment
quantitative variables
Variability
Parameter - or 'statistical parameter'
10. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Ratio measurements
nominal - ordinal - interval - and ratio
Pairwise independence
Statistical dispersion
11. Long-term upward or downward movement over time.
Observational study
Parameter - or 'statistical parameter'
Trend
A probability density function
12. A numerical facsimilie or representation of a real-world phenomenon.
Divide the sum by the number of values.
Pairwise independence
The standard deviation
Simulation
13. 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.
categorical variables
A Random vector
Random variables
Seasonal effect
14. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
methods of least squares
Observational study
the sample or population mean
Residuals
15. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
Sampling Distribution
P-value
An estimate of a parameter
Descriptive statistics
16. 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.
Quantitative variable
Lurking variable
Conditional probability
An experimental study
17. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Placebo effect
Step 2 of a statistical experiment
Statistical dispersion
quantitative variables
18. Is its expected value. The mean (or sample mean of a data set is just the average value.
The Mean of a random variable
Statistical adjustment
Descriptive
Nominal measurements
19. E[X] :
Confounded variables
expected value of X
A Distribution function
A data set
20. The probability of correctly detecting a false null hypothesis.
Count data
Correlation
Trend
Power of a test
21. 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.
The standard deviation
Mutual independence
A random variable
Skewness
22. Data are gathered and correlations between predictors and response are investigated.
Parameter
Valid measure
observational study
Residuals
23. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Sampling Distribution
Bias
Treatment
Type II errors
24. 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.
Residuals
quantitative variables
Sampling
Probability density functions
25. 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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26. S^2
Statistical inference
the population variance
the population correlation
Greek letters
27. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Joint probability
the population mean
Divide the sum by the number of values.
Greek letters
28. Of a group of numbers is the center point of all those number values.
An experimental study
The average - or arithmetic mean
the population cumulants
Placebo effect
29. Are usually written in upper case roman letters: X - Y - etc.
Variability
Random variables
The standard deviation
Parameter
30. When you have two or more competing models - choose the simpler of the two models.
Count data
the population mean
Probability density functions
Law of Parsimony
31. 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.
Ratio measurements
Nominal measurements
Treatment
Statistics
32. Failing to reject a false null hypothesis.
inferential statistics
Treatment
Marginal distribution
Type 2 Error
33. 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)
Probability density
Mutual independence
A Probability measure
Interval measurements
34. A subjective estimate of probability.
Step 3 of a statistical experiment
Credence
A probability space
Placebo effect
35. Probability of rejecting a true null hypothesis.
Experimental and observational studies
variance of X
A probability density function
Alpha value (Level of Significance)
36. Is a sample and the associated data points.
Confounded variables
quantitative variables
Statistic
A data set
37. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
expected value of X
Probability density
f(z) - and its cdf by F(z).
applied statistics
38. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Variability
Kurtosis
A sampling distribution
Sample space
39. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re
the population cumulants
The Expected value
An experimental study
Type 2 Error
40. Where the null hypothesis is falsely rejected giving a 'false positive'.
Joint distribution
Residuals
Type I errors
applied statistics
41. Is a sample space over which a probability measure has been defined.
the sample or population mean
A probability space
The Mean of a random variable
P-value
42. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
s-algebras
Ratio measurements
A random variable
Residuals
43. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
A Probability measure
Probability and statistics
Law of Large Numbers
44. A group of individuals sharing some common features that might affect the treatment.
A probability space
A Random vector
Block
Type 1 Error
45. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Descriptive
Prior probability
Independent Selection
46. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
The standard deviation
Sampling Distribution
f(z) - and its cdf by F(z).
Likert scale
47. 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.
Sampling frame
A population or statistical population
A Probability measure
hypothesis
48. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
quantitative variables
Conditional distribution
The median value
Law of Large Numbers
49. In particular - the pdf of the standard normal distribution is denoted by
Mutual independence
the population correlation
Statistical dispersion
f(z) - and its cdf by F(z).
50. 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.
Statistical inference
Step 2 of a statistical experiment
Statistical adjustment
Dependent Selection