# Stat & Prob ch 2

 Data Range The difference between the maximum and minimum member of a data set. Class Width The difference between the lower (or upper) limits of two adjacent classes. Class boundary The midpoint of the interval between the upper limit of a class and the lower limit of the next class Class midpoint The midpoint of the interval between the lower and upper limits of a class. Class frequency The number of scores that fall in a class. Relative frequency The number of scores that fall in a class Cumulative frequency The sum of frequencies for a specific class and all classes below it. Frequency histogram A bar diagram where each bar corresponds to one of the classes, and its height is equal to class' frequency (to some scale). Ogive a Graph of cumulative frequency distribution Pie chart A circular diagram depicting the distribution of qualitative data. Center A representative or average value that indicates where the middle of the data set is located. Variation A measure of the amount that the data values vary. Distribution The nature or shape of the spread of the data over the range of values (such as bell-shapred, uniform, or skewed). Outliers Sample values that lie very far away from the vast majority of the other sample values. Time Changing characteristics of the data over time CVDOT Computer Virus Destroy Or Terminate Center, Variation, Distribution, Outliers, Time Class width= (maximum data value) - (Minimum data value) Number of classes The number of classes should be between 5 & 20 Relative frequency= class frequencysum of all frequencies Percentage frequency = Class frequency sum of all frequency x 100% Mean The average value of all members of a data set Sample mean: Population Mean Median The middle value of a data set that is arranged in ascending or descending order. Mode The value most frequently occurring in a data set. Midrange The half-sum of the minimum and maximum value of a data set. Weighted Mean The mean of a data set whose members have a different significance (weight) w: Skewness The measure of a distribution’s asymmetry. (A left-skewed distribution has a peak shifted to the right, and vice versa.) Standard deviation A measure of variation taking into account each member of a data set. Population standard deviation Sample standard deviation Coefficient of variation (CV) A relative measure of variation. z- score (standardized value) A measure of relative standing, showing how many standard deviations the given value is below orabove the mean. Unusually small or unusually large values Any values that are more than two standard deviations below or above the mean. Quartiles Characteristic values dividing a data set (arranged in ascending/descending order) in quarters. Interquartile range (IQR) The difference between the third and the first quartile. Outlier An extremely small or extremely large value in a data set. Sigma demotes the sum of a set of data values x is the variable usually used to represent the individual data values. n represents the number of data values in a sample N represents the number of data values in a populations. Probability experiment An action involving uncertainty and consisting of a number of trials for which an outcome (measurement, response etc.) is obtained. Sample space The set of all possible outcomes of an experiment. Event A collection of outcomes. Simple event An event that includes just one outcome. Compound event An event that includes two or more outcomes. Probability Rule 1 (Relative Frequency Approximation) If the experiment was performed m times and event A occurred f times, then the probability of event A is Probability Rule 2 (Classical Approach) If the number of simple events (sample space size) is n and the number of ways the event A can occur is s, thenthe probability of event A is Subjective probability A probability that is based on intuitive feeling rather than on logical reasoning. Law of Large Numbers The larger is the number of trials, the closer is the probability by Rule 1 to the actual probability. Range of probability The probability of an event may be a number in the interval from 0 to 1 inclusive. The probability of an impossible event is 0. The probability of an event that is certain to occur is 1. Complementary events Two events such that one or the other must occur, but not both at the same time. Formal Addition Rule The probability that either event A occurs, or event B occurs, or both occur at the same time is P(A or B) = P(A) + P(B) – P(A and B) where P(A and B) is the probability that both A andB occur at the same time. Mutually exclusive (disjoint) events Two events that cannot occur at the same time. Addition Rule for mutually exclusive events P(A or B) = P(A) + P(B) Rule of Complementary Events If two events A and are complementary, then Conditional probability (probability of B given A) The probability of event B occurring under conditionthat event A has occurred. Independent events Two event (A and B) such that the probability of B does not depend on whether A occurred or not. Formal Multiplication Rule The probability of events A and B occurring consequently isP(A and B) = P(A)×P(B½A) Multiplication Rule for independent events P(A and B) = P(A)×P(B) Rule of At Least One P (at least 1) = 1 – P(0) Fundamental Counting Rule If a procedure consists of two events, such that one can occur in m ways and the other in n ways,the whole procedure can occur in m·n ways. The same principal is applicable to a procedure consisting of more than two events. Factorial function The product = n! is called n factorial. This definition applies to n> 1. 0! = 1. Permutation Rule (when all items are different) The number of ways r items can be selected from a set of n items (r < n) and arranged in all possible orders is Another form of this formula: Factorial Rule n different items can be arranged in n! ways. Combination Rule The number of ways r items can be selectedfrom a set of n items (r < n) is Authoraaron.simmons ID63542 Card SetStat & Prob ch 2 DescriptionStatistics and Probability chapter 2 lecture Updated2011-02-09T03:30:48Z Show Answers