CHAPTER 13 CORRELATION AND REGRESSION ANALYSIS OUTLINE 4.1 Definition of Correlation Analysis 4.2 Scatter Diagram and Types of Relationships 4.3 Correlation Coefficient 4.4 Interpretation of Correlation Coefficient 4.5 Definition of Regression Analysis 4.6 Dependent and Independent Variables 4.7 Simple Linear Regression: Least Squares Method 4.8 Using the simple Linear Regression equation 4.9 Cautionary Notes and Limitations OBJECTIVES By the end
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95-791 Spring 2013 Lecture #8 Predictive analytics: Regression Artur Dubrawski awd@cs.cmu.edu This unit • Good-old correlation scores revisited • Locally weighted regression – As an approximator of non-linear functions – As a framework for active/purposive acquisition of data 95-791 Data Mining Lecture #8 Slide 2 Copyright © 2000-2013 Artur Dubrawski Correlational scores of association between attributes of data • • • • Linear Rank Quadratic …. Would not it be great to have an
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you cannot consult the regression R2 because (a) ln(Y) may be negative for 0 < Y < 1. (b) the TSS are not measured in the same units between the two models. (c) the slope no longer indicates the effect of a unit change of X on Y in the log-linear model. (d) the regression R2 can be greater than one in the second model. 1 (v) The exponential function (a) is the inverse of the natural logarithm function. (b) does not play an important role in modeling nonlinear regression functions in econometrics
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Introduction This presentation on Regression Analysis will relate to a simple regression model. Initially‚ the regression model and the regression equation will be explored. As well‚ there will be a brief look into estimated regression equation. This case study that will be used involves a large Chinese Food restaurant chain. Business Case In this instance‚ the restaurant chain ’s management wants to determine the best locations in which to expand their restaurant business. So far the most
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Types of regression and linear regression equation 1. The term regression was first used as a statistical concept in 1877 by Sir Francis Galton. 2. Regression determines ‘cause and effect’ relationship between variables‚ so it can aid to the decision-making process. 3. It can only indicate how or to what extent variables are associated with each other. 4. There are two types of variables used in regression analysis i.e. The known variable is called as Independent Variable and the variable which
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today that dream is out of reach for an increasing number of Americans. Why? It is because there are not nearly enough jobs for everyone. Without a jobs recovery‚ there simply is not going to be a housing recovery. In this report‚ I will perform a regression analysis to determine the effect of the Unemployment Rate (UR) on Total New Houses Sold (TNHS). I expect that there will be a negative relationship between the two variables. In other words‚ as the unemployment rate increases‚ the total number of
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Chapter 6: Multiple Linear Regression Data Mining for Business Intelligence Shmueli‚ Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Topics Explanatory vs. predictive modeling with regression Example: prices of Toyota Corollas Fitting a predictive model Assessing predictive accuracy Selecting a subset of predictors (variable selection) Explanatory Modeling Goal: Explain relationship between predictors (explanatory variables) and target Familiar use of regression in data analysis
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1 EQT 271 Engineering Statistics 2.2.2 Linear regression and correlation In linear regression‚ you should follows those instructions: 1. Choose one pair variables‚ first create the scatterplot (using Excel). Do this by simply plotting one variable as the x –axis and the other y-axis. Based on the scatterplot‚ comment on the relationship after fitting a simple curve‚ so you can be creative in pairing the variables. 2. Find the linear regression model by computing either manually or using Excel
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MATH 231: Basic Statistics Homework #5 – Correlation and Regression: 1). Bi-lo Appliance Super-Store has outlets in several large metropolitan areas in New England. The general sales manager aired a commercial for a digital camera on selected local TV stations prior ro a sale starting on Saturday and ending on Sunday. She obtained the information for Saturday-Sunday digital camera sales at the various outlets and paired it with the number of times the advertisement was shown on local TV stations
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the diet plan: The chicken food type should contribute at most 25% of the total calories intake that will result from the diet plan. The vegetable food type should provide at least 30% of the minimum daily requirements for vitamins. Provide a linear programming formulation for the above case. (No need to solve the problem.) Element | Milk | Chicken | Bread | Vegetables | Calories (X1) | 160 | 25% * 210 | 120 | 150 | Carbohydrates (X2) | 110 | 130 | 110 | 120 | Protein (X3) | 90 | 190
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