Technical Problem Solving 2014 -15. Gathering data, Gathering data, Processing data, and Processing data, and Using this data to solve the given problem.

Slides:



Advertisements
Similar presentations
Linear regression T-test Your last test !!. How good does this line fit the data?  What are some things that determine how good the line fits the.
Advertisements

1.What is Pearson’s coefficient of correlation? 2.What proportion of the variation in SAT scores is explained by variation in class sizes? 3.What is the.
Design of Experiments Lecture I
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.
11 Simple Linear Regression and Correlation CHAPTER OUTLINE
Correlation and Regression
Objectives (BPS chapter 24)
Chapter Eighteen MEASURES OF ASSOCIATION
Chapter Topics Types of Regression Models
Topics: Regression Simple Linear Regression: one dependent variable and one independent variable Multiple Regression: one dependent variable and two or.
Lecture 23 Multiple Regression (Sections )
PROBABILITY AND SAMPLES: THE DISTRIBUTION OF SAMPLE MEANS.
AP Statistics Section 12.1 A. Now that we have looked at the principles of testing claims, we proceed to practice. We begin by dropping the unrealistic.
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Chapter 2: The Research Enterprise in Psychology
Introduction to Linear Regression and Correlation Analysis
Chapter 2: The Research Enterprise in Psychology
APPENDIX B Data Preparation and Univariate Statistics How are computer used in data collection and analysis? How are collected data prepared for statistical.
September In Chapter 14: 14.1 Data 14.2 Scatterplots 14.3 Correlation 14.4 Regression.
6.1 What is Statistics? Definition: Statistics – science of collecting, analyzing, and interpreting data in such a way that the conclusions can be objectively.
Stats for Engineers Lecture 9. Summary From Last Time Confidence Intervals for the mean t-tables Q Student t-distribution.
1 8. Marketing Research & Information Systems. 2 The Marketing Information System Part of management information system Involves people, equipment & procedures.
Analyzing and Interpreting Quantitative Data
Topic 10 - Linear Regression Least squares principle - pages 301 – – 309 Hypothesis tests/confidence intervals/prediction intervals for regression.
TEKS (6.10) Probability and statistics. The student uses statistical representations to analyze data. The student is expected to: (B) identify mean (using.
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
Research Seminars in IT in Education (MIT6003) Quantitative Educational Research Design 2 Dr Jacky Pow.
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.
Descriptive & Inferential Statistics Adopted from ;Merryellen Towey Schulz, Ph.D. College of Saint Mary EDU 496.
Agresti/Franklin Statistics, 1 of 88 Chapter 11 Analyzing Association Between Quantitative Variables: Regression Analysis Learn…. To use regression analysis.
Appendix B: Statistical Methods. Statistical Methods: Graphing Data Frequency distribution Histogram Frequency polygon.
Chapter 2 The Research Enterprise in Psychology. Table of Contents The Scientific Approach: A Search for Laws Basic assumption: events are governed by.
© 2008 McGraw-Hill Higher Education The Statistical Imagination Chapter 8. Parameter Estimation Using Confidence Intervals.
Chapter 6: Analyzing and Interpreting Quantitative Data
College and Career-Readiness Conference Summer 2015 FOR ALGEBRA TEACHERS.
Correlation & Regression Analysis
Experimental Methods: Statistics & Correlation
STATISTICS FOR SCIENCE RESEARCH (The Basics). Why Stats? Scientists analyze data collected in an experiment to look for patterns or relationships among.
IMPORTANCE OF STATISTICS MR.CHITHRAVEL.V ASST.PROFESSOR ACN.
Copyright © 2011, 2005, 1998, 1993 by Mosby, Inc., an affiliate of Elsevier Inc. Chapter 19: Statistical Analysis for Experimental-Type Research.
Lesson 14 - R Chapter 14 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.
Doing the Right Thing! … statistically speaking...
Intro to Statistics for the Behavioral Sciences PSYC 1900 Lecture 7: Regression.
Power Point Slides by Ronald J. Shope in collaboration with John W. Creswell Chapter 7 Analyzing and Interpreting Quantitative Data.
Chapter 6 Becoming Acquainted With Statistical Concepts.
Univariate Point Estimation Confidence Interval Estimation Bivariate: Linear Regression Multivariate: Multiple Regression 1 Chapter 4: Statistical Approaches.
What Is Sociology? Original Content Copyright © Holt McDougal. Additions and changes to the original content are the responsibility of the instructor.
Statistical principles: the normal distribution and methods of testing Or, “Explaining the arrangement of things”
Statistics (Chapter 3). CHE Statistics Forensic science is based in experiment, measurement, and analysis. Whenever measurements are made, however,
Statistics & Evidence-Based Practice
Becoming Acquainted With Statistical Concepts
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Inference for Regression (Chapter 14) A.P. Stats Review Topic #3
REGRESSION G&W p
STATISTICS FOR SCIENCE RESEARCH
Splash Screen.
Analyzing and Interpreting Quantitative Data
Experimental Methods: Statistics & Correlation
Statistical Analysis of Research
Part Three. Data Analysis
Statistics with Stiles
STATS DAY First a few review questions.
SA3202 Statistical Methods for Social Sciences
Introduction to Statistics
Technical Problem Solving
Simple Linear Regression
Product moment correlation
15.1 The Role of Statistics in the Research Process
Introductory Statistics
Presentation transcript:

Technical Problem Solving

Gathering data, Gathering data, Processing data, and Processing data, and Using this data to solve the given problem Using this data to solve the given problem Description: Teams will be required to answer a question by;

Event and Team Parameters: Team of up to two Team of up to two Eye Protection: #4 Eye Protection: #4 Time Limit: 50 minutes Time Limit: 50 minutes Up to 3 events at Regional, State and 2 at the National Tournament. Up to 3 events at Regional, State and 2 at the National Tournament.

Possible Probes: Temperature Temperature Dual Force Dual Force Colorimeter Colorimeter CBR2 (Motion Detector) CBR2 (Motion Detector)

Event Topics Physical Science/Physics/Chemistry Physical Science/Physics/Chemistry Topic 1 & 2 will focus on the Forensic patterns of the physical evidence associated with a crime scene. The teams will design, conduct, and analyze experiments to solve a proposed crime based on the physical evidence supplied. Topic 1 & 2 will focus on the Forensic patterns of the physical evidence associated with a crime scene. The teams will design, conduct, and analyze experiments to solve a proposed crime based on the physical evidence supplied.

TPS Topic Related Terms

Science Principles Beer’s Law Newton’s Law of Cooling Significant Figures Random / Systematic Error

Mathematical Principles of Data Handling Stat Plot Line of Best Fit Regressions Statistics Mean, mode, median Standard Deviation Normal Distribution

Mathematics (cont.) Mathematical Modeling Residuals R-values Correlation coefficient Confidence Level Interpretation Extrapolation Significant Figures

Sample Labs Data Collection

ScoringScoring Teams ranked based on the highest total points from the sum of the scores for two stations. Teams ranked based on the highest total points from the sum of the scores for two stations. Station scores based on accuracy (50%), procedure (30%), and content knowledge (20%) Station scores based on accuracy (50%), procedure (30%), and content knowledge (20%) Teams ranked based on the highest total points from the sum of the scores for two stations. Teams ranked based on the highest total points from the sum of the scores for two stations. Station scores based on accuracy (60%), procedure (20%), and content knowledge (20%) Station scores based on accuracy (60%), procedure (20%), and content knowledge (20%)

Resources

Contact Information