Linear regression. Case study Galactose diffusion in silica mesopore.

Slides:



Advertisements
Similar presentations
Chapter 12 Simple Linear Regression
Advertisements

1 Simple Linear Regression and Correlation The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES Assessing the model –T-tests –R-square.
A Short Introduction to Curve Fitting and Regression by Brad Morantz
Classification and Prediction: Regression Via Gradient Descent Optimization Bamshad Mobasher DePaul University.
Statistics for Managers Using Microsoft® Excel 5th Edition
Statistics for Managers Using Microsoft® Excel 5th Edition
Chapter 13 Introduction to Linear Regression and Correlation Analysis
SIMPLE LINEAR REGRESSION
Quantitative Business Analysis for Decision Making Simple Linear Regression.
Linear Regression Example Data
SIMPLE LINEAR REGRESSION
Chapter 14 Introduction to Linear Regression and Correlation Analysis
Spreadsheet Problem Solving
Lorelei Howard and Nick Wright MfD 2008
Chapter 2 – Simple Linear Regression - How. Here is a perfect scenario of what we want reality to look like for simple linear regression. Our two variables.
1 1 Slide Simple Linear Regression Chapter 14 BA 303 – Spring 2011.
Regression Analysis British Biometrician Sir Francis Galton was the one who used the term Regression in the later part of 19 century.
Regression and Correlation Methods Judy Zhong Ph.D.
3.1 Solving Linear Systems by Graphing
SIMPLE LINEAR REGRESSION
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-1 Chapter 12 Simple Linear Regression Statistics for Managers Using.
Correlation and simple linear regression Marek Majdan Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;
Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.
Simple Linear Regression
1 FORECASTING Regression Analysis Aslı Sencer Graduate Program in Business Information Systems.
Analytical vs. Numerical Minimization Each experimental data point, l, has an error, ε l, associated with it ‣ Difference between the experimentally measured.
1 Experimental Statistics - week 10 Chapter 11: Linear Regression and Correlation.
Regression For the purposes of this class: –Does Y depend on X? –Does a change in X cause a change in Y? –Can Y be predicted from X? Y= mX + b Predicted.
Chap 12-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition Chapter 12 Introduction to Linear.
Applied Quantitative Analysis and Practices LECTURE#22 By Dr. Osman Sadiq Paracha.
Applied Quantitative Analysis and Practices LECTURE#23 By Dr. Osman Sadiq Paracha.
Equations of Linear Relationships
Go to Table of Content Single Variable Regression Farrokh Alemi, Ph.D. Kashif Haqqi M.D.
Chapter 7 Relationships Among Variables What Correlational Research Investigates Understanding the Nature of Correlation Positive Correlation Negative.
Regression Regression relationship = trend + scatter
Lesson Multiple Regression Models. Objectives Obtain the correlation matrix Use technology to find a multiple regression equation Interpret the.
Simple Linear Regression. The term linear regression implies that  Y|x is linearly related to x by the population regression equation  Y|x =  +  x.
Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 13-1 Introduction to Regression Analysis Regression analysis is used.
–The shortest distance is the one that crosses at 90° the vector u Statistical Inference on correlation and regression.
3.1 Solving Linear Systems by Graphing 9/20/13. Solution of a system of 2 linear equations: Is an ordered pair (x, y) that satisfies both equations. Graphically,
SWBAT: Calculate and interpret the residual plot for a line of regression Do Now: Do heavier cars really use more gasoline? In the following data set,
Economics 173 Business Statistics Lecture 10 Fall, 2001 Professor J. Petry
Linear Prediction Correlation can be used to make predictions – Values on X can be used to predict values on Y – Stronger relationships between X and Y.
Chapter 11: Linear Regression and Correlation Regression analysis is a statistical tool that utilizes the relation between two or more quantitative variables.
Algebra 2 January What are the 3 steps to graphing a linear equation? (section 2.2; Jan 12) 2. What is true about the slopes of perpendicular.
Unit 3 Section : Regression  Regression – statistical method used to describe the nature of the relationship between variables.  Positive.
Lecture 10 Introduction to Linear Regression and Correlation Analysis.
3.3 Solving Linear Systems by Linear Combination 10/12/12.
EPSY 641 – Intermediate Statistics Copyright © 2009 Robert J. Hall, Ph.D. Simple Linear Regression.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,
Chapter 12 Simple Regression Statistika.  Analisis regresi adalah analisis hubungan linear antar 2 variabel random yang mempunyai hub linear,  Variabel.
Chapter 11 Linear Regression and Correlation. Explanatory and Response Variables are Numeric Relationship between the mean of the response variable and.
13-4 The Sine Function Hubarth Algebra II.
Correlation and Regression
SIMPLE LINEAR REGRESSION MODEL
(Residuals and
Solutions, Zeros, and Roots
CHAPTER 29: Multiple Regression*
Linear regression Fitting a straight line to observations.
Statistical Inference about Regression
Nonlinear regression.
Solving Equations involving Decimal Coefficients
11C Line of Best Fit By Eye, 11D Linear Regression
SIMPLE LINEAR REGRESSION
Linear regression.
Regression and Correlation of Data
Presentation transcript:

Linear regression

Case study Galactose diffusion in silica mesopore

Controlled drug release systems

MSD balistic regime caging regime diffusive regime

MSD

T

1. How to check if the molecule is in the diffusive regime?  calculate slope of log(MSD) vs. log(t)

2. How to calculate self-diffusion coefficient?  calculate slope of MSD vs. T and divide it by 6.

How to analyse data?

Plot!

Plot! Human brain is one the most powerfull computationall tools Works differently than a computer…

Simple example – finding maximum y(x max ) Computer x1x1 x2x2 x3x3

Computer x1x1 1.Set y(x max ) = y(x 1 ). x2x2 x3x3

Simple example – finding maximum y(x max ) Computer x1x1 1.Set y(x max ) = y(x 1 ). 2.Go to the next point x 2 : x2x2 x3x3

Simple example – finding maximum y(x max ) Computer x1x1 1.Set y(x max ) = y(x 1 ). 2.Go to the next point x 2 : 1.If y(x 2 ) > y(x max ) then x max = x 2 2. Else do nothing. x2x2 x3x3

Simple example – finding maximum y(x max ) Computer x1x1 1.Set y(x max ) = y(x 1 ). 2.Go to the next point x 2 : 1.If y(x 2 ) > y(x max ) then x max = x 2 2. Else do nothing. 3. Repeat this procedure until you reach the end. x2x2 x3x3

Simple example – finding maximum y(x max ) Human brain x1x1 x2x2 x3x3

Simple example – finding maximum y(x max ) Human brain x1x1 x2x2 x3x3 Here!

Simple example – finding maximum y(x max ) Human brain x1x1 x2x2 x3x3 Here! With increasing number of points quicker answer

How to analyse data? Plot x against y Observe trend - correlation

How to „measure” linearity? Geometry

How to measure angle between two vectors? Scalar product

How to measure angle between two vectors? Scalar product

How to measure angle between two vectors? Scalar product

How to measure angle between two vectors? Scalar product

How to measure angle between two vectors? Scalar product

Example

Example How to do it?

Example We choose two vectors

Example How to do it? We choose two vectors

Example How to do it? We choose two vectors

Example How to do it? We choose two vectors

Example How to do it? We choose two vectors

Example How to do it? We choose two vectors

What’s the relevance? y1y1 x1x1 x2x2 x3x3 x4x4 y2y2 y3y3 y4y4 X Y Two sets of data Data are vectors!

What’s the relevance? y1y1 x1x1 x2x2 x3x3 x4x4 y2y2 y3y3 y4y4 X Y Two sets of data Linear relationship parallel

How to measure parallelism between two vectors? y1y1 x1x1 x2x2 x3x3 x4x4 y2y2 y3y3 y4y4 Linear relationship parallel = zero angle

How to calculute the angle? Scalar product! y1y1 x1x1 x2x2 x3x3 x4x4 y2y2 y3y3 y4y4 X Y Two sets of data

How to calculute the angle? Scalar product!

Our case y1y1 x1x1 x2x2 x3x3 x4x4 y2y2 y3y3 y4y4 X Y Two sets of data

X Y The best = smallest error What is the best position of the line? Error = data value – estimated value

X Y The best = smallest error What is the best position of the line?

How to adjust a and b so SSE is the smallest? How to calculate minimum of the SSE(a,b) function?

How to adjust a and b so SSE is the smallest?

We obtain a set of linear equations of two variables a and b

Finally… Set of linear equations

How to solve it? Set of linear equations.

Linear regression procedure