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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. Describes the spread in the values of the sample statistic when many samples are taken.
Probability density functions
Variability
The variance of a random variable
Treatment
2. Probability of accepting a false null hypothesis.
Beta value
methods of least squares
Inferential
Dependent Selection
3. 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 Distribution function
Alpha value (Level of Significance)
Treatment
The Covariance between two random variables X and Y - with expected values E(X) =
4. A variable describes an individual by placing the individual into a category or a group.
The variance of a random variable
experimental studies and observational studies.
observational study
Qualitative variable
5. Probability of rejecting a true null hypothesis.
The Covariance between two random variables X and Y - with expected values E(X) =
That value is the median value
Bias
Alpha value (Level of Significance)
6. 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
variance of X
Observational study
Placebo effect
Type 2 Error
7. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
A Distribution function
Sampling
Bias
Prior probability
8. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
observational study
Joint distribution
A statistic
Joint probability
9. Rejecting a true null hypothesis.
Type 1 Error
Valid measure
Alpha value (Level of Significance)
Trend
10. A subjective estimate of probability.
Standard error
Credence
Marginal distribution
An estimate of a parameter
11. Is a sample and the associated data points.
Step 2 of a statistical experiment
Probability density functions
Type 2 Error
A data set
12. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Variability
Residuals
Sampling Distribution
An experimental study
13. 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
applied statistics
experimental studies and observational studies.
covariance of X and Y
inferential statistics
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.
Atomic event
Simple random sample
Statistics
Bias
15. A numerical measure that describes an aspect of a population.
An event
Parameter
experimental studies and observational studies.
Sampling Distribution
16. 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.
Step 3 of a statistical experiment
A random variable
A population or statistical population
Sampling
17. A numerical facsimilie or representation of a real-world phenomenon.
Confounded variables
A Distribution function
Simulation
Ratio measurements
18. 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.
Type 1 Error
A data point
Dependent Selection
P-value
19. Some commonly used symbols for population parameters
hypothesis
the population mean
Valid measure
A probability distribution
20. Many statistical methods seek to minimize the mean-squared error - and these are called
The average - or arithmetic mean
Simpson's Paradox
Step 1 of a statistical experiment
methods of least squares
21. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a
Joint probability
descriptive statistics
Law of Large Numbers
Step 2 of a statistical experiment
22. Cov[X - Y] :
covariance of X and Y
Count data
Qualitative variable
A data set
23. 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
Correlation coefficient
Experimental and observational studies
A sample
Probability and statistics
24. Working from a null hypothesis two basic forms of error are recognized:
Lurking variable
Marginal probability
Greek letters
Type I errors & Type II errors
25. 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}.
the population mean
Pairwise independence
The sample space
Count data
26. Failing to reject a false null hypothesis.
The sample space
A data set
Type 2 Error
Cumulative distribution functions
27. 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.
Inferential statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Marginal distribution
Standard error
28. 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
Type 1 Error
The standard deviation
The Range
29. 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)
Interval measurements
The Range
Bias
Probability density
30. Is data that can take only two values - usually represented by 0 and 1.
An Elementary event
Sampling Distribution
Binary data
Block
31. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Random variables
Individual
The sample space
Inferential statistics
32. 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
Binomial experiment
Skewness
The Expected value
Statistics
33. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Block
Type II errors
Greek letters
Treatment
34. A measure that is relevant or appropriate as a representation of that property.
Valid measure
Binomial experiment
Sampling Distribution
hypothesis
35. 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
The Expected value
inferential statistics
The sample space
Sample space
36. The proportion of the explained variation by a linear regression model in the total variation.
Inferential statistics
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Statistical adjustment
Coefficient of determination
37. Is the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.
Sampling frame
Mutual independence
Marginal probability
nominal - ordinal - interval - and ratio
38. Describes a characteristic of an individual to be measured or observed.
Ratio measurements
An Elementary event
The median value
Variable
39. When there is an even number of values...
Coefficient of determination
Statistics
That is the median value
categorical variables
40. 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.
Count data
Variability
Sampling Distribution
Experimental and observational studies
41. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.
An Elementary event
A probability distribution
Quantitative variable
P-value
42. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Seasonal effect
An estimate of a parameter
Count data
A Random vector
43. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Skewness
Sampling Distribution
Alpha value (Level of Significance)
applied statistics
44. Is data arising from counting that can take only non-negative integer values.
The standard deviation
Count data
Individual
Atomic event
45. (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 likelihood function
A Distribution function
Step 1 of a statistical experiment
variance of X
46. Is that part of a population which is actually observed.
f(z) - and its cdf by F(z).
Quantitative variable
A sample
Bias
47. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
Independence or Statistical independence
Coefficient of determination
Step 1 of a statistical experiment
48. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
Nominal measurements
Prior probability
Step 1 of a statistical experiment
49. Two variables such that their effects on the response variable cannot be distinguished from each other.
Type 2 Error
Outlier
Confounded variables
Simple random sample
50. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Residuals
The sample space
An Elementary event
A statistic