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# studyguide 1 200

Penn State

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This 0 page Study Guide was uploaded by Evangelos Katradis on Sunday February 7, 2016. The Study Guide belongs to 200 at Pennsylvania State University taught by Marilyn Blanco in Fall. Since its upload, it has received 69 views. For similar materials see Supply Chain Management (business statistics) in Business at Pennsylvania State University.

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Date Created: 02/07/16

SCM 200 Study Guide Tooic 1 Intro to Statistics Statistics 1 Descriptive summarize the values of a data set by using charts graphs averages amp tables 2 Inferential make generalizations based on the descriptive statistics of our sample data Types of data 1 Qualitative categories nominal data units are placed in classes and have no natural ordering ex PSU ID car color ordinal data units are ranked into some natural order absolute differences are not meaningful ex hotel ratings student class standing 2 Quantitative numerical measurements interva scale no defined zero value ratio is not meaningful ex clothing sizes temperature ratio scale defined zero value ratio is meaningful ex prices distances TF a descriptive measure of a sample is a parameter F TF we should determine our objectives of a study before we collect the data T TF the numbers on a basketball jersey are an example of qualitative data F Tooic 2 Visualizino Data with one Variable Bar Chart vs Histogram Frequency vs Relative Frequency vs Cumulative Relative frequency yaxis s relative freq yaxis cumulative relative always going up skewed left neg skewed right pos MC a cumulative relative frequency totals to a0 b100 c n total sampleB TF a data set that is skewed left has most of the data values on the left with few data values trailing off to the right F Stem and Leaf keeps original data values Q a stem and leaf plot is useful because it shows distribution of data AND contains all the original data values Tooic 3 Distribution Parameters and Statistics Boxplots a pictoral display that indicates the range median and IQR data valuesleft to right smallest value Q1 medianQ2 Q3 largest value TF the first quartilevalueQ1 of a distribution can never be less than zero F TF a boxplot is a good way to show the mean of a data set F TF generally speaking a stem and leaf plot can t be constructed from a boxplot but a boxplot can be constructed from a stem and leaf plot T Descriptive Measures 1 absolute measure has data units 2 relative measure has no data units independent of data units Summary of Characteristics Characteristic arithmetic mean median mode always exsist yes yes no affected by extreme yes no no value uses all the data values yes no no Symmetry unimodal mean median mode all in center of curve bimodal mode mean amp median center mode no mode mean amp median center TF the median and mode are not affected by outlier values F TF every data set has a mode F MC scm 200 20 students 25 students 55 students a weighted average of the of students in these sec onsis 20100 2 25100 25 55100 55 5555 2525 202 405 Measures of Variability range vs lQR both measure distance bw 2 observation they are a single MC Which of the following measures compute the average distance from the mean a mean absolute dev b variance c IQR d mean absolute deviation AND variance DF TF median is a measure of variability of a data set F TF if we want the average of all the deviations from the mean of a data set we can simply add the deviations and divide by n F we would get 0 total Mean absolute deviation example 6141414 x xx bar absvalue xxbar 6 6 6 14 2 2 14 2 2 14 2 2 N population n1 sample st dev can never be less than MAD variability absolute value xxbar variation square xxbar parameter stat mean mu x bar standard sigma s dev TF a standard deviation can sometimes be larger in numerical value than a variance T Coefficient of variationCV allows us to measure the risk when the mean is not the same CV st dev mean X 100 Measures of Variation Absolute vs Relative 1 Absolute standard dev original data unites 2 Relative coefficient of variation independent of data units The Empirical Rule 6895997 Zscore have to have bell curve x meanst dev z MC 95 of data values in a bell shaped distribution lie within st dev from the mean 0 1 2 or 3 2 TF student score 70 class mean 80 st dev 5 z score 2 F Tooic 4 Probabilitv Probability vs Statistical Inference 1 Probability general gt specific 2 Statistical Inference specific general Mutually Exclusive Events vs Complementary events if two events are mutually exclusive if the occurrence of one event excludes the occurrence of the other both events cannot occur at the same time the complement of an event is 2nd event made up of all supplements not in that first event these 2 events make up the entire sample space 17 F1 drawing an ace Of Spades and a 3 of hearts are complementary events TF the items in a sample space must be exhaustive T AssigningDetermining Probabilities 1 Theoretical Approach theoretical probability of possible way of obtaining the event total of equally likely possible outcomes 2 Relative Frequency of times an event occurs of replication 3 Subjective Judgement Conditions cannot be replicated probability represents an individuals judgement DOORS What should you do to give you the greatest chance to receive the new car make the switch lose if you switch 13 win if you switch 23 TF When one throws a die 1000 times and determines that the probability of obtaining a 6 on a die is 16 that person has used the theoretical approach to probability F Tooic 5 Tvoes of Probabilitv Distributions Requirements for a Discrete Probability Distribution 1 the probability of each event or combo of events must range from 0 to 1 2 sum of the probabilities of all possible events must TF the distribution of peoples heights is an example of a discrete probability distribution F TF the sum of the probabilities in a discrete probability distribution could total 12 F Tooic 6 Random Variables and Random Sampling Random variable 1 numerical value 2 probabilities associated w those values probability doesn t need to be equal Eventsgt Random Variablesgt Probability Distributionsgt Inference and Decision Making TF Probabilities associated with random variables must all be equal F expected value not what I expect to happen next time All terms population entire group of people or objects to be studied process activities that are performed over and over to transform inputs into outcomes sample subset of a population or process parameter value that summarizes a characteristic of a population or processdescriptive measure of a population ex avg SAT score statistic value that summarizes a characteristic of a sampledesriptive measure of a sample sampling error difference between the result of a sample and the corresponding result of a census statistical inference using sample information to learn about a pop or a process statistical variable single characteristic of any object or event distribution the way that observations are spread out across a range of values frequency table a table that tabulates the of times a variable occurs bar chart a graph of qualitative data in which the classes are on the horizontal axis amp the frequencies are on the vertical axis the height of the bar is proportional to the freq of the class bars DON T touch bin frequency table group that covers a particular range of values histogram bar graph of guantitative data in which each bar represents a bin and the height of the bar is proportional to the of data values in the bin bars DO touch percentiles values in a data set values must be in order from minmax that divide the data set into 100 equal parts quartiles values located at the 25th 50th 75th percentiles 010203 interquartile range IQR 0201 gt difference bw the first and third quartilescenter 50 of data range the distance bw the largest and smallest values within a data set skewness measure of the lack of symmetry in the distribution of data values skewness of zero symmetric distribution pos distribution that is skewed rightpos neg distribution that is skewed left neg simple events the most basic possible outcomes of an experiment that can t be broken down anyfunher sample space collection of all possible simple events event subset of a sample space discrete consists of whole s or values that have distance bw them and are countable doesn t mean finite can have infinite of possible outcomes not within given range the probability is the height of the bar when the distribution is in the graph continuous theoretically an infinite of outcomes within a given range probabilities are assigned to a range of continuous values rather than to distinct individual values probability of any specific value is 0 total area under curve 1 probabilities are calculated using probability density functiongtPDF probability associated w range of values is to the area under the curve

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