Probability and Statistics for Engineers

Slides:



Advertisements
Similar presentations
Describing Quantitative Variables
Advertisements

Unit 1.1 Investigating Data 1. Frequency and Histograms CCSS: S.ID.1 Represent data with plots on the real number line (dot plots, histograms, and box.
Random Sampling and Data Description
QM Spring 2002 Statistics for Decision Making Descriptive Statistics.
1 Chapter 4: Variability. 2 Variability The goal for variability is to obtain a measure of how spread out the scores are in a distribution. A measure.
Basic Descriptive Statistics Healey, Chapter 2
© 2006 Baylor University EGR 1301 Slide 1 Lecture 18 Statistics Approximate Running Time - 30 minutes Distance Learning / Online Instructional Presentation.
6.1 What is Statistics? Definition: Statistics – science of collecting, analyzing, and interpreting data in such a way that the conclusions can be objectively.
2011 Summer ERIE/REU Program Descriptive Statistics Igor Jankovic Department of Civil, Structural, and Environmental Engineering University at Buffalo,
Topic 1: Descriptive Statistics CEE 11 Spring 2001 Dr. Amelia Regan These notes draw liberally from the class text, Probability and Statistics for Engineering.
Variability The goal for variability is to obtain a measure of how spread out the scores are in a distribution. A measure of variability usually accompanies.
ETM U 1 Statistical inference “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” (H.G.
JMB Chapter 1EGR Spring 2010 Slide 1 Probability and Statistics for Engineers  Descriptive Statistics  Measures of Central Tendency  Measures.
Chapter 6: Random Errors in Chemical Analysis CHE 321: Quantitative Chemical Analysis Dr. Jerome Williams, Ph.D. Saint Leo University.
The Central Tendency is the center of the distribution of a data set. You can think of this value as where the middle of a distribution lies. Measure.
Dr. Serhat Eren 1 CHAPTER 6 NUMERICAL DESCRIPTORS OF DATA.
MDM4U Chapter 3 Review Normal Distribution Mr. Lieff.
Determination of Sample Size: A Review of Statistical Theory
A Short Tour of Probability & Statistics Presented by: Nick Bennett, Grass Roots Consulting & GUTS Josh Thorp, Stigmergic Consulting & GUTS Irene Lee,
Essential Statistics Chapter 11 Picturing Distributions with Graphs.
Numerical descriptors BPS chapter 2 © 2006 W.H. Freeman and Company.
CHAPTER 1 Picturing Distributions with Graphs BPS - 5TH ED. CHAPTER 1 1.
EGR Statistical Inference “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” (H.G.
MDH Chapter 1EGR 252 Fall 2015 Slide 1 Probability and Statistics for Engineers  Descriptive Statistics  Measures of Central Tendency  Measures of Variability.
Chapter 3 Review MDM 4U Mr. Lieff. 3.1 Graphical Displays be able to effectively use a histogram name and be able to interpret the various types of distributions.
Statistics Descriptive Statistics. Statistics Introduction Descriptive Statistics Collections, organizations, summary and presentation of data Inferential.
Descriptive Statistics
Methods for Describing Sets of Data
Math 201: Chapter 2 Sections 3,4,5,6,7,9.
STATISTICS AND PROBABILITY IN CIVIL ENGINEERING
ISE 261 PROBABILISTIC SYSTEMS
Data Mining: Concepts and Techniques
BUSINESS MATHEMATICS & STATISTICS.
Chapter 2: Methods for Describing Data Sets
Chapter 6 – Descriptive Statistics
Probability and Statistics for Engineers
Central Tendency and Variability
Probability and Statistics for Engineers
Topic 5: Exploring Quantitative data
Displaying Distributions with Graphs
Displaying and Summarizing Quantitative Data
Probability and Statistics for Engineers
2-1 Data Summary and Display 2-1 Data Summary and Display.
Probability and Statistics for Engineers
Chapter 1: Exploring Data
Probability and Statistics for Engineers
Chapter 1: Exploring Data
Basic Practice of Statistics - 3rd Edition
Honors Statistics Chapter 4 Part 3
Chapter 1: Exploring Data
Basic Practice of Statistics - 3rd Edition
Warm Up # 3: Answer each question to the best of your knowledge.
Good morning! Please get out your homework for a check.
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Review for Exam 1 Ch 1-5 Ch 1-3 Descriptive Statistics
Probability and Statistics for Engineers
Chapter 1: Exploring Data
CHAPTER 2: Basic Summary Statistics
Chapter 1: Exploring Data
Probability and Statistics
Chapter 1: Exploring Data
DESIGN OF EXPERIMENT (DOE)
Chapter 1: Exploring Data
Probability and Statistics for Engineers
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Chapter 1: Exploring Data
Descriptive Statistics Civil and Environmental Engineering Dept.
Presentation transcript:

Probability and Statistics for Engineers Descriptive Statistics Measures of Central Tendency Measures of Variability Probability Distributions Discrete Continuous Statistical Inference Design of Experiments Regression JMB Chapter 1 EGR 252.001 Spring 2008

Descriptive Statistics Numerical values that help to characterize the nature of data for the experimenter. Example: The absolute error in the readings from a radar navigation system was measured with the following results: the sample mean, x = ? 17 22 39 31 28 52 147 (17+22+39+31+28+52+147)/7 = 48 order: 17 22 28 31 39 52 147 ↑ median = x (n+1)/2 , n odd median = (xn/2 + xn/2+1)/2 JMB Chapter 1 EGR 252.001 Spring 2008

Calculation of Mean Example: The absolute error in the readings from a radar navigation system was measured with the following results: _ the sample mean, X = (17+ 22+ 39 + 31+ 28 + 52 + 147) / 7 = 48 17 22 39 31 28 52 147 (17+22+39+31+28+52+147)/7 = 48 order: 17 22 28 31 39 52 147 ↑ median = x (n+1)/2 , n odd median = (xn/2 + xn/2+1)/2 JMB Chapter 1 EGR 252.001 Spring 2008

Calculation of Median Example: The absolute error in the readings from a radar navigation system was measured with the following results: the sample median, x = ? Arrange in increasing order: 17 22 28 31 39 52 147 n odd median = x (n+1)/2 , → 31 n even median = (xn/2 + xn/2+1)/2 17 22 39 31 28 52 147 ~ (17+22+39+31+28+52+147)/7 = 48 order: 17 22 28 31 39 52 147 ↑ median = x (n+1)/2 , n odd median = (xn/2 + xn/2+1)/2 JMB Chapter 1 EGR 252.001 Spring 2008

Descriptive Statistics: Variability A measure of variability (Recall) Example: The absolute error in the readings from a radar navigation system was measured with the following results: sample range: Max - Min 17 22 39 31 28 52 147 range = max – min (useful measure, but very susceptible to extreme values and doesn’t say much about what happens in between) = 147 – 17 = 130 variance – measures the spread of the data around the mean (more on next page …) JMB Chapter 1 EGR 252.001 Spring 2008

Calculations: Variability of the Data sample variance, sample standard deviation, 17 22 39 31 28 52 147 mean 48 median 31 variance 2037.3 std dev 45.137 JMB Chapter 1 EGR 252.001 Spring 2008

Other Descriptors Discrete vs Continuous Distribution of the data discrete: countable continuous: measurable Distribution of the data “What does it look like?” JMB Chapter 1 EGR 252.001 Spring 2008

Graphical Methods Dot diagram Stem and leaf plot See example in text example (radar data) Stem Leaf Frequency 1 7 1 2 2 8 2 3 1 9 2 4 5 2 1 6 7 8 9 10 11 12 13 14 7 1 Stem Leaf Frequency 1 7 1 2 2 8 2 3 1 9 2 4 5 2 1 6 7 8 9 10 11 12 13 14 7 1 JMB Chapter 1 EGR 252.001 Spring 2008

Graphical Methods (cont.) Frequency Distribution (histogram) Develop equal-size class intervals – “bins” ‘Rules of thumb’ for number of intervals 7-15 intervals per data set Square root of n Interval width = range / # of intervals Build table Identify interval or bin starting at low point Determine frequency of occurrence in each bin Calculate relative frequency Build graph Plot frequency vs interval midpoint JMB Chapter 1 EGR 252.001 Spring 2008

Data for Histogram Example: stride lengths (in inches) of 25 male students were determined, with the following results: What can we learn about the distribution of stride lengths for this sample? Stride Length 28.60 26.50 30.00 27.10 27.80 26.10 29.70 27.30 28.50 29.30 26.80 27.00 26.60 29.50 28.00 29.00 25.70 28.80 31.40 JMB Chapter 1 EGR 252.001 Spring 2008

Constructing a Histogram Determining frequencies and relative frequencies Lower Upper Midpoint Frequency Relative Frequency 24.85 26.20 25.525 2 0.08 27.55 26.875 10 0.40 28.90 28.225 7 0.28 30.25 29.575 5 0.20 31.60 30.925 1 0.04 Class intervals – divide max-min by 5 (sqrt of 25), then either add that number to successive intervals OR let Excel find the bins (caution – Excel bins are determined differently … you may want to play a little to get a good picture of the data…) JMB Chapter 1 EGR 252.001 Spring 2008

Histograms JMB Chapter 1 EGR 252.001 Spring 2008

Relative Frequency Graph JMB Chapter 1 EGR 252.001 Spring 2008