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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The relationship between an object’s position and time if it travels in a straight line at a constant speed‚ as well as the relationship between the same object’s speed and time‚ will both result in a linear model. This hypothesis will be tested by using a model car‚ a spark timer‚ and timer tape. Procedure 1.Obtain a model car‚ spark timer‚ half a meter of timer tape. 2.Tape the length of timer tape to the back of the model car. 3.Feed the timer tape through the spark timer‚ setting the
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Exam 2 Preparation Guidelines February 24‚ 2013 Exam 2 is scheduled for Tuesday 3/5. Coverage will include material identified on the syllabus through 2/28. The exam will be comprised of two parts: (1) multiple choice questions (and possibly a few true/false)‚ (2) short answers. The weight between parts I and II will likely be in the neighborhood of 50-50. I’m guessing that 80% or more of the exam points will based on content since exam 1 (i.e.‚ content in chapters 8‚ 10‚ and 11). The exam is closed
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analysis before attempting to use structural modeling. Major applications of structural equation modeling include: 1. causal modeling‚ or path analysis‚ which hypothesizes causal relationships among variables and tests the causal models with a linear equation system. Causal models can involve either manifest variables‚ latent variables‚ or both; 2. confirmatory factor analysis‚ an extension of factor analysis in which specific hypotheses about the structure of the factor loadings and intercorrelations
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variables are in the file “CHARITY.DES”. To examine what influence individuals’ decisions on donations‚ the linear regression model respond = β0 + β1 resplast + β2 avggift + β3 propresp + β4 mailsyear +u is used. For hypothesis-testing questions‚ please always present hypotheses‚ test statistic and its distribution under the null‚ decision rule and conclusion. a) Why the model is known as a linear probability model (LPM)? What is the meaning of β1? b) Suppose that MLR.1-4 hold for the model when
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support in completing this project. TABLE OF CONTENTS 1. Linear Programming ……........…………………………….................................4 2. Transportation Problem ……………………………............................................5 3. Case Study………………………………………..................................................8 4. Other Methods of solving transportation problem..................................................11 LINEAR PROGRAMMING Linear programming is a mathematical method for determining a way to
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collection of these treasures can be seen at the National Archaeological Musuem in Athens. The Mycenaean were also literate and wrote in a script known as Linear B. This script is an early form of Greek which is unrelated from Linear A from the Minoan Civilisation of Crete. It has however been deciphered. Other examples of the script Linear B have also been found on Crete‚ which has led to the possibility that the island may have been invaded by the Mycenaean people at around 1500 BC. At around
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16 15 13 16 14 6 20 100 MARKS RECEIVED 1 Question 1 Graphical solution (16 marks) For a linear programming model given below: Decision variables x1 Units of product 1 to produce. x2 – Units of product 2 to produce. Objective function Maximize 4.0x1 + 3.6x2 Constraints Constraint 1: 11x1 + 5x2 > 55 Constraint 2: 3x1 + 4x2 < 36 Constraint 3: 4x1 – 9x2 < 0 Nonnegativity: x1‚ x2 >= 0 Solve this linear programming model by using the graphical approach (Graph paper is provided on the next page)
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CHAPTER 8 FORECASTING AND DEMAND PLANNING Have you ever gone to a restaurant and been told that they are sold out of their “special‚” or gone to the university bookstore and found that the texts for your course are on backorder? Have you ever had a party at your home only to realize that you don’t have enough food for everyone invited? Just like getting caught unprepared in the rain‚ these situations show
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expedite their bill payments will help our business and our customer’s personal and internal finances. To validate the relationship between the amount of a bill and the number of days late it is for both commercial and residential accounts‚ we apply a linear regression method to generate an accurate statistical analysis of the data. By using this form of analysis‚ we will be able to answer the following questions with the information provided. * Does the size of the bill somehow relate to the number
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