is present. The last scatter plot‚ Figure 3‚ shows household size vs. annual income. This graph shows that there is no correlation at all between these two factors. Making the factors independent of each other and viable for use in multiple regression. Frequency tables and plots of annual incomes and household size from the sample were also constructed. Figure four plots the frequency of household size. From this plot we can see that a household size of 2 is most common‚ with 30 percent
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Demand and Behavior © 2005 Prentice Hall‚ Inc. 4.1 Getting Information About C Ab t Consumer Behavior B h i Expert opinion Consumer surveys Test marketing and price experiments i t Analyses of census and other y historical data Regression analysis © 2005 Prentice Hall‚ Inc. 4.2 Managerial Rule of Thumb: Analyzing C A l i Consumer Behavior B h i Managers must consider 1. 2. 3. Whether the participating groups are truly representative of the larger population
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AND REGRESSION Introduction Correlation and Regression Scatter Plot/Diagram Coefficient of Correlation Simple Linear Regression sanizah@tmsk.uitm.edu.my Learning objectives • Explain the concept of correlation • Calculate Pearson’s correlation coefficient and interpret the results • Calculate Spearman’s rank correlation for qualitative and quantitative data and interpret the results • Determine the regression equation for a set of data and interpret the equation • Use the regression equation
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Brooks/Cole‚ Cengage Learning 2 Three Tools we will use … • Scatterplot‚ a two-dimensional graph of data values • Correlation‚ a statistic that measures the strength and direction of a linear relationship between two quantitative variables. • Regression equation‚ an equation that describes the average relationship between a quantitative response and explanatory variable. Copyright ©2011 Brooks/Cole‚ Cengage Learning 3 3.1 Looking for Patterns with Scatterplots Questions to Ask about
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August 26‚ 2012 MATH 533 Course Project Part C Professor Khago Introduction: The following report displays regression and correlation analysis for AJ Davis Department Stores data on credit balance and size. We will use the data collected from 50 credit customers to complete the following analysis; * Generate a scatterplot for CREDIT BALANCE vs. SIZE‚ including the graph of the "best fit" line. Interpret. * Determine the equation of the "best fit" line‚ which describes the relationship
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your Part II Final Project assignment. When constructing a multiple regression model‚ we must ask the question “Does a linear relationship exist between the dependent variable and the independent variables. Based on the multiple regression model of the Virginia hospitals data‚ all of the independent variables are not significant predictors of the dependent variable. This is evidenced by the varying p-values shown in the regression analysis. The null hypothesis for each independent variable states
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BASIC ECONOMETRICS FOURTH EDITION Damodar N. Gujarati United States Military Academy‚ West Point Boston Burr Ridge‚ IL Dubuque‚ IA Madison‚ WI New York San Francisco St. Louis Bangkok Bogota Caracas Kuala Lumpur Lisbon London Madrid Mexico City Milan Montreal New Delhi Santiago Seoul Singapore Sydney Taipei Toronto McGraw-Hill Higher Education A Division of The McGraw-Hill Companies ’EZ BASIC ECONOMETRICS Published by McGraw-HiII/lrwin‚ a business unit of The McGraw-Hili Companies
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Chapter 12 Simple Linear Regression Case Problem 1: Measuring Stock Market Risk a. Selected descriptive statistics follow: Variable N Mean StDev Minimum Median Maximum Microsoft 36 0.00503 0.04537 -0.08201 0.00400 0.08883 Exxon Mobil 36 0.01664 0.05534 -0.11646 0.01279 0.23217 Caterpillar 36 0.03010 0.06860 -0.10060 0.04080 0.21850 Johnson & Johnson 36 0.00530 0.03487 -0.05917 -0.00148 0.10334
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scale independent: between -1 and 1‚ close to 1 is upward‚ 0 is central‚ -1 is downward sloping. Finding the regression Regression formula with one regressor Slope Intercept Finding R2 TSS=ESS+SSR The Coefficient of Determination = R2 This gives the total fit of ‚ between 0 (chance) and 1 (perfect prediction) Standard Errors Standard Error of the Regression Standard error of Hypothesis Testing 1. 2. Define H0 3. Define H1 4. Define Tcrit/Pcrit
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to examine the factors that influence the Consumer Price Index. We observe four variables‚ namely‚ money supply‚ gross domestic product‚ interest rate‚ and share price. By utilizing quarterly data from 1996 to 2008‚ this study applies multiple regressions method to find the best model and factors which can explain Consumer Price Index. The result indicates gross domestic product‚ interest rate‚ and stock price significant effect to consumer price index‚ whereas money supply does not have significant
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