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week 2 notes_scm

by: Evangelos Katradis

week 2 notes_scm 200

Evangelos Katradis
Penn State

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week 2 notes
Supply Chain Management (business statistics)
Marilyn Blanco
Class Notes
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This 3 page Class Notes was uploaded by Evangelos Katradis on Sunday February 7, 2016. The Class Notes belongs to 200 at Pennsylvania State University taught by Marilyn Blanco in Fall. Since its upload, it has received 20 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
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 values­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­F 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 x x­x bar abs.value (x­xbar) 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


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