2 Excel* MegaStat Minitab SPSS JMP POM* *We will focus on this readily available software in the demonstrations to follow Statistical Software.

Slides:



Advertisements
Similar presentations
Here we add more independent variables to the regression.
Advertisements

Guide to Using Minitab 14 For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 8th Ed. Chapter 15: Multiple.
Correlation and regression
2 Excel* MegaStat Minitab SPSS JMP POM *We will focus on this readily available software in the demonstrations to follow Statistical Software.
The World’s Fastest Crash Course in Statistics Or, What You Need to Know to Answer Your Research Question 13 November 2006.
LSP 120: Quantitative Reasoning and Technological Literacy Section 118 Özlem Elgün.
© Copyright 2000, Julia Hartman 1 An Interactive Tutorial for SPSS 10.0 for Windows © by Julia Hartman Multiple Linear Regression Next.
Linear Regression In Excel. Linear Regression  In this presentation you will learn the following: How to make a scatter plot in Excel (Click Here)Here.
Statistics 350 Lecture 1. Today Course outline Stuff Section
Business Statistics - QBM117 Least squares regression.
Correlation and Regression. Relationships between variables Example: Suppose that you notice that the more you study for an exam, the better your score.
Regression multiple Dan Fisher Marriott School of Management Brigham Young University November 2005 linear.
Example 16.3 Estimating Total Cost for Several Products.
Correlation Question 1 This question asks you to use the Pearson correlation coefficient to measure the association between [educ4] and [empstat]. However,
Regression Basics For Business Analysis If you've ever wondered how two or more things relate to each other, or if you've ever had your boss ask you to.
Linear Regression Modeling with Data. The BIG Question Did you prepare for today? If you did, mark yes and estimate the amount of time you spent preparing.
McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 13 Linear Regression and Correlation.
DISCLAIMER This guide is meant to walk you through the physical process of graphing and regression in Excel…. not to describe when and why you might want.
Forecasting using trend analysis
1 Doing Statistics for Business Doing Statistics for Business Data, Inference, and Decision Making Marilyn K. Pelosi Theresa M. Sandifer Chapter 11 Regression.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Simple Linear Regression Analysis Chapter 13.
2 For Exponential Smoothing we will use the Excel Spreadsheet in this module. I created the spreadsheet so there is no copyright; do as you please Statistical.
Relationships between Variables. Two variables are related if they move together in some way Relationship between two variables can be strong, weak or.
Chapter 14 – Correlation and Simple Regression Math 22 Introductory Statistics.
Prior Knowledge Linear and non linear relationships x and y coordinates Linear graphs are straight line graphs Non-linear graphs do not have a straight.
Linear Trend Lines = b 0 + b 1 X t Where is the dependent variable being forecasted X t is the independent variable being used to explain Y. In Linear.
Statistics for the Social Sciences Psychology 340 Fall 2013 Correlation and Regression.
GrowingKnowing.com © Correlation and Regression Correlation shows relationships between variables. This is important. All professionals want to.
Then click the box for Normal probability plot. In the box labeled Standardized Residual Plots, first click the checkbox for Histogram, Multiple Linear.
CHAPTER 3 INTRODUCTORY LINEAR REGRESSION. Introduction  Linear regression is a study on the linear relationship between two variables. This is done by.
Linear Trend Lines = b 0 + b 1 X t Where is the dependent variable being forecasted X t is the independent variable being used to explain Y. In Linear.
Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,
Created by Erin Hodgess, Houston, Texas
The McGraw-Hill Companies, Inc. 2006McGraw-Hill/Irwin DSS-ESTIMATING COSTS.
Chapter 11 Correlation and Simple Linear Regression Statistics for Business (Econ) 1.
Chapter 6 Simple Regression Introduction Fundamental questions – Is there a relationship between two random variables and how strong is it? – Can.
Click to edit Master title style Midterm 3 Wednesday, June 10, 1:10pm.
PS 225 Lecture 17 Correlation Line Review. Scatterplot (Scattergram)  X: Independent Variable  Y: Dependent Variable  Plot X,Y Pairs Length (in)Weight.
Warm-Up Write the equation of each line. A B (1,2) and (-3, 7)
Correlation The apparent relation between two variables.
Financial Statistics Unit 2: Modeling a Business Chapter 2.2: Linear Regression.
Chapter 9: Correlation and Regression Analysis. Correlation Correlation is a numerical way to measure the strength and direction of a linear association.
Scatter Plots, Correlation and Linear Regression.
STA302: Regression Analysis. Statistics Objective: To draw reasonable conclusions from noisy numerical data Entry point: Study relationships between variables.
1 Simple Linear Regression and Correlation Least Squares Method The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES.
Simple Linear Regression The Coefficients of Correlation and Determination Two Quantitative Variables x variable – independent variable or explanatory.
STA302: Regression Analysis. Statistics Objective: To draw reasonable conclusions from noisy numerical data Entry point: Study relationships between variables.
Chapter 5 Lesson 5.2 Summarizing Bivariate Data 5.2: LSRL.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
Correlation and Regression Stats. T-Test Recap T Test is used to compare two categories of data – Ex. Size of finch beaks on Baltra island vs. Isabela.
Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,
Introduction Many problems in Engineering, Management, Health Sciences and other Sciences involve exploring the relationships between two or more variables.
An Interactive Tutorial for SPSS 10.0 for Windows©
*Bring Money for Yearbook!
REGRESSION (R2).
ENM 310 Design of Experiments and Regression Analysis
Multiple Regression.
CHAPTER 10 Correlation and Regression (Objectives)
Mathematical Modeling
2. Find the equation of line of regression
2-7 Curve Fitting with Linear Models Holt Algebra 2.
Mathematical Modeling Making Predictions with Data
STA 282 – Regression Analysis
Mathematical Modeling Making Predictions with Data
STEM Fair Graphs.
Simple Linear Regression
Section 1.4 Curve Fitting with Linear Models
Correlation & Trend Lines
Correlation and Simple Linear Regression
Correlation and Simple Linear Regression
Presentation transcript:

2 Excel* MegaStat Minitab SPSS JMP POM* *We will focus on this readily available software in the demonstrations to follow Statistical Software

3 Regression lines are typically used when you want to predict possible future values for variables Regressions can be both linear and non-linear; we will focus on linear You can have an n-dimensional regression line, which means you can have several independent variables effecting the dependent variable When do I need a Regression Analysis?

4 Let’s look at an example: Suppose you collect data on Domestic Revenue in Freight Ton Miles for Non Scheduled Cargo flights. This data is available on the FAA webpage: You can easily download this data in an Excel Format from this website. Simple Linear Regression Two variables: One independent (x) and one dependent (y) y=β 1 x+β 0 Β 1 is called the slope and β 0 is called the y-intercept

5 Simple Linear Regression Question: Is there a relationship between month and Non-Scheduled Freight Tons in miles? Can we make a decent prediction for what to expect in 2014?

6 Simple Linear Regression Take a look at the data in the attached Excel File under the data Tab. Be sure to ask questions as to which data we should get rid of, and why.

7 Simple Linear Regression Which data did you think to eliminate? Note that 9-11 had an anomaly effect, and thus it would not be ideal to include this data in our results. Thus we will modify the Data and store this in the tab labeled Data Modified. Be sure to report this in your research as it highlights your analytical thinking capabilities!!!

8 Simple Linear Regression Note I chose to start with November 2002 data; this is up to you. The most important thing is to include reasoning for your choice in your analysis write-up.

9 Simple Linear Regression Let’s perform the analysis using Excel’s Data Analysis tab. I like to rename my data 1, 2, etc., so be sure to note November 2002 =1. If you need to download Excel’s Data Analysis package, see here: help/load-the-analysis-toolpak- HP aspx help/load-the-analysis-toolpak- HP aspx

10 Simple Linear Regression Note I chose to start with November 2002 data; this is up to you. The most important thing is to include reasoning for your choice in your analysis write-up.

11 Simple Linear Regression: Data Analysis Step 1: Go to the data tab Step 2: Select Regression Step 3: Click on the square next to y-data and highlight all the data for your dependent variable Step 4: Do the same for the x variable. If you were running a multiple regression with more than one x, you would highlight all x data columns. Step 5: Click okay (or make minor changes as you see fit for output, alpha, etc.)

12 Simple Linear Regression-Conclusions So what do you findings in Excel tell you? See Excel Sheet named Results. 1. The equation of the line is y= x (under coefficients) We can see the slope is negative trending, but does not look very strong This is supported by the weak R Squared of Note that the data tends to be trending up in the more recent years

13 Simple Linear Regression-Conclusions As an analyst, I would NOT suggest correlating these two variables using the data given. Perhaps focus on more recent data Look for further factors Reporting what not to do is just as important as noting a success in many cases