Correlation Coefficient -1 0 1 Negative No Positive Correlation Correlation Correlation.

Slides:

Advertisements

Similar presentations

Correlation Chapter 9.

Advertisements

CORRELATON & REGRESSION

PPA 415 – Research Methods in Public Administration

Correlation MARE 250 Dr. Jason Turner.

Correlation: Relationship between Variables

Covariance and Correlation

Correlation and Regression. Relationships between variables Example: Suppose that you notice that the more you study for an exam, the better your score.

Review Regression and Pearson’s R SPSS Demo

Linear Regression Analysis

Chapter 8: Bivariate Regression and Correlation

Correlation By Dr.Muthupandi,. Correlation Correlation is a statistical technique which can show whether and how strongly pairs of variables are related.

Correlation and regression 1: Correlation Coefficient

Scatter Plots and Linear Correlation. How do you determine if something causes something else to happen? We want to see if the dependent variable (response.

Copyright © 2011 Pearson Education, Inc. Slide 5-1 Unit 5E Correlation Coefficient.

Scatterplots October 14, Warm-Up Given the following domain and range in set notation, write the equivalent domain and range in algebraic notation.

Aims: To use Pearson’s product moment correlation coefficient to identify the strength of linear correlation in bivariate data. To be able to find the.

C. A. Warm Up 1/28/15 SoccerBasketballTotal Boys1812 Girls1614 Total Students were asked which sport they would play if they had to choose. 1)Fill in the.

Ski Party. The Situation The graduating class his year has decided to have a unique graduation party. They have rented a ski hill for a day. The rental.

Lecture 22 Dustin Lueker.  The sample mean of the difference scores is an estimator for the difference between the population means  We can now use.

Agenda Review Association for Nominal/Ordinal Data –  2 Based Measures, PRE measures Introduce Association Measures for I-R data –Regression, Pearson’s.

1. Graph 4x – 5y = -20 What is the x-intercept? What is the y-intercept? 2. Graph y = -3x Graph x = -4.

4.2 Introduction to Correlation Objective: By the end of this section, I will be able to… Calculate and interpret the value of the correlation coefficient.

4-6 Scatter Plots and Lines of Best Fit

Run the colour experiment where kids write red, green, yellow …first.

 Graph of a set of data points  Used to evaluate the correlation between two variables.

2 pt 3 pt 4 pt 5pt 1 pt 2 pt 3 pt 4 pt 5 pt 1 pt 2pt 3 pt 4pt 5 pt 1pt 2pt 3 pt 4 pt 5 pt 1 pt 2 pt 3 pt 4pt 5 pt 1pt Slope-Intercept Form Point-Slope.

7-2 Correlation Coefficient Objectives Determine and interpret the correlation coefficient.

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.

Year 8 Scatter Diagrams Dr J Frost Last modified: 24 th November 2013 Objectives: Understand the purpose of a scatter diagram,

Relationships If we are doing a study which involves more than one variable, how can we tell if there is a relationship between two (or more) of the.

Correlation and Regression. Section 9.1  Correlation is a relationship between 2 variables.  Data is often represented by ordered pairs (x, y) and.

3.3 Correlation: The Strength of a Linear Trend Estimating the Correlation Measure strength of a linear trend using: r (between -1 to 1) Positive, Negative.

Chapter 4 Summary Scatter diagrams of data pairs (x, y) are useful in helping us determine visually if there is any relation between x and y values and,

Correlation and Regression: The Need to Knows Correlation is a statistical technique: tells you if scores on variable X are related to scores on variable.

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.

Financial Statistics Unit 2: Modeling a Business Chapter 2.2: Linear Regression.

WARM – UP #5 1. Graph 4x – 5y = -20 What is the x-intercept? What is the y-intercept? 2. Graph y = -3x Graph x = -4.

Scatter Plots The accompanying table shows the number of applications for admissions that a college received for certain years. Create a scatter plot.

2.5 Using Linear Models A scatter plot is a graph that relates two sets of data by plotting the data as ordered pairs. You can use a scatter plot to determine.

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.

Scatterplots and Linear Regressions Unit 8. Warm – up!! As you walk in, please pick up your calculator and begin working on your warm – up! 1. Look at.

.  Relationship between two sets of data  The word Correlation is made of Co- (meaning "together"), and Relation  Correlation is Positive when the.

Chapter 5: Introductory Linear Regression

4.4 – SCATTER PLOTS AND LINES OF FIT Today’s learning goal is that students will be able to: interpret scatter plots, identify correlations between data.

Scatter Plots & Lines of Best Fit To graph and interpret pts on a scatter plot To draw & write equations of best fit lines.

GOAL: I CAN USE TECHNOLOGY TO COMPUTE AND INTERPRET THE CORRELATION COEFFICIENT OF A LINEAR FIT. (S-ID.8) Data Analysis Correlation Coefficient.

UNIT 8 Regression and Correlation. Correlation Correlation describes the relationship between two variables. EX: How much you study verse how well you.

Correlation & Linear Regression Using a TI-Nspire.

Slide Copyright © 2009 Pearson Education, Inc. Types of Distributions Rectangular Distribution J-shaped distribution.

Linear Regression, with Correlations Coefficient.

A) What is the probability for a male to want bowling to the total number of students? b) Given that a student is female, what is the probability that.

Correlation Measures the relative strength of the linear relationship between two variables Unit-less Ranges between –1 and 1 The closer to –1, the stronger.

Statistics 200 Lecture #6 Thursday, September 8, 2016

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.

Interpret Scatterplots & Use them to Make Predictions

Spearman’s Rank Correlation Test

Section 13.7 Linear Correlation and Regression

Simple Linear Regression

Correlation and Regression

Statistical Analysis Error Bars

STA 291 Summer 2008 Lecture 23 Dustin Lueker.

Linear Correlation and Regression

y = mx + b Linear Regression line of best fit REMEMBER:

Objective: Interpret Scatterplots & Use them to Make Predictions.

Correlations LO: To be able to analyse the relationship between co-variables The know the difference between correlations and experiments To be able to.

STA 291 Spring 2008 Lecture 23 Dustin Lueker.

X ⦁ X = 64 ±8 ±14 X ⦁ X ⦁ X =

Presentation transcript:

Correlation Coefficient Negative No Positive Correlation Correlation Correlation

Correlation Coefficient Negative No Positive Correlation Correlation Correlation

Correlation Coefficient Negative No Positive Correlation Correlation Correlation

Correlation Coefficient Negative No Positive Correlation Correlation Correlation

Correlation Coefficient Negative No Positive Correlation Correlation Correlation The closer r, the correlation coefficient, is to 1 or -1, the stronger the relationship. The closer r is to 0, the weaker the correlation (little to no relationship in the data) is just as strong as It just happens to be that is a strong negative correlation and is a strong positive correlation.

Interpret Correlation Coefficient “Talk about it” Given the scenario: Hypothesis: The more students study, the higher the English test scores are. Data: Results: a= b= r= x12345 y

Interpret Correlation Coefficient “Talk about it” Given the scenario: Hypothesis: The more students study, the higher the English test scores are. Data: Results: a= 6.3 b= 66.9 r= x12345 y

Interpret Correlation Coefficient “Talk about it” Given the scenario: Hypothesis: The more students study, the higher the English test scores are. Data: Results: a= 6.3 b= 66.9 r= x12345 y The equation for the line of best fit will be y = 6.3x r = which is a good correlation coefficient. It is a Positive Correlation

Interpret Correlation Coefficient “Talk about it” Given the scenario: Hypothesis: The more pounds of sand I use, the less pounds of rock I need to fill a patio. Data: Results: a= b= r= x y

Interpret Correlation Coefficient “Talk about it” Given the scenario: Hypothesis: The more pounds of sand I use, the less pounds of rock I need to fill a patio. Data: Results: a= b= r= x y

Interpret Correlation Coefficient “Talk about it” Given the scenario: Hypothesis: The more pounds of sand I use, the less pounds of rock I need to fill a patio. Data: Results: a= b= r= x y The equation for the line of best fit will be y = x r = which is a decent correlation coefficient. It is a Negative Correlation

Interpret Correlation Coefficient “Talk about it” Given the scenario: Hypothesis: The more pounds of sand I use, the less pounds of rock I need to fill a patio. Data: Results: a= b= r= x y The equation for the line of best fit will be y = x r = which is a decent correlation coefficient. It is a Negative Correlation What does the y-intercept tell us?

Interpret Correlation Coefficient “Talk about it” Given the scenario: Hypothesis: The more pounds of sand I use, the less pounds of rock I need to fill a patio. Data: Results: a= b= r= x y When x = 0, y = Since x represents the pounds of sand and y represents the pounds of rock, the point (0, 116.1) means when I use 0 pounds of sand I need pounds of rock. When x = 0, y = Since x represents the pounds of sand and y represents the pounds of rock, the point (0, 116.1) means when I use 0 pounds of sand I need pounds of rock. What does the y-intercept tell us?

Interpret Correlation Coefficient “Talk about it” Given the scenario: Hypothesis: The more pounds of sand I use, the less pounds of rock I need to fill a patio. Data: Results: a= b= r= x y When x = 0, y = Since x represents the pounds of sand and y represents the pounds of rock, the point (0, 116.1) means when I use 0 pounds of sand I need pounds of rock. When x = 0, y = Since x represents the pounds of sand and y represents the pounds of rock, the point (0, 116.1) means when I use 0 pounds of sand I need pounds of rock. What does the y-intercept tell us? (0, 116.1)

Interpret Correlation Coefficient “Talk about it” Given the scenario: Data: Results: a= b= r= x y The equation for the line of best fit will be y = x r = which is a really close to zero and therefore we can assume there is no correlation here.