Section 10.2: Fitting a Linear Model to Data

Slides:



Advertisements
Similar presentations
Statistics Measures of Regression and Prediction Intervals.
Advertisements

 Coefficient of Determination Section 4.3 Alan Craig
10.1 Scatter Plots and Trend Lines
Section 8.3 – Systems of Linear Equations - Determinants Using Determinants to Solve Systems of Equations A determinant is a value that is obtained from.
Overview 4.2 Introduction to Correlation 4.3 Introduction to Regression.
Measures of Regression and Prediction Intervals
Regression lesson 4 Starter Dangers of Predicting (extrapolation) Interpretation questions Exam questions.
VCE Further Maths Least Square Regression using the calculator.
Managerial Economics Demand Estimation. Scatter Diagram Regression Analysis.
Further Topics in Regression Analysis Objectives: By the end of this section, I will be able to… 1) Explain prediction error, calculate SSE, and.
Linear Regression Least Squares Method: the Meaning of r 2.
Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.
Warm Up Write the equation of the line passing through each pair of passing points in slope-intercept form. 1. (5, –1), (0, –3) 2. (8, 5), (–8, 7) Use.
Statistical Methods Statistical Methods Descriptive Inferential
Sec 1.5 Scatter Plots and Least Squares Lines Come in & plot your height (x-axis) and shoe size (y-axis) on the graph. Add your coordinate point to the.
Regression Regression relationship = trend + scatter
9.2A- Linear Regression Regression Line = Line of best fit The line for which the sum of the squares of the residuals is a minimum Residuals (d) = distance.
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,
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.
Math 4030 – 11b Method of Least Squares. Model: Dependent (response) Variable Independent (control) Variable Random Error Objectives: Find (estimated)
Statistics: Unlocking the Power of Data Lock 5 STAT 250 Dr. Kari Lock Morgan Simple Linear Regression SECTION 2.6 Least squares line Interpreting coefficients.
LEAST-SQUARES REGRESSION 3.2 Role of s and r 2 in Regression.
Section 1.6 Fitting Linear Functions to Data. Consider the set of points {(3,1), (4,3), (6,6), (8,12)} Plot these points on a graph –This is called a.
Section 9.3 Measures of Regression and Prediction Intervals.
Method 3: Least squares regression. Another method for finding the equation of a straight line which is fitted to data is known as the method of least-squares.
Welcome to Algebra 2! Get out your homework Get out catalogs Get out writing utensils Put bags on the floor Be quiet!!! 3/2/ : Curve Fitting with.
Chapters 8 Linear Regression. Correlation and Regression Correlation = linear relationship between two variables. Summarize relationship with line. Called.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
4.5 – Analyzing Lines of Best Fit Today’s learning goal is that students will be able to Use residuals to determine how well lines of fit model data. Distinguish.
Section 11.1: Solving Linear Systems by Graphing
4-6 Regression Lines Goal:
Lesson 4.5 Topic/ Objective: To use residuals to determine how well lines of fit model data. To use linear regression to find lines of best fit. To distinguish.
Regression and Median-Fit Lines
2.1 Tangents & Velocities.
Section 11.4: Solving Linear Systems by Multiplying First
ENM 310 Design of Experiments and Regression Analysis
Section 8.3: Using Special Factors to Solve Equations
4.5 – Analyzing Lines of Best Fit
Section 10.1: Scatter Plots and Trend Lines
Module 15-2 Objectives Determine a line of best fit for a set of linear data. Determine and interpret the correlation coefficient.
The Least-Squares Regression Line
Chapter 15 Linear Regression
Section 13.3: Solving Absolute Value Equations
Section 11.2: Solving Linear Systems by Substitution
Linear Regression.
Solve Linear Systems Algebraically (part 2) Elimination
Correlation and Regression
Solve Systems of Linear Equations in Three Variables
Section 13.1: Understanding Piecewise-Defined Functions
Section 20.1: Exploring What Makes Triangles Congruent
Section 18.1: Sequences of Transformations
^ y = a + bx Stats Chapter 5 - Least Squares Regression
Solve Systems of Linear Inequalities
Linear regression Fitting a straight line to observations.
Section 9.3: Histograms and Box Plots
Section 5.2 Using Intercepts.
Residuals and Residual Plots
Interpreting Rate of Change and Slope
Major: All Engineering Majors Authors: Autar Kaw, Luke Snyder
Section 2.2: Solving Absolute Value Equations
Section 13.2: Absolute Value Functions and Transformations
Section 2: Linear Regression.
Least-Squares Regression
HW# : Complete the last slide
A medical researcher wishes to determine how the dosage (in mg) of a drug affects the heart rate of the patient. Find the correlation coefficient & interpret.
Section 12.2: Graphing Systems of LInear Inequalities
Dr. Fowler  AFM  Unit 8-5 Linear Correlation
Homework: PG. 204 #30, 31 pg. 212 #35,36 30.) a. Reading scores are predicted to increase by for each one-point increase in IQ. For x=90: 45.98;
Regression and Correlation of Data
Presentation transcript:

Section 10.2: Fitting a Linear Model to Data

Objective(s): By following instructions, students will be able to: Use the linear regression function on a graphing calculator to find the line of best fit for a two-variable data set.

Explore 2A

Explore 2A

Explore 2

The data in the tables are given along with two possible lines of fit The data in the tables are given along with two possible lines of fit. Calculate the residual for both lines of fit and then find the sum of the squared residuals. Identify the lesser sum and the line with better fit. Explain 1A X Y(actual) Y(predicted) by y=x+2.4 Residual for y=x+2.4 Square the residuals y=x+2.4 2 7 4 8 6 Y(predicted) by y=x+2.2 Residual for y=x+2.2 Square the residuals y=x+2.2 Compare the sums of the squared residuals.

The data in the tables are given along with two possible lines of fit The data in the tables are given along with two possible lines of fit. Calculate the residual for both lines of fit and then find the sum of the squared residuals. Identify the lesser sum and the line with better fit. Explain 1B X Y(actual) Y(predicted) by y=2x+3 Residual for y=2x+3 Square the residuals Y=2x+3 1 5 2 4 3 6 10 Y(predicted) by y=2x+2.5 Residual for y=2x+2.5 Square the residuals Y=2x+2.5 Next, square the residuals and find their sum.

The data in the table are given along with two possible lines of fit The data in the table are given along with two possible lines of fit. Calculate the residuals for both lines of fit and then find the sum of the squared residuals. Identify the lesser sum and the line with better fit. Your-Turn #1

Given latitudes and average temperatures in degrees Celsius for several cities, use your calculator to find an equation for the line of best fit. Then interpret the correlation coefficient and use the line of best fit to estimate the average temperature of another city using the given latitude. Explore 2A

Given latitudes and average temperatures in degrees Celsius for several cities, use your calculator to find an equation for the line of best fit. Then interpret the correlation coefficient and use the line of best fit to estimate the average temperature of another city using the given latitude. Explore 2B

Your-Turn #2

HW: Sec 10.2 pg 371 #s 1-13 ODD, 15-21 ALL