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by: Evangelos Katradis


Marketplace > Pennsylvania State University > Business > 200 > SCM FINAL STUDY GUIDE
Evangelos Katradis
Penn State

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final study guide for scm 200
Supply Chain Management (business statistics)
Marilyn Blanco
Study Guide
50 ?




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This 7 page Study Guide was uploaded by Evangelos Katradis on Sunday May 1, 2016. The Study Guide belongs to 200 at Pennsylvania State University taught by Marilyn Blanco in Fall. Since its upload, it has received 43 views. For similar materials see Supply Chain Management (business statistics) in Business at Pennsylvania State University.

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Date Created: 05/01/16
SCM 200 Final Study Guide Topic 1: Intro to Statistics Statistics 1. Descriptive: summarize the values of a data set by using charts, graphs, averages & 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 ­interval scale: no defined zero value; ratio is not meaningful ex. clothing sizes, temperature ­ratio scale: defined zero value; ratio is meaningful ex. prices, distances T/F­­ a descriptive measure of a sample is a parameter­­­­­­­­­­­­­­­­­­­­­­­F T/F­­ we should determine our objectives of a study before we collect the data­­­­­­­­­­­T T/F­­ the numbers on a basketball jersey are an example of qualitative data­­­­­­­­­­­­­­F Topic 2: Visualizing 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___: a)0%  b)100% c) n (total # sample)­­B T/F: a data set that is skewed left has most of the data values on the left with few data values  trailing off to the right­­­­­­­­­­­­­­­­­­­­F 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  Topic 3: Distribution Parameters and Statistics Boxplots: a pictoral display that indicates the range, median, and IQR data values(left to right): smallest value, Q1, median(Q2), Q3, largest value T/F: the first quartile(value)(Q1) of a distribution can never be less than zero­­­­­­­­­­F T/F: a boxplot is a good way to show the mean of a data set­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­F T/F: 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 plot­­­­­­­­T 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 valuesyes no no Symmetry:  unimodal­ mean, median, mode all in center of curve bimodal­ mode, mean & median center, mode no mode­ mean & median (center) T/F: the median and mode are not affected by outlier values­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­ F T/F: every data set has a mode­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­F MC: scm 200: 20 students, 25 students, 55 students. a weighted average of the # of students in  these sections is:  20/100= .2    25/100= .25   55/100= .55 ­­­­­­­­­  55(.55) + 25(.25) + 20(.2)= 40.5 Measures of Variability: range vs IQR­­ both measure distance b/w 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  variance­­­­­­­­­­DF T/F: median is a measure of variability of a data set­­­­­­­­­­­­­­­­­F T/F: 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 n­­­­­­­­­­­­­­­­F ( we would get 0 total) Mean absolute deviation example: 6,14,14,14 x x­x bar abs.value (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 T/F: a standard deviation can sometimes be larger in numerical value than a variance­­­­­­­­­­­­­T Coefficient of variation(CV): 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: 68%­95%­99.7% Z­score: have to have bell curve*  { (x­ mean)/st. 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 T/F: student score: 70  class mean: 80  st. dev.: 5    z score: 2­­­­­­­­­­­­­­­­­­­­­­­­­­­­F Topic 4: Probability Probability vs Statistical Inference: 1. Probability: general ­­­­> 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 T/F: drawing an ace of spades and a 3 of hearts are complementary events­­­­­­­­­­­­­­­F T/F: the items in a sample space must be exhaustive­­­­­­­­­­­­­­T Assigning/Determining 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: 1/3 win if you switch: 2/3 T/F: When one throws a die 1,000 times and determines that the probability of obtaining a 6 on a die is 1/6, that person has used the theoretical approach to probability­­­­­­­­­­F Topic 5: Types of Probability 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 = 1 T/F: the distribution of peoples heights is an example of a discrete probability distribution­­­­­­­F T/F: the sum of the probabilities in a discrete probability distribution could total 1.2.­­­­­­­­F Topic 6: Random Variables and Random Sampling Random variable? 1. numerical value 2. probabilities: associated w/ those values (probability doesn’t need to be equal) Events­­> Random Variables­­> Probability Distributions­­­> Inference and Decision Making T/F: Probabilities associated with random variables must all be equal­­­­­­­­­F **expected value: not what I expect to happen next time** The Expected Value concept: ­ the symbol “E” denotes the average value of  hence: mu= E(x)=mean of x Variance of a Discrete Random Variable: (when x is a random variable) ­(sigma)^2=Variance of X= Var(x) ­(sigma)^2=  sum of (x­mu)^2 X P(x) ­P(x)= Probability of X ­since mu= E(x)= sum of x X P(x) ­Var(x)= (sigma)^2= sum of [x­E(x)]^2 X P(x) Standard Deviation of a Discrete Random Variable: (the st. dev. of a random variable X is the pos. square root of the var of X) ­sigma= of X= SD(x) ­SD(x)= sigma= sq. root of {sum of ([x­E(x)]^2) P(x)} Sampling distribution: ­ probability distribution ­ shows all possible sample results for a given sampling situation Not:  ­ a population  ­ not a sample distribution Depends on:  1) sample size (n) 2) statistic being computed (mean, median, range, etc.) T/F A sampling distribution is a distribution of all possible values of a statistic for a given  sample size............T T/F All standard deviations are standard errors (S.E.) are standard deviations.............F T/F Given a population standard deviation, as sample size increases, standard error also  increases.........F Steps in Testing Hypotheses 1) formulate a null and alternative hypothesis 2) specify a level of significance (alpha) 3) Specify the appropriate Test Statistic 4) take random sample and calculate the value of the Test Statistic 5) compute the p­value and compare it with alpha 6) draw conclusion: accept or reject Ho P­value • it is a probability. it is like a weight of evidence with regard to the null hypothesis • it is computed from the sample data • probability of obtaining the observed value or values more extreme than the observed  value • large p­value supports the null • small p­value support the alternative  ­ P­value decision rule: reject Ho if the p­value is less than or equal to the level of significance T/F In order to compute the p­value from the sample data, we need to know both the alternative  hypothesis and the level of significance.......F p­value < alpha ­Reject p­value > alpha ­Accept scm week 10 notes linear regression­ the relationship between a scalar dependent variable (y) and aa explanatory  variable (x).  coefficient of determination (R­squared)­ key output of regression analysis ­ it is often used to predict, forecast or for error reduction ­ linear regression analysis can be applied to quantify the strength of the relationship ­ it is the best fitting line (straight) through the points ­ it allows us to summarize and study relationships between two continuous variables ­ most basic and commonly used predictive analysis ­ estimates are used to describe data and to explain the relationship 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 process­ descriptive 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 &  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 quantitative 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 min/max) that divide the data set into 100 equal parts • quartiles: values located at the 25th, 50th, 75th percentiles (Q1,Q2,Q3) • interquartile range (IQR): Q2­Q1­­­­> difference b/w the first and third  quartiles(center 50% of data) • range: the distance b/w 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 right(pos) ­neg # = distribution that is skewed left (neg) • simple events: the most basic possible outcomes of an experiment that can’t be  broken down any further • sample space: collection of all possible simple events • event: subset of a sample space discrete: consists of whole #s or values that have distance b/w 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 function­­­>PDF ­probability associated w/ range of values is = to the area under the curve


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