Problems on Regression and Correlation Prepared by: Dr. Elias Dabeet Q1. Dr. Green (a pediatrician) wanted to test if there is a correlation between the number of meals consumed by a child per day (X) and the child weight (Y). Included you will find a table containing the information on 5 of the children. Use the table to answer the following: Child Number of meals consumed per day (X) child weight (Y) X² Y² XY Ahmad 11 8 121 64 88 Ali 16 11 256 121 176 Osama 12 9 144
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0 Model Specification 6 3.1 Linear Regression Model. 6 3.2 The Regression Specification Error Test 8 3.3 Non-linear models 9 3.4 Autocorrelation. 10 3.5 Heteroskedasticity Test 10 4.0 Hypothesis Testing 11 5.0 Binary (Dummy) Variables 11 6.0 Conclusion 13 Reference List 13 1.0 Introduction and Motivation Crude oil is one of the world’s most important natural resources. Over the past six decades or so‚ crude oil – because of the products derived from it‚ has become highly indispensable
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Applied Linear Regression Notes set 1 Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa‚ AL 35487-0348 Phone: (205) 348-4431 Fax: (205) 348-8648 September 26‚ 2006 Textbook references refer to Cohen‚ Cohen‚ West‚ & Aiken’s (2003) Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. I would like to thank Angie Maitner and Anne-Marie Leistico for comments made on earlier versions of these notes. If you
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and the number of construction permits issued at present. Example 2: The demand for new house or automobile is very much affected by the interest rates changed by banks. Regression analysis is one such causal method. It is not limited to locating the straight line of best fit. Types:- 1. Simple (or Bivariate) Regression Analysis: Deals with a Single independent variable that determines the value of a dependent variable. Ft+1 = f (x) t Where Ft+1: the forecast for the next period. This
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Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc‚ Inc./Getty Images A random sample of eight drivers insured with a company and having similar auto insurance policies was selected. The following table lists their driving experiences (in years) and monthly auto insurance premiums. Driving Experience (years) Monthly Auto Insurance Premium 5 2 12 9
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Heat Equation from Partial Differential Equations An Introduction (Strauss) These notes were written based on a number of courses I taught over the years in the U.S.‚ Greece and the U.K. They form the core material for an undergraduate course on Markov chains in discrete time. There are‚ of course‚ dozens of good books on the topic. The only new thing here is that I give emphasis to probabilistic methods as soon as possible. Also‚ I introduce stationarity before even talking about state classification
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8.1 Solutions 37 37 43 43 47 49 49 57 58 60 62 65 66 67 69 8 Appendix A R – A software tool for statistics Appendix B Introduction to MATLAB B.1 Working with vectors and matrices in the MATLAB environment B.2 MATLAB graphics B.3 Solving equations and computing integrals B.4 Statistics in MATLAB B.5 Using MATLAB to solve linear and quadratic programming problems Appendix C Introduction to AMPL C.1 Running optimization models in AMPL C.2 Mean-variance efficient portfolios in AMPL C.3 The knapsack
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REGRESSION ANALYSIS Correlation only indicates the degree and direction of relationship between two variables. It does not‚ necessarily connote a cause-effect relationship. Even when there are grounds to believe the causal relationship exits‚ correlation does not tell us which variable is the cause and which‚ the effect. For example‚ the demand for a commodity and its price will generally be found to be correlated‚ but the question whether demand depends on price or vice-versa; will not be answered
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Simple Linear Regression in SPSS 1. STAT 314 Ten Corvettes between 1 and 6 years old were randomly selected from last year’s sales records in Virginia Beach‚ Virginia. The following data were obtained‚ where x denotes age‚ in years‚ and y denotes sales price‚ in hundreds of dollars. x y a. b. c. d. e. f. g. h. i. j. k. l. m. 6 125 6 115 6 130 4 160 2 219 5 150 4 190 5 163 1 260 2 260 Graph the data in a scatterplot to determine if there is a possible linear relationship. Compute and interpret
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Topic 8: Multiple Regression Answer a. Scatterplot 120 Game Attendance 100 80 60 40 20 0 0 5‚000 10‚000 15‚000 20‚000 25‚000 Team Win/Loss % There appears to be a positive linear relationship between team win/loss percentage and game attendance. There appears to be a positive linear relationship between opponent win/loss percentage and game attendance. There appears to be a positive linear relationship between games played and game attendance. There does not appear to be any relationship
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