multipleRegressionLab

# multipleRegressionLab - Recitation for week #10 (starting...

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Unformatted text preview: Recitation for week #10 (starting Sun, Apr 3) and Homework #10: Multiple Linear Regression OR&IE 3120, Spring 2011 OVERVIEW This assignment is Recitation (Computer Lab) #10 as well as Homework #10. The assignment is to complete the 6 homework problems in this handout. You will start the homework assignment during the lab but can complete it anytime before the due date, which is Wed, April 13, 2011 at noon. If you own a computer, then you can install R on it at no cost and complete the assignment at home. The lab is probably your first experience using R . Before coming to lab, I suggest that you browse the document Statistical Computing Using R in the file “ UsingR.pdf ” on Blackboard and that you bring that document to lab. You should include with your homework any R output and graphs used answer the questions. 1. CASE BACKGROUND As part of a longer manufacturing process, a factory needs to cast a large number of rectangular metallic blocks. The blocks are manufactured by using a mold consisting of the main cavity, a cup through which the molten metal is poured, and two risers for cooling (see Figure 1). The size and shape of the pouring cup and risers affect how quickly the metal can be poured into the mold, how quickly it cools, and whether it sets correctly. Figure 1: The mold consists of the main cavity, a pouring cup/sprue and two risers Objective: The factory needs to cast batches of 100 blocks of size 4.5 × 4.5 × 7 inches. 1 The current casting approach is fairly conservative — it takes a long time to pour, but the blocks always set correctly and are usable. Your goal is to achieve a significant reduction in average casting time while still ensuring that most blocks are feasible (usable). In this week’s lab, you will focus on casting time, while next week the focus will be on feasibility. This week you should ignore the variable “ Feasible ” which indicates whether a casting is usable. Variables: The following variables can be varied: Riser Height, Riser Diameter, Riser 1 Position, Riser 2 Position, Gate Diameter, Cup Height, Sprue Height, Sprue Diameter Bottom, and Sprue Diameter Top (see Figure 1). The response variable is “ BatchTime ”. Figure 2: Parameters for the mold construction Data: To obtain data on how various variable values affect pouring and cooling, a batch of 100 castings is poured with random variations in the mold variables about their baseline values. The data are available in the file castdata2011.csv . Each row contains parameter values (the inputs), and the cast batch time. The first line in the file contains the header with the names of the variables. The data start in the second row. The first row of data has the baseline values, that is, the values of the variables used in the current casting approach.....
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## This note was uploaded on 03/18/2012 for the course ORIE 3120 taught by Professor Jackson during the Spring '09 term at Cornell University (Engineering School).

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multipleRegressionLab - Recitation for week #10 (starting...

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