Regression To be able to calculate the regression line of y on x To be able to interpret the equation of the regression line By the end of the lesson you.

Slides:



Advertisements
Similar presentations
CORRELATION To be able to plot scattergraphs accurately To be able to describe and interpret correlation To be able to calculate Sxx, Syy, Sxy and the.
Advertisements

Least-Squares Regression Section 3.3. Correlation measures the strength and direction of a linear relationship between two variables. How do we summarize.
Physics 1.2.
Regression and Correlation
Linear Regression Larson/Farber 4th ed. 1 Section 9.2.
Regression, Residuals, and Coefficient of Determination Section 3.2.
Section 9.2 Linear Regression © 2012 Pearson Education, Inc. All rights reserved.
Measure your handspan and foot length in cm to nearest mm We will record them as Bivariate data below: Now we need to plot them in what kind of graph?
The Scientific Method Interpreting Data — Correlation and Regression Analysis.
Biostatistics Unit 9 – Regression and Correlation.
Regression Section 10.2 Bluman, Chapter 101. Example 10-1: Car Rental Companies Construct a scatter plot for the data shown for car rental companies in.
Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.
Least-Squares Regression: Linear Regression Section 3.2 Reference Text: The Practice of Statistics, Fourth Edition. Starnes, Yates, Moore.
Least Squares Regression: y on x © Christine Crisp “Teach A Level Maths” Vol. 2: A2 Core Modules.
Linear Regression Model In regression, x = independent (predictor) variable y= dependent (response) variable regression line (prediction line) ŷ = a +
Regression Regression relationship = trend + scatter
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
Regression.
Section 9.2 Linear Regression. Section 9.2 Objectives Find the equation of a regression line Predict y-values using a regression equation.
CHAPTER 5 Regression BPS - 5TH ED.CHAPTER 5 1. PREDICTION VIA REGRESSION LINE NUMBER OF NEW BIRDS AND PERCENT RETURNING BPS - 5TH ED.CHAPTER 5 2.
Correlation.
Linear Regression 1 Section 9.2. Section 9.2 Objectives 2 Find the equation of a regression line Predict y-values using a regression equation.
Copyright © 2015, 2012, and 2009 Pearson Education, Inc. 1 Chapter Correlation and Regression 9.
^ y = a + bx Stats Chapter 5 - Least Squares Regression
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
Chapter 5 Lesson 5.2 Summarizing Bivariate Data 5.2: LSRL.
Chapters 8 Linear Regression. Correlation and Regression Correlation = linear relationship between two variables. Summarize relationship with line. Called.
Unit One: Math Review 1.What is the SI unit for mass? ans: kilogram 2. What prefix means 1000 x large? ans: kilo- 3. What is the prefix represented in.
REGRESSION Stats 1 with Liz. AIMS By the end of the lesson, you should be able to… o Understand the method of least squares to find a regression line.
The Least Squares Regression Line. The problem with drawing line of best fit by eye is that the line drawn will vary from person to person. Instead, use.
Explain the trend the graph shows. Extrapolate the graph to make predictions. Outcomes Draw a line graph with all labels and units. How are these 4 pictures.
Regression lines A line of best fit should: Go through ( x , y )
LEAST – SQUARES REGRESSION
CHAPTER 3 Describing Relationships
Chapter 3: Describing Relationships
Regression and Correlation
Least-Squares Regression
LSRL Least Squares Regression Line
Data Analysis and Statistical Software I ( ) Quarter: Autumn 02/03
AP Stats: 3.3 Least-Squares Regression Line
Ice Cream Sales vs Temperature
^ y = a + bx Stats Chapter 5 - Least Squares Regression
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
Residuals and Residual Plots
Least-Squares Regression
Chapter 3: Describing Relationships
Do now Take out worksheet from yesterday and check your answers with your groups Please grab a chromebook for your groups.
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
Regression All about going back (regressing) from the points to the function from which they came. We are looking at linear regression.
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
3.2 – Least Squares Regression
Chapter 3: Describing Relationships
Tuesday, September 29 Check HW Probs 3.13, 14, 20 (7-11-doubles)
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
Lesson 2.2 Linear Regression.
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
Least Squares Regression Chapter 3.2
Chapter 3: Describing Relationships
9/27/ A Least-Squares Regression.
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
Chapter 3: Describing Relationships
Presentation transcript:

Regression To be able to calculate the regression line of y on x To be able to interpret the equation of the regression line By the end of the lesson you should be able to answer this regression exam question

The meaning of the regression line Rather than draw a line a best fit by eye you can calculate and plot accurately a line that minimises the distance between the data plotted and the line. y = a + bx a is the value of the melting point when carbon is 0% b is the rate at which the melting point reduces as the carbon percentage increases The vertical distance between the points and the line of best fit are called residuals (red lines) This is why the regression line is sometimes called the least squares regression line

The meaning of the regression line The x axis shows the independent (or explanatory) variable. It is set independently of the other variable The y axis shows the dependent (or response) variable. These variables are determined by the x values

Important formulae Sxx = Σx² - (Σx)² n Sxy = Σxy - ΣxΣy n b = Sxy Sxx a = y - bx Regression line equation y = a + bx

Example The results from an experiment in which different masses were placed on a spring and the resulting length of the spring measured, are shown below Mass, x (kg) Length, y (cm) Σx = 300, Σx²=22000, x = 60, Σxy = 18238, Σy² = , Σy = 288.6, y = a)Calculate Sxx and Sxy b)Calculate the regression line of y on x c)Calculate the length of the spring when a mass of 50kg is added d)Calculate the length of the spring when a mass of 140kg is added. Give a reason why this may or may not be a reliable answer.

Mass, x (kg) Length, y (cm) Σx = 300, Σx²=22000, x = 60, Σxy = 18238, Σy² = , Σy = 288.6, y = a)Calculate Sxx and Sxy Sxx = ² = Sxy = – 300x288.6 = b) Calculate the regression line of y on x b = Sxy = 922 = Sxx 4000 a = y – bx a = – x 60 = y = x

c) Calculate the length of the spring when a mass of 50kg is added d) Calculate the length of the spring when a mass of 140kg is added. Give a reason why this is or is not a reliable answer y = x c) Y = x50 = cm d) Y = x 140 = 75.66cm This may not a reliable answer as it has been calculated using extrapolation. It could be unreliable140kg is outside the range of data given and used to calculate the regression line.

Plenary Can you now answer this mathsnet exam question?