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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Answers to Midterm Test No. 1 1. Consider a regression model of relating Y (the dependent variable) to X (the independent variable) Yi = (0 + (1Xi+ (i where (i is the stochastic or error term. Suppose that the estimated regression equation is stated as Yi = (0 + (1Xi and ei is the residual error term. A. What is ei and define it precisely. Explain how it is related to (i. ei is the residual error term in the sample regression function and is defined as eI hat = Y
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Economics 203 Syllabus APLIAEconomic Statistics II Sections AL1‚ BL1 Fall 2013 Instructor: Office: Phone: e-mail: Office hours: Lecture hours: Lecture Section: Lecture Location: Professor Joseph A. Petry 116 David Kinley Hall 333-4260 jpetry@illinois.edu Wed 10:15 – 11:15 M/W 3:00 – 3:50 (AL1); M/W 4:00 – 4:50 (BL1) AL1‚ BL1 141 Wohlers Hall Lab Time: Lab Days: Lab Location: TA Office Hours: TA Contacts: Head TA Varies by TA section Thursday / Friday 901 W. Oregon
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Chap 13 44 1.4 100 1.3 110 1.3 110 0.8 85 1.2 105 1.2 105 1.1 120 0.9 75 1.4 80 1.1 70 1.0 105 1.1 95 A sample of 12 homes sold last week in St. Paul‚ Minnesota‚ is selected. Can we conclude that‚ as the size of the home (reported below in thousands of square feet) increases‚ the selling price (reported in $ thousands) also increases? * Compute the coefficient of correlation. * = [12(1344) – (13.8)(1160)]/12(16.26) – (13.8)2][12(114850)
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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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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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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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intervals and prediction intervals from simple linear regression The managers of an outdoor coffee stand in Coast City are examining the relationship between coffee sales and daily temperature. They have bivariate data detailing the stand ’s coffee sales (denoted by [pic]‚ in dollars) and the maximum temperature (denoted by [pic]‚ in degrees Fahrenheit) for each of [pic] randomly selected days during the past year. The least-squares regression equation computed from their data is [pic].
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