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# Chp1 - Linear Regression with One Predictor Variable...

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Linear Regression with One Predictor Variable Relation between variables Regression models Simple linear regression Statistical estimation and inference for linear regression models 1 Relations between Variables Functional relation: deterministic, Y = f ( X ), such as Area = π × Radius 2 Hight in meters = 0 . 304 Hight in feet Statistical relation: Variables tend to vary together but not de- terministic, such as Heights of father and children Toluca Company data in Table 1.1 2

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Toluca Company Example The Toluca Company manufactures refrigeration equipment as well as many replacement parts. In a cost improvement program, company oﬃcials wished to determine the optimum lot size for producing this part. To determine this relationship, data on lot size and work hours for 25 recent production runs were utilized. The production conditions were stable during the six-month period in which the 25 runs were made and were expected to continue to be the same during the next three years, the planning period for which the cost improvement program was being conducted. 3 Toluca Company Example: Contd. 25 observations LotSize 80 30 50 90 70 60 120 80 100 50 40 70 WorkHours 399 121 221 376 361 224 546 352 353 157 160 252 LotSize 90 20 110 100 30 50 90 110 30 90 40 80 70 WorkHours 389 113 435 420 212 268 377 421 273 468 244 342 323 Read the data into R and do scatterplot toluca = read.table("http://www.stat.columbia.edu/~jwang/ w4315/data/toluca.txt", header=TRUE) plot(toluca, pch=20) 4
Toluca Company Example: Contd.

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Chp1 - Linear Regression with One Predictor Variable...

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