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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. 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.






2. Failing to reject a false null hypothesis.






3. ?






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






5. E[X] :






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






7. A group of individuals sharing some common features that might affect the treatment.






8. A measurement such that the random error is small






9. 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






10. (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.






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






12. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.






13. 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.






14. 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






15. 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






16. 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






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






18. (or multivariate random variable) is a vector whose components are random variables on the same probability space.






19. Probability of accepting a false null hypothesis.






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






21. (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






22. Is a parameter that indexes a family of probability distributions.






23. 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.






24. 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






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






26. Two variables such that their effects on the response variable cannot be distinguished from each other.






27. Some commonly used symbols for sample statistics






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






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






30. The proportion of the explained variation by a linear regression model in the total variation.






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






32. 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.






33. 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






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






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






36. Some commonly used symbols for population parameters






37. (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






38. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.






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






40. Long-term upward or downward movement over time.






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






42. 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






43. Rejecting a true null hypothesis.






44. 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}.






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






46. 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.


47. 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






48. 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.






49. Where the null hypothesis is falsely rejected giving a 'false positive'.






50. A numerical measure that assesses the strength of a linear relationship between two variables.