Number of vacations in past 5 years

Slides:



Advertisements
Similar presentations
Bi-Variate Data PPDAC. Types of data We are looking for a set of data that is affected by the other data sets in our spreadsheet. This variable is called.
Advertisements

Section 10-3 Regression.
Kin 304 Regression Linear Regression Least Sum of Squares
Probabilistic & Statistical Techniques Eng. Tamer Eshtawi First Semester Eng. Tamer Eshtawi First Semester
Correlation and Regression
Regression Regression: Mathematical method for determining the best equation that reproduces a data set Linear Regression: Regression method applied with.
5-7: Scatter Plots & Lines of Best Fit. What is a scatter plot?  A graph in which two sets of data are plotted as ordered pairs  When looking at the.
LINEAR REGRESSION: What it Is and How it Works Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r.
LINEAR REGRESSION: What it Is and How it Works. Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r.
REGRESSION What is Regression? What is the Regression Equation? What is the Least-Squares Solution? How is Regression Based on Correlation? What are the.
Business Statistics - QBM117 Least squares regression.
REGRESSION Predict future scores on Y based on measured scores on X Predictions are based on a correlation from a sample where both X and Y were measured.
Bivariate Data Analysis Bivariate Data analysis 3.
Descriptive Methods in Regression and Correlation
Linear Regression.
Scatter Plots and Lines of Fit Lesson 4-5 Splash Screen.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved Section 10-3 Regression.
1.4 Data in 2 Variables Definitions. 5.3 Data in 2 Variables: Visualizing Trends When data is collected over long period of time, it may show trends Trends.
1 FORECASTING Regression Analysis Aslı Sencer Graduate Program in Business Information Systems.
Writing the Equation of a Line
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.
Slope-Intercept Form of a Line
Section 4.2 Regression Equations and Predictions.
Graphing in Science Class
Holt Algebra Curve Fitting with Linear Models 2-7 Curve Fitting with Linear Models Holt Algebra 2 Lesson Presentation Lesson Presentation.
Do Now 12/3/09 Take out HW from last night. -Text p. 328, #3-6, 8-12 evens, 16 & 17 (4 graphs) Copy HW in planner. - Text p. 338, #4-14 evens, 18 & 20.
Regression Regression relationship = trend + scatter
Introduction to regression 3D. Interpretation, interpolation, and extrapolation.
Aim: Review for Exam Tomorrow. Independent VS. Dependent Variable Response Variables (DV) measures an outcome of a study Explanatory Variables (IV) explains.
College Prep Stats. x is the independent variable (predictor variable) ^ y = b 0 + b 1 x ^ y = mx + b b 0 = y - intercept b 1 = slope y is the dependent.
Correlation and Regression. Section 9.1  Correlation is a relationship between 2 variables.  Data is often represented by ordered pairs (x, y) and.
Chapter 2 – Linear Equations and Functions
Line of Best fit, slope and y- intercepts MAP4C. Best fit lines 0 A line of best fit is a line drawn through data points that represents a linear relationship.
1.9 Comparing Two Data Sets. Revisiting Go For the Gold! 3a) Whose slope is larger? The women’s is growing faster ( m/year > m/year)
Scatter Plots, Correlation and Linear Regression.
7-3 Line of Best Fit Objectives
Chapter 3-Examining Relationships Scatterplots and Correlation Least-squares Regression.
Chapter 2 Examining Relationships.  Response variable measures outcome of a study (dependent variable)  Explanatory variable explains or influences.
Scatter Plot and Trend Lines
9.2 Linear Regression Key Concepts: –Residuals –Least Squares Criterion –Regression Line –Using a Regression Equation to Make Predictions.
LEAST-SQUARES REGRESSION 3.2 Least Squares Regression Line and Residuals.
Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Chapter 10 Correlation and Regression 10-2 Correlation 10-3 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.
Simple Linear Regression The Coefficients of Correlation and Determination Two Quantitative Variables x variable – independent variable or explanatory.
Linear Best Fit Models Learn to identify patterns in scatter plots, and informally fit and use a linear model to solve problems and make predictions as.
Describing Relationships. Least-Squares Regression  A method for finding a line that summarizes the relationship between two variables Only in a specific.
Going Crackers! Do crackers with more fat content have greater energy content? Can knowing the percentage total fat content of a cracker help us to predict.
1 Objective Given two linearly correlated variables (x and y), find the linear function (equation) that best describes the trend. Section 10.3 Regression.
Altitude vs Atmpospere vs temp Purpose statement: I am going to investigate the relationship between Mean pressure and Tempurature (degrees C)
Lecture Slides Elementary Statistics Twelfth Edition
Linear Regression Essentials Line Basics y = mx + b vs. Definitions
Correlation & Forecasting
Predictions 3.9 Bivariate Data.
Chapter 1 A Physics Toolkit 1.3 Graphing Data.
Writing About Math Complete 1-5 Silently
Scatter Plots - Line of Best Fit
Chapter 1 A Physics Toolkit 1.3 Graphing Data.
Algebra 1 Section 6.6.
No notecard for this quiz!!
^ y = a + bx Stats Chapter 5 - Least Squares Regression
Lesson 5.7 Predict with Linear Models The Zeros of a Function
Chapter 3 Describing Relationships Section 3.2
Scatter Plots Unit 11 B.
LG: I can assess the reliability of a linear model
Scatterplots line of best fit trend line interpolation extrapolation
Algebra Review The equation of a straight line y = mx + b
Lesson 2.2 Linear Regression.
Chapter 9 Regression Wisdom.
Draw Scatter Plots and Best-Fitting Lines
Presentation transcript:

Number of vacations in past 5 years Minds On… Describe the correlation shown below Do you think it’s reasonable to assume that there is a ‘causal’ relationship between these variables? Number of vacations in past 5 years Number of Pets in household

LG: I can assess the reliability of a linear model Line of Best Fit LG: I can sketch a line of best fit, determine an equation for the line, and use this equation to make predictions LG: I can assess the reliability of a linear model

Lines of Best Fit A line of best fit is a line drawn through data points to best represent a linear relationship between the two variables Also called a trend line or regression line The line is not just ‘through the middle’, it should be as close as possible to all data points A line of best fit doesn’t work for all data; sometimes a curve of best fit is a better option

Outliers Any point that lies far away from the main cluster of points is an outlier May be caused by inaccurate measurements, or may be unusual but still valid The line of best fit should reflect all valid data points, including outliers

Effect of Outliers on Line of Best Fit Which trend line best represents the data? Why? Suggests there are NO outliers Gives too much importance to the outliers Is affected by the outliers, but is affected MORE by the larger cluster of data

Recall: Find the equation of this line y = mx + b STEP 1: Choose 2 points on the line STEP 2: Find slope STEP 3: Find y-intercept STEP 4: state equation

Using a Line of Best Fit to Make Predictions Interpolation – Predictions WITHIN data point Extrapolation – Predictions BEYOND data points

Reliability Some factors make predictions from a line of best fit less reliable Data spread over small range Small sample size Nonlinear data