'Box plot' presentation slideshows

3-1 Discrete Random Variables

3-1 Discrete Random Variables. 3-1 Discrete Random Variables. Example 3-1. 3-2 Probability Distributions and Probability Mass Functions. Figure 3-1 Probability distribution for bits in error . 3-2 Probability Distributions and Probability Mass Functions.

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766 views • 59 slides

INTRODUCTION TO BIOSTATISTICS

INTRODUCTION TO BIOSTATISTICS. DR.S.Shaffi Ahamed Asst. Professor Dept. of Family and Comm. Medicine KKUH. This session covers:. Origin and development of Biostatistics Definition of Statistics and Biostatistics Reasons to know about Biostatistics Types of data

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STATS & RELATED

574 views • 48 slides

Tables & Charts

685 views • 45 slides

Statistics and Data Analysis

432 views • 35 slides

Goals

Chapter Four. Goals. Describing Data: Displaying and Exploring Data. GOALS When you have completed this chapter, you will be able to:. ONE Develop and interpret a dot plot. TWO Develop and interpret a stem-and-leaf display. THREE Compute and interpret quartiles, deciles, and percentiles.

551 views • 30 slides

Chart

Today you will: Interpret or solve real world problems using data from charts , (dot) line plots, stem-and-leaf plots, double-bar graphs, line graphs, (box plot) box-and-whisker plots, scatter plots, frequency tables or double line graphs . . Chart. Line Plot Dot Plot.

615 views • 30 slides

A Conceptual Approach to Statistics

A Conceptual Approach to Statistics. Mitchell H. Katz, MD Director of Health. City & County of San Francisco Department of Public Health Mitch.Katz@sfdph.org. Why is statistic analysis so important for clinical research?. Because most treatments are not that effective!.

504 views • 27 slides

Distributions & Graphs

Distributions & Graphs. Variable Types. Discrete (nominal) Sex, race, football numbers Continuous (interval, ratio) Temperature, Test score, Reaction time. Frequency Distributions. Graphic representation of data Easier to understand than raw numbers Helps communicate to others

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Data preprocessing before classification

509 views • 23 slides

Exploratory Tools for Spatial Data: Diagnosing Spatial Autocorrelation

Exploratory Tools for Spatial Data: Diagnosing Spatial Autocorrelation. Main Message when modeling & analyzing spatial data: SPACE MATTERS!. Relationships between observations from independent data can be analyzed in numerous ways. Some include:

383 views • 20 slides

Describing a Skewed Distribution Numerically

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Chapter 15

Chapter 15. ANOVA: Testing for Differences among Many Samples, and Much More. ANOVA. An alysis o f V ariance A general framework for statistical hypothesis testing Based on comparing sources of variability

269 views • 14 slides

Effects of Scanner Height on Fingerprint Capture

Effects of Scanner Height on Fingerprint Capture. Brian Stanton, Mary Theofanos, Shahram Orandi, Ross Micheals, Nien-Fan Zhang. HFES October 5, 2007.

322 views • 14 slides

INCLUDING UNCERTAINTY MODELS FOR SURROGATE BASED GLOBAL DESIGN OPTIMIZATION The EGO algorithm

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION GROUP. INCLUDING UNCERTAINTY MODELS FOR SURROGATE BASED GLOBAL DESIGN OPTIMIZATION The EGO algorithm. Thanks to Felipe A. C. Viana. BACKGROUND: SURROGATE MODELING.

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Name: ___________________________________ 9/9 (or 9/10) Algebra Warm-up

Name: ___________________________________ 9/9 (or 9/10) Algebra Warm-up Give the five-number summary for the data set {3,7,8,5,12,14,21,13,18}. Using the five-number summary above, sketch a box-plot for the data. Name: ___________________________________ 9/9 (or 9/10) Algebra Warm-up

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Assessing Normality

Assessing Normality. Are my data normally distributed?. Evidence for Normality. Mean and median should not be too different. Histogram or stem-and-leaf plot should look symmetric. Box plot should look symmetric. Normal probability plot should roughly be a straight line.

436 views • 6 slides

Height, Weight & BMI Difference

Height, Weight & BMI Difference. By Paige Hall and Jessica Reid. What is the Height, Weight & BMI Difference Between 10 Boys & 10 Girls at 16 Years of Age?. Data (Height, Weight & BMI).

488 views • 6 slides

Data for Decision-Making Processes: Linking Data to Quality Improvement Initiatives

Data for Decision-Making Processes: Linking Data to Quality Improvement Initiatives . John Dow, M.S.W. South Florida Behavioral Health Network. Bisma Sayed, M.S.W. University of Miami Department of Sociology. Objectives. Understand the value of utilizing data for decision-making

185 views • 0 slides