Residuals. Residual or Deviation A residual or deviation = y data – y line = y actual – y theoetical = y experiment - y model A residual plot is if for.

Slides:



Advertisements
Similar presentations
5.4 Correlation and Best-Fitting Lines
Advertisements

Chapter 3 Examining Relationships Lindsey Van Cleave AP Statistics September 24, 2006.
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.
4.1: Linearizing Data.
 With your partner, roll a number cube 20 times. Record your data in a table. Include a column for the cumulative total (or sum) of your rolls up to that.
Inference for Regression 1Section 13.3, Page 284.
* Obviously, the pattern of the points in the sample does not match the pattern of the population.
Regression and Correlation
AP Statistics Chapter 8: Linear Regression
Welcome to class today! Chapter 12 summary sheet Jimmy Fallon video
Quantitative Business Analysis for Decision Making Simple Linear Regression.
Linear Regression Analysis
Measures of dispersion Standard deviation (from the mean) ready.
Adapted from Walch Education A linear equation describes a situation where there is a near- constant rate of change. An exponential equation describes.
Linear Regression.
Measures of Regression and Prediction Intervals
Residuals and Residual Plots Most likely a linear regression will not fit the data perfectly. The residual (e) for each data point is the ________________________.
Biostatistics Unit 9 – Regression and Correlation.
Advanced Algebra II Notes 3.5 Residuals Residuals: y-value of data point – y-value on the line Example: The manager of Big K Pizza must order supplies.
Confidence Intervals for the Regression Slope 12.1b Target Goal: I can perform a significance test about the slope β of a population (true) regression.
+ Chapter 12: Inference for Regression Inference for Linear Regression.
Scatter plots and Regression Algebra II. Linear Regression  Linear regression is the relationship between two variables when the equation is linear.
Section 4.2 Least Squares Regression. Finding Linear Equation that Relates x and y values together Based on Two Points (Algebra) 1.Pick two data points.
Correlation & Regression – Non Linear Emphasis Section 3.3.
Wednesday, May 13, 2015 Report at 11:30 to Prairieview.
Quadratic Models without QuadReg Julie Graves NCSSM and Philips Exeter Academy June 2014.
3.2 Least Squares Regression Line. Regression Line Describes how a response variable changes as an explanatory variable changes Formula sheet: Calculator.
7.5 – Graphing Square Roots and Cube Roots
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
Regression Regression relationship = trend + scatter
+ Chapter 12: More About Regression Section 12.1 Inference for Linear Regression.
1. {(0, 10), (2, 7), (4, 5), (6, 2), (10, 1) } a. Make a scatter plot b. Describe the correlation c. Write the equation of the line of best fit.
3.2 - Least- Squares Regression. Where else have we seen “residuals?” Sx = data point - mean (observed - predicted) z-scores = observed - expected * note.
Section 2.6 – Draw Scatter Plots and Best Fitting Lines A scatterplot is a graph of a set of data pairs (x, y). If y tends to increase as x increases,
Line of Best Fit 4.2 A. Goal Understand a scatter plot, and what makes a line a good fit to data.
5.4 Line of Best Fit Given the following scatter plots, draw in your line of best fit and classify the type of relationship: Strong Positive Linear Strong.
Correlation The apparent relation between two variables.
9.1B – Computing the Correlation Coefficient by Hand
 Find the Least Squares Regression Line and interpret its slope, y-intercept, and the coefficients of correlation and determination  Justify the regression.
7-3 Line of Best Fit Objectives
9.2 Linear Regression Key Concepts: –Residuals –Least Squares Criterion –Regression Line –Using a Regression Equation to Make Predictions.
Example: set E #1 p. 175 average ht. = 70 inchesSD = 3 inches average wt. = 162 lbs.SD = 30 lbs. r = 0.47 a)If ht. = 73 inches, predict wt. b)If wt. =
2.5 CORRELATION AND BEST-FITTING LINES. IN THIS LESSON YOU WILL : Use a scatter plot to identify the correlation shown by a set of data. Approximate the.
Learning Task/Big Idea: Students will learn how to graph quadratic equations by binding the vertex and whether the parabola opens up or down.
Residual Plots Unit #8 - Statistics.
Linear Regression What kind of correlation would the following scatter plots have? Negative Correlation Positive Correlation No Correlation.
Introduction to Regression
Linear Regression Chapter 8. Fat Versus Protein: An Example The following is a scatterplot of total fat versus protein for 30 items on the Burger King.
Warm – up #6 1. {(0, 10), (2, 7), (4, 5), (6, 2), (10, 1) } a. Make a scatter plot b. Describe the correlation c. Write the equation of the line of best.
Predicting With Lines of Best Fit Draw a line of best fit for a set of data. Use a line of best fit to make predictions. Lesson 5.2.
AP Statistics Section 15 A. The Regression Model When a scatterplot shows a linear relationship between a quantitative explanatory variable x and a quantitative.
REGRESSION MODELS OF BEST FIT Assess the fit of a function model for bivariate (2 variables) data by plotting and analyzing residuals.
Chicken Pox in the United States x y # of years after 1988 Chicken Pox in the U.S.A. YearCases (Thousands)
Chapter 8 Part I Answers The explanatory variable (x) is initial drop, measured in feet, and the response variable (y) is duration, measured in seconds.
Residuals Algebra.
Regression and Correlation
MATH 2311 Section 5.5.
Ch12.1 Simple Linear Regression
Data Transformation Data Analysis.
2.6 Draw Scatter Plots and Best-Fitting Lines
No notecard for this quiz!!
Unit 3 – Linear regression
Examining Relationships
Residuals and Residual Plots
Systems of Linear and Quadratic Equations
MATH 2311 Section 5.5.
Criteria for tests concerning standard deviations.
Lesson 2.2 Linear Regression.
Draw Scatter Plots and Best-Fitting Lines
Creating and interpreting scatter plots
Presentation transcript:

Residuals

Residual or Deviation A residual or deviation = y data – y line = y actual – y theoetical = y experiment - y model A residual plot is if for each data point, you keep the x and make the y-value equal to the residual (AKA deviation) There should be no obvious pattern to the deviation plot If there is an obvious pattern then the model is probably inadequate and you need to switch to a parabola, square root, etc.

Residual or Deviation A residual or deviation = y data – y line The data point at x = 8 is 0.34 units below the line, so the residual plot has the point (8, –0.34). The data point at x = 2 is 0.88 units above the line so the residual plot has the point (2, 0.88) Normal PlotResidual Plot

Residual Plot More scatter at higher x-values, so there’s more predictability at low number of slices.

Residual Plot This looks linear, but the residual shows hidden curvature. Actual equation y = 0.01x x + 1 Subtle curvature is often revealed in residual plot