Test your basic knowledge |

CLEP General Mathematics: Probability And Statistics

Subjects : clep, math
Instructions:
  • Answer 50 questions in 15 minutes.
  • If you are not ready to take this test, you can study here.
  • 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. The proportion of the explained variation by a linear regression model in the total variation.






2. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the






3. Is a sample and the associated data points.






4. A measure that is relevant or appropriate as a representation of that property.






5. 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






6. S^2






7. Some commonly used symbols for sample statistics






8. In particular - the pdf of the standard normal distribution is denoted by






9. 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.






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.






11. 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.






12. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.






13. 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






14. 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






15. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data






16. 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.






17. Data are gathered and correlations between predictors and response are investigated.






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.






19. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that






20. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a






21. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)






22. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.






23. Are simply two different terms for the same thing. Add the given values






24. 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






25. 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.






26. Working from a null hypothesis two basic forms of error are recognized:






27. Is data that can take only two values - usually represented by 0 and 1.






28. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.






29. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.

Warning: Invalid argument supplied for foreach() in /var/www/html/basicversity.com/show_quiz.php on line 183


30. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.






31. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as






32. Gives the probability of events in a probability space.






33. 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






34. 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






35. (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






36. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.






37. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.






38. The collection of all possible outcomes in an experiment.






39. Many statistical methods seek to minimize the mean-squared error - and these are called






40. 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.






41. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).






42. Describes a characteristic of an individual to be measured or observed.






43. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.






44. Error also refers to the extent to which individual observations in a sample differ from a central value - such as






45. 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.






46. 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






47. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.






48. Rejecting a true null hypothesis.






49. Failing to reject a false null hypothesis.






50. Is that part of a population which is actually observed.