By: Makenzie Quinn, Jamie Rappleye, Joel Reed, Bradley Richins, Heather Roberts and Coty Rohan.

Slides:



Advertisements
Similar presentations
Inference for Linear Regression (C27 BVD). * If we believe two variables may have a linear relationship, we may find a linear regression line to model.
Advertisements

FACTOR THE FOLLOWING: Opener. 2-5 Scatter Plots and Lines of Regression 1. Bivariate Data – data with two variables 2. Scatter Plot – graph of bivariate.
Total Population of Age (Years) of People that Smoke
Fitting a Function to Data Adapted from Walch Education.
Data enter key Comma r “correlation coefficient” ALPHA Activates “green” statistic functions ALPHA Activates “green” statistic functions Mean Standard.
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 ________________________.
Researchers, such as anthropologists, are often interested in how two measurements are related. The statistical study of the relationship between variables.
First Quantitative Variable: Ear Length  The unit of measurement for this variable is INCHES.  A few possible values for this first quantitative variable.
2-5 Using Linear Models Make predictions by writing linear equations that model real-world data.
6.1 What is Statistics? Definition: Statistics – science of collecting, analyzing, and interpreting data in such a way that the conclusions can be objectively.
STAT E100 Section Week 3 - Regression. Review  Descriptive Statistics versus Hypothesis Testing  Outliers  Sample vs. Population  Residual Plots.
Presentation Design & Purpose of the StudyKaren Foster Study DesignJulianne Ehlers DataChristopher Gleason Statistics, & GraphsKaren Foster Difficulties.
Group participation McKie Delahunty- created slides 1,4,5,6,7,8,11,12 & compiled all for powerpoint Jenica Hansen- created slides 2,3 Semhar Moges- created.
Wednesday, May 13, 2015 Report at 11:30 to Prairieview.
© 2010 Pearson Prentice Hall. All rights reserved. CHAPTER 12 Statistics.
Statistical Methods Statistical Methods Descriptive Inferential
Chapter 21 Basic Statistics.
Regression Regression relationship = trend + scatter
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.
Total Population of Age (Years) of People. Pie Chart of Males and Females that Smoke Systematic Gender Sample Total Population: 32.
I was on the second day of a road trip when I decided to keep a record of how far I had traveled from home. The table below shows how many hours I drove.
Chelsie Guild, Taylor Larsen, Mary Magee, David Smith, Curtis Wilcox TERM PROJECT- VISUAL PRESENTATION.
Scatter Diagrams Objective: Draw and interpret scatter diagrams. Distinguish between linear and nonlinear relations. Use a graphing utility to find the.
Correlation and Regression. Section 9.1  Correlation is a relationship between 2 variables.  Data is often represented by ordered pairs (x, y) and.
Statistics Bivariate Analysis By: Student 1, 2, 3 Minutes Exercised Per Day vs. Weighted GPA.
Transformations.  Although linear regression might produce a ‘good’ fit (high r value) to a set of data, the data set may still be non-linear. To remove.
Creating a Residual Plot and Investigating the Correlation Coefficient.
Correlation and Regression. fourth lecture We will learn in this lecture: Correlation and Regression 1- Linear Correlation Coefficient of Pearson 2- Simple.
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,
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.
9.1B – Computing the Correlation Coefficient by Hand
Algebra 3 Lesson 1.9 Objective: SSBAT identify positive, negative or no correlation. SSBAT calculate the line of best fit using a graphing calculator.
Group 4 Members and Participants Amelia Corey, Angie Coates, Cynthia Bradwisch, Aaron Grow, and Daniel Champion.
Review #2.
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,
Statistics Group 1 Elisabeth Brino Jamie Derbidge Slade Litten Kristen Kidder Jamie.
STATISTICS 1040 TERM PROJECT SPRING THE QUESTION Is a student’s Grade Point Average (GPA) correlated with their age?
Is their a correlation between GPA and number of hours worked? By: Excellent Student #1 Excellent Student #2 Excellent Student #3.
For adult men, is the amount of money spent per week on fast food related to body weight? By: Chad Vigil, Jeannette Watson, Jason Williams, Amanda Webster,
Materials needed: journal, pencil, calculator and homework.
.  Relationship between two sets of data  The word Correlation is made of Co- (meaning "together"), and Relation  Correlation is Positive when the.
Day 102 – Linear Regression Learning Targets: Students can represent data on a scatter plot, and describe how the variables are related and fit a linear.
Linear Regression What kind of correlation would the following scatter plots have? Negative Correlation Positive Correlation No Correlation.
Algebra 2 Notes April 23, ) 2.) 4.) 5.) 13.) domain: all real #s 14.) domain: all real #s 16.) domain: all real #s 17.) domain: all real #s 22.)
Unit 3 Section : Regression  Regression – statistical method used to describe the nature of the relationship between variables.  Positive.
MATHS CORE AND OPTIONAL ASSESSMENT STANDARDS Found in the Subject Assessment Guidelines (SAG) Dated January 2008.
Carli Young Annaliese Prusse Stephanie Harper Hannah Minkus Patrica Molyneux Is Birth weight Related To Gestational Age?
P REVIEW TO 6.7: G RAPHS OF P OLYNOMIAL. Identify the leading coefficient, degree, and end behavior. Example 1: Determining End Behavior of Polynomial.
REGRESSION MODELS OF BEST FIT Assess the fit of a function model for bivariate (2 variables) data by plotting and analyzing residuals.
Scatter Plots & Lines of Best Fit To graph and interpret pts on a scatter plot To draw & write equations of best fit lines.
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.
1.6 Modeling Real-World Data with Linear Functions Objectives Draw and analyze scatter plots. Write a predication equation and draw best-fit lines. Use.
The Data Collection and Statistical Analysis in IB Biology John Gasparini The Munich International School Part VI – A Statistical Test Flow Chart.
Group 13 Wingspan vs. Height
FOR TEEN AND YOUNG ADULT MALES (13 TO 29) IS AGE RELATED TO THE NUMBER OF HOURS SPENT PLAYING VIDEO/COMPUTER GAMES? By Amanda Webster, Jennifer Burgoyne,
Elementary Statistics
EXPLORATORY DATA ANALYSIS and DESCRIPTIVE STATISTICS
Aim: How do we fit a regression line on a scatter plot?
2-7 Curve Fitting with Linear Models Holt Algebra 2.
Correlation and Regression
Section 10.2: Fitting a Linear Model to Data
Unit 2 Quantitative Interpretation of Correlation
Arms vs. Feet Group 4 Members and Participants Amelia Corey, Angie Coates, Cynthia Bradwisch, Aaron Grow, and Daniel Champion.
LEARNING GOALS FOR LESSON 2.7
Monday, March 10th Warm Up To find the residual you take the ACTUAL data and ______________ the PREDICTED data. If the residual plot creates a pattern.
Regression and Correlation of Data
Presentation transcript:

By: Makenzie Quinn, Jamie Rappleye, Joel Reed, Bradley Richins, Heather Roberts and Coty Rohan

Purpose? Is there a correlation between a man’s pinky size and their ear size?

How to accomplish our purpose? Find 50 random individual men Aged between 20 to 40 Use the three segments of the finger The highest point of the ear lobe and the lowest point of the ear lobe

Our Measurements

Our Calculations

Pinky Size Charts For the Histogram the y-value is frequency and the x-value is the pinky size

Ear Size Charts For the Histogram the y-value is frequency and the x-value is the ear size

Scatter Plot For the y- value it is the pinky size. For the x-value it is the ear size

Equations for our Linear correlation coefficient and Line of regression Linear correlation coefficient, r= Line of regression: y= x

Difficulties Communicating between group members Getting everyone to reply Time consuming

Analysis There is no correlation between men’s pinky size and their ear size The distribution were both skewed to the right

Interpretation and Conclusion We have decided that there is no correlation between ear size and pinky size.