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Statistics Notes Week 3

by: Elizabeth Schnarr

Statistics Notes Week 3 Stat 190-01

Marketplace > Truman State University > Math > Stat 190-01 > Statistics Notes Week 3
Elizabeth Schnarr
Truman State

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About this Document

These notes cover the shapes of distribution, modes, and probability
Basic Statistics
Sunghoon Chung
Class Notes
Math, Stats, Statistics, distribution, Shapes, modes, personality
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This 5 page Class Notes was uploaded by Elizabeth Schnarr on Friday September 9, 2016. The Class Notes belongs to Stat 190-01 at Truman State University taught by Sunghoon Chung in Fall 2016. Since its upload, it has received 5 views. For similar materials see Basic Statistics in Math at Truman State University.


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Date Created: 09/09/16
Week 3: 9/5­9/11  Statistics  Chapter 2: Summarizing Data in Tables and Graphs  Section 2: Organize Discrete and Continuous Data      Shapes of distribution  ● When describing a distribution one must pay attention to its shape, center, and  spread                                    Uniform Distribution:         Bell­shaped Distribution      ● These two shapes are considered to be symmetrical            Week 3: 9/5­9/11  Skewed Right Distribution          Skewed Left Distribution      ● Wherever the tail (thinner part) is on the graph is the direction the graph is  skewed  ● Peaks in graphs are called modes  ● There are unimodals, bimodals, and multimodals                         Week 3: 9/5­9/11      Example of modes:    Unimodal       Bimodal      Multimodal                          Week 3: 9/5­9/11  Statistics  Chapter 5: Probability  Sections 1­4      Definitions  ● Experiment: an act or process of observation that leads to a single outcome that  cannot be predicted with certainty  ● Sample Space: (S), the collection of all possible outcomes  ● Event: (E) any collection of outcomes from a probability experiment  ● Simple event: (e) events with one outcome    Intro  ● Probability is the measure of the likelihood of a random phenomenon  (experiment) or chance behavior   ● Probability describes the long­term proportion with which a certain outcome will  occur in situations with short­term uncertainty  ● Deals with experiments that yield random short­term results or outcomes, yet  reveal long­term predictability    Event Example    Sample space: S= (HT), (HH), (TT), (TH)    Event  Outcomes in Event  1 head and 1 tail  (HT), (TH)  Head on 1st coin  (HT), (HH)  At least 1 head  (HT), (HH), (TH)  Heads on both  (HH)            Week 3: 9/5­9/11  Probability Model  Rules…  1. Probability Event, P(E), must be greater than or equal to 0 and less than or equal  to 1  2. Sum of the probabilities of all outcomes must equal 1    Example of a Probability Model…    Color  Probability  Brown  0.12  Yellow  0.15  Red  0.12  Blue  0.23  Orange   0.23  Green  0.15   


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