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. (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.






3. 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.






4. 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.






5. A measurement such that the random error is small






6. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.






7. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.






8. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.






9. 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






10. Is its expected value. The mean (or sample mean of a data set is just the average value.






11. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.






12. 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.






13.






14. 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






15. Is a sample and the associated data points.






16. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)






17. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.






18. A numerical facsimilie or representation of a real-world phenomenon.






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






20. 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.

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


21. Gives the probability distribution for a continuous random variable.






22. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.






23. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).






24. A numerical measure that describes an aspect of a population.






25. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.






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






27. Is denoted by - pronounced 'x bar'.






28. Probability of accepting a false null hypothesis.






29. 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}.






30. Probability of rejecting a true null hypothesis.






31. 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.






32. (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






33. ?






34. 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






35. Failing to reject a false null hypothesis.






36. The standard deviation of a sampling distribution.






37. To find the average - or arithmetic mean - of a set of numbers:






38. 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.






39. 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






40. A numerical measure that describes an aspect of a sample.






41. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.






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






43. 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






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






45. 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






46. 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






47. 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






48. 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






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






50. 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