 ? Presented by: Samuel J. Ivy Kevin WingfieldAshley J. Sullivan Morehouse College Morehouse College Spelman College Jamika BaltropAmanda Eure Elizabeth.

Slides:



Advertisements
Similar presentations
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Advertisements

Forecasting Using the Simple Linear Regression Model and Correlation
11 Simple Linear Regression and Correlation CHAPTER OUTLINE
BHS Methods in Behavioral Sciences I April 18, 2003 Chapter 4 (Ray) – Descriptive Statistics.
14-1 Introduction An experiment is a test or series of tests. The design of an experiment plays a major role in the eventual solution of the problem.
© 2002 Prentice-Hall, Inc.Chap 8-1 Statistics for Managers using Microsoft Excel 3 rd Edition Chapter 8 Two Sample Tests with Numerical Data.
The Simple Regression Model
Fall 2006 – Fundamentals of Business Statistics 1 Chapter 13 Introduction to Linear Regression and Correlation Analysis.
BHS Methods in Behavioral Sciences I April 21, 2003 Chapter 4 & 5 (Stanovich) Demonstrating Causation.
1 Pertemuan 13 Uji Koefisien Korelasi dan Regresi Matakuliah: A0392 – Statistik Ekonomi Tahun: 2006.
SIMPLE LINEAR REGRESSION
Pengujian Parameter Koefisien Korelasi Pertemuan 04 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Linear Regression and Correlation Analysis
Chapter 13 Introduction to Linear Regression and Correlation Analysis
SIMPLE LINEAR REGRESSION
Korelasi dalam Regresi Linear Sederhana Pertemuan 03 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
© 2000 Prentice-Hall, Inc. Chap Forecasting Using the Simple Linear Regression Model and Correlation.
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Chapter 14 Introduction to Linear Regression and Correlation Analysis
Chapter 7 Forecasting with Simple Regression
Constant Dosage day 7 Apigenin Control. Weight Adjusted Dosage Control Apigenin.
Cost Analysis and Classification Systems
SIMPLE LINEAR REGRESSION
Introduction to Linear Regression and Correlation Analysis
Statistics for the Behavioral Sciences (5 th ed.) Gravetter & Wallnau Chapter 17 The Chi-Square Statistic: Tests for Goodness of Fit and Independence University.
Copyright © Cengage Learning. All rights reserved.
Class Meeting #11 Data Analysis. Types of Statistics Descriptive Statistics used to describe things, frequently groups of people.  Central Tendency 
6.1 What is Statistics? Definition: Statistics – science of collecting, analyzing, and interpreting data in such a way that the conclusions can be objectively.
Hypothesis test flow chart frequency data Measurement scale number of variables 1 basic χ 2 test (19.5) Table I χ 2 test for independence (19.9) Table.
Unit 4: Modeling Topic 6: Least Squares Method April 1, 2003.
EQT 272 PROBABILITY AND STATISTICS
PowerPoint presentations prepared by Lloyd Jaisingh, Morehead State University Statistical Inference: Hypotheses testing for single and two populations.
OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.
© 2003 Prentice-Hall, Inc.Chap 13-1 Basic Business Statistics (9 th Edition) Chapter 13 Simple Linear Regression.
S519: Evaluation of Information Systems Social Statistics Inferential Statistics Chapter 10: t test.
Introduction to Linear Regression
Graphs, Charts, and Tables - Describing Your Data ©
14-1 Introduction An experiment is a test or series of tests. The design of an experiment plays a major role in the eventual solution of the problem.
-Test for one and two means -Test for one and two proportions
Ch4 Describing Relationships Between Variables. Section 4.1: Fitting a Line by Least Squares Often we want to fit a straight line to data. For example.
Chapter 2 Graphs, Charts, and Tables - Describing Your Data ©
The Nature of Science & Science Skills Test Review.
Trial Group AGroup B Mean P value 2.8E-07 Means of Substances Group.
1 11 Simple Linear Regression and Correlation 11-1 Empirical Models 11-2 Simple Linear Regression 11-3 Properties of the Least Squares Estimators 11-4.
Copyright © Cengage Learning. All rights reserved. 13 Linear Correlation and Regression Analysis.
CORRELATION: Correlation analysis Correlation analysis is used to measure the strength of association (linear relationship) between two quantitative variables.
1 Chapter 6 Energy and Energy Transfer 2 3 Introduction to Energy The concept of energy is one of the most important topics in science Every physical.
Chapter Twelve The Two-Sample t-Test. Copyright © Houghton Mifflin Company. All rights reserved.Chapter is the mean of the first sample is the.
Data Analysis.
Lecture 10: Correlation and Regression Model.
Introduction to Basic Statistical Tools for Research OCED 5443 Interpreting Research in OCED Dr. Ausburn OCED 5443 Interpreting Research in OCED Dr. Ausburn.
Scatter Diagrams scatter plot scatter diagram A scatter plot is a graph that may be used to represent the relationship between two variables. Also referred.
Foundations for Functions Chapter Exploring Functions Terms you need to know – Transformation, Translation, Reflection, Stretch, and Compression.
Business Statistics, 4e, by Ken Black. © 2003 John Wiley & Sons Business Statistics, 4e by Ken Black Chapter 10 Statistical Inferences about Two.
1 Chapter 6 Energy and Energy Transfer 2 3 Introduction to Energy The concept of energy is one of the most important topics in science Every physical.
ENGR 610 Applied Statistics Fall Week 7 Marshall University CITE Jack Smith.
Correlation. The statistic: Definition is called Pearsons correlation coefficient.
Lecture 7: Bivariate Statistics. 2 Properties of Standard Deviation Variance is just the square of the S.D. If a constant is added to all scores, it has.
S519: Evaluation of Information Systems Social Statistics Inferential Statistics Chapter 9: t test.
DETERMINING THE GRIP STRENGTH OF A ROBOTIC MANIPULATOR ANNA MARTIN, MECHANICAL ENGINEERING MENTOR: DR. SPRING BERMAN SCHOOL FOR THE ENGINEERING OF MATTER,
CORRELATION-REGULATION ANALYSIS Томский политехнический университет.
Descriptive Statistics ( )
MATH-138 Elementary Statistics
Analysis and Empirical Results
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Statistics for Managers using Microsoft Excel 3rd Edition
SIMPLE LINEAR REGRESSION
ENM 310 Design of Experiments and Regression Analysis Chapter 3
Scatter Plots That was easy Year # of Applications
Presentation transcript:

 ? Presented by: Samuel J. Ivy Kevin WingfieldAshley J. Sullivan Morehouse College Morehouse College Spelman College Jamika BaltropAmanda Eure Elizabeth City-State UniversityWinston-Salem State University

 This investigation seeks to find a relationship between the frequencies of forest fires with acreage burned effecting the state of Kentucky and the factors of global warming. Under global warming, we focus on the components climate change and precipitation rate in hopes of establishing this relationship. In delving deeper into the effects of forest fires, or wildfires, we explore a mathematical model offered as a solution to optimally contain these disasters while minimizing the costs of resources and eventually recovery.

 Each year millions of wild land globally are consumed by forest fire  Causes damages to harvesting and residential areas while conflicting high financial burdens to state and federal governments  According to Science, global warming is thought to be a catalyst for forest fires  There’s a concern to reduce the minimize forest fires.

 Regression Analysis:  ANOVA  Differential Inclusions

Figure 1: The visualization shows a change of the number of fires in Kentucky, ranging from 330 to 4,600 over the years of 1945 – The peak over this 60 year period was in 1963 with 4,579 fires. However, the smallest number of fires occurred in 1946 with 331 fires. Figure 2: The visualization shows changes of annual precipitation in Kentucky, ranging from 34 to 63 inches over the years 1945 – The peak over this 60 year period was in 1950 with a precipitation of However, the smallest amount of precipitation occurred in 1963 at

Figure 3: The visualization shows change of the annual average temperature, ranging from approximately 54 ⁰ F- 58 ⁰ F over the years of The peak over this 60 year period was in 1998 where the annual average temperature was ⁰ F and the lowest is ⁰ F in 1958.

Figure 4: A cubic function of sq. acres burned over the years Figure 5: A cubic function of sq. acres burned as a function of temperature. Results: Fig. 5 & Fig. 6 shows no correlation with the number of sq. acres burned

Figure 6: A cubic function of the number of sq. acres burned as a function of the annual precipitation. Figure 7: A 3-D scatter plot with the number of sq. acres burned along the y- axis, annual precipitation on x-axis and annual average temperature on the z-axis. Results: Negative correlation between the number of sq. acres burned and annual precipitation. showed that a correlation exists but temperature does not play a role in the number of sq. acres

Table 1: The output from the Data Analysis tool in Excel with five year period as a factor. Table 2: The output from the Data Analysis tool in Excel with the precipitation levels. Results: no significant difference between average acreage burned across yr periods

t-Test: Two-Sample Assuming Unequal Variances Low Precipitation Medium Precipitation Mean Variance E+09 Observations1729 Hypothesized Mean Difference0 df18 t Stat P(T<=t) one-tail t Critical one-tail Table 3: The output from the Data Analysis tool in Excel. t-Test: Two-Sample Assuming Unequal Variances Medium Precipitation High Precipitation Mean Variance3.22E Observations3013 Hypothesized Mean Difference0 df32 t Stat P(T<=t) one-tail t Critical one-tail Table 4: The output from the Data Analysis tool in Excel. Results: There exists a significant difference between the average acreage burned across the 3 precipitation levels

A differential inclusion takes on the form where F:  2   2 is a set valued function Moreover, where F is Lipschitz with Lipschitz constant k: That is

Figure 8: The solution to the example. Figure 9: The graph of the reachable set using Riemann Sum.

 Assume fire can be contained.  Then a controller can construct a “wall” or one dimensional rectifiable curve that can reduce the size of affected area.  Let block strategy  be defined as where R  (t) is the set reached by trajectories of differential inclusion at any given time 

Figure 10: The left diagram shows the construction of the wall at the same time the contaminated set R 0 expands. The right diagram takes into account additional area in time τ > 0 for wall construction.

 Observations:  Theorem 1. For the system described above, assume for some  ’>2  and every  2. Then, for every bounded initial set R 0, there exists r > 0 and an admissible strategy  such that, for all t  0.  If there exists an optimal strategy, then at every point of a free arc there exists a corresponding vector oriented in the direction of outer normal to the minimal time function, and the vector’s curvature is proportional to cost.  Let there be an optimal strategy. By constructing two boundary arcs originating from the same point P in opposition directions with respect to the front of the fire and assuming that the contaminated region is encircled by walls, than this strategy is not optimal.

 High temp has relationship with frequency of forest fires & the amount of acres burned  There’s a relationship between precipitation & the number sq. acreage burned  There’s a significant difference between the average acreage burned across 3 precipitation levels.  There exists a relationship between global warming and forest fires.

 Dr. Luttamaguzi, our faculty mentor  Dr. Johnny Houston, Institute Director  Dr. Farrah Chandler, Associate Director  Other faculty and peers