Dispersion: Range Difference between minimum and maximum values

Slides:

Advertisements

Similar presentations

Means and Variances of RV 7.2. Example # of BoysP(X) Find the expected value, or mean, of the probability distribution above:

Advertisements

Psy302 Quantitative Methods

Measures of Dispersion or Measures of Variability

1. 2 BIOSTATISTICS TOPIC 5.4 MEASURES OF DISPERSION.

BASIC FUNCTIONS OF EXCEL. Addition The formula for addition is: =SUM( insert cells desired to sum up ) This returns the sum of the selected cells.

Measures of Variability or Dispersion Range - high and lows. –Limitations: Based on only two extreme observations Interquartile range - measures variablility.

Variability 2011, 10, 4. Learning Topics  Variability of a distribution: The extent to which values vary –Range –Variance** –Standard Deviation**

Measures of Spread The Range, Variance, and Standard Deviation.

Variability Measures of spread of scores range: highest - lowest standard deviation: average difference from mean variance: average squared difference.

Biostatistics Unit 2 Descriptive Biostatistics 1.

Central Tendency & Variability Dec. 7. Central Tendency Summarizing the characteristics of data Provide common reference point for comparing two groups.

2.3. Measures of Dispersion (Variation):

As with averages, researchers need to transform data into a form conducive to interpretation, comparisons, and statistical analysis measures of dispersion.

Business Statistics BU305 Chapter 3 Descriptive Stats: Numerical Methods.

 Deviation is a measure of difference for interval and ratio variables between the observed value and the mean.  The sign of deviation (positive or.

(c) 2007 IUPUI SPEA K300 (4392) Outline Correlation and Covariance Bivariate Correlation Coefficient Types of Correlation Correlation Coefficient Formula.

2.4 PKBjB0 Why we need to learn something so we never sound like this.

Measures of dispersion Standard deviation (from the mean) ready.

CHAPTER 4 Measures of Dispersion. In This Presentation  Measures of dispersion.  You will learn Basic Concepts How to compute and interpret the Range.

(c) 2007 IUPUI SPEA K300 (4392) Outline: Numerical Methods Measures of Central Tendency Representative value Mean Median, mode, midrange Measures of Dispersion.

Chapter 3 Descriptive Measures

(c) 2007 IUPUI SPEA K300 (4392) Outline Normal Probability Distribution Standard Normal Probability Distribution Standardization (Z-score) Illustrations.

 The data set below gives the points per game averages for the 10 players who had the highest averages (minimum 70 games or 1400 points) during the

SECTION 12-3 Measures of Dispersion Slide

Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 3 Section 2 – Slide 1 of 27 Chapter 3 Section 2 Measures of Dispersion.

10b. Univariate Analysis Part 2 CSCI N207 Data Analysis Using Spreadsheet Lingma Acheson Department of Computer and Information Science,

Measures of Dispersion. Introduction Measures of central tendency are incomplete and need to be paired with measures of dispersion Measures of dispersion.

LECTURE CENTRAL TENDENCIES & DISPERSION POSTGRADUATE METHODOLOGY COURSE.

Measures of Dispersion MATH 102 Contemporary Math S. Rook.

3 common measures of dispersion or variability Range Range Variance Variance Standard Deviation Standard Deviation.

Chapter 3: Averages and Variation Section 2: Measures of Dispersion.

Review: Measures of Dispersion Objectives: Calculate and use measures of dispersion, such as range, mean deviation, variance, and standard deviation.

Unit 1 Review Standards 1-8. Standard 1: Describe subsets of real numbers.

Solve by factoring. x² = - 4 – 5x 2,. Solve by factoring. n² = -30 – 11n -4 and -1.

Objectives The student will be able to:

Thinking Mathematically Statistics: 12.3 Measures of Dispersion.

2.4 Measures of Variation Coach Bridges NOTES. What you should learn…. How to find the range of a data set How to find the range of a data set How to.

Measures of Dispersion Section 4.3. The case of Fred and Barney at the bowling alley Fred and Barney are at the bowling alley and they want to know who’s.

Session 8 MEASURES Of DISPERSION. Learning Objectives  Why do we study dispersion?  Measures of Dispersion Range Standard Deviation Variance.

(c) 2007 IUPUI SPEA K300 (4392) Outline Comparing Group Means Data arrangement Linear Models and Factor Analysis of Variance (ANOVA) Partitioning Variance.

Closing prices for two stocks were recorded on ten successive Fridays. Mean = 61.5 Median = 62 Mode = 67 Mean = 61.5 Median = 62 Mode =

Economics 111Lecture 7.2 Quantitative Analysis of Data.

Introduction Dispersion 1 Central Tendency alone does not explain the observations fully as it does reveal the degree of spread or variability of individual.

Chapter 2 The Mean, Variance, Standard Deviation, and Z Scores.

Mean and Standard Deviation

Discrete Probability Distributions

Describing Distributions of Quantitative Data

Copyright 2015 Davitily.

Identify the random variable of interest

Measures of Dispersion

Descriptive Measures Descriptive Measure – A Unique Measure of a Data Set Central Tendency of Data Mean Median Mode 2) Dispersion or Spread of Data A.

Data Mining: Concepts and Techniques

Measures of Central Tendency

Warm Up What is the mean, median, mode and outlier of the following data: 16, 19, 21, 18, 18, 54, 20, 22, 23, 17 Mean: 22.8 Median: 19.5 Mode: 18 Outlier:

Statistics 4/26 Objective: Students will be able to find measures of statistical dispersion. Standards: 1.02 Summarize and analyze univariate data to solve.

Standard Deviation Example

3-2 Measures of Variance.

BUS7010 Quant Prep Statistics in Business and Economics

Construction Engineering 221

10.2 Variance Math Models.

Coding in the form y = a + bx

Measures of Dispersion (Spread)

Notes – Standard Deviation, Variance, and MAD

Section 2.4 Measures of Variation.

Standard Deviation How many Pets?.

Chapter 12 Statistics.

2.3. Measures of Dispersion (Variation):

Lecture 4 Psyc 300A.

Presentation transcript:

Dispersion: Range Difference between minimum and maximum values (c) 2007 IUPUI SPEA K300 (4392)

Dispersion: Mean Deviation Mean difference from the mean Easy to understand Difficult to handle in modeling (c) 2007 IUPUI SPEA K300 (4392)

Dispersion: Variance Average of the squares of difference from the mean Second moment Giving penalty for large deviations (c) 2007 IUPUI SPEA K300 (4392)

Dispersion: Variance Short-cut formulae (c) 2007 IUPUI SPEA K300 (4392)

Dispersion: Standard Deviation Square root of variance Compare to absolute deviation (c) 2007 IUPUI SPEA K300 (4392)

Dispersion: 3-21on p. 124 Y Y-µ (Y-µ)^2 Y^2 10 -25 625 100 60 25 3600 50 15 225 2500 30 -5 900 40 5 1600 20 -15 400 210 1750 9100 Sum: 210; mean: 35 = 210/6 (c) 2007 IUPUI SPEA K300 (4392)

Dispersion: Computation1 N=6; µ=35, Ʃ(y-µ)^2= 1750; Ʃy^2=9100 Variance using the standard formula (c) 2007 IUPUI SPEA K300 (4392)

Dispersion: Computation2 N=6; µ=35, Ʃy^2=9100 Variance using the short-cut (c) 2007 IUPUI SPEA K300 (4392)

Dispersion: Computation3 N=6; Ʃy=210; µ=35, Ʃy^2=9100 Variance using the short-cut in textbook (c) 2007 IUPUI SPEA K300 (4392)

Dispersion: Computation4 σ^2=291.7, s^2=350, Ʃ(y-µ)^2= 1750 Compute standard deviation (c) 2007 IUPUI SPEA K300 (4392)