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Bowling Green | M 758

#### 15 sample documents related to M 758

• Bowling Green M 758
# MATH 758 - January 26 work # there was a simple error in Thursday\'s class (if you had looked at the program I passed up, # maybe one of you could have caught this) # I didn\'t define the data vector correctly - it should have been (sum, log(product

• Bowling Green M 758
1. Simulation of a beta(5, 2) curve. (a) Write a R function to simulate n values of a beta(5, 2) random variable using an accept/reject algorithm using a uniform bounding function. One of the output arguments for the function should be the acceptance

• Bowling Green M 758
MATH 758 Computational Statistics - Homework on variance reduction 1. Suppose one observes independent random variables X 1 ,K, X p , where X i is distributed N ( i ,1) . Suppose we judge the goodness of an estimator ^ = ( ^1 ,K, ^p ) by ) 2 and you

• Bowling Green M 758

• Bowling Green M 758
* COMPUTATIONAL STATISTICS R FILES * - OPTIMIZATION - bisection.R - illustrates bisection algorithm newton1.R - illustrates newton-raphson algorithm newton1a.R - illustrates newton-raphson algorithm with stopping criteria secant.R - illustrates

• Bowling Green M 758
ab h 574 173 519 150 110 34 146 35 601 176 442 133 253 60 261 63 138 35 403 100 572 152 138 28 518 145 358 89 456 131 606 146 539 154 411 116 533 155 523 138 599 169 598 200 266 74 333 86 219 49 154 39 544 172 512 134 478 134 633 178 587 159 624 172

• Bowling Green M 758
y x 1 0.009682767 1 0.027613893 1 0.015366519 1 0.013371465 1 0.011427641 1 0.068604835 1 0.009240593 1 0.030349 1 0.031075438 0 0.007211147 1 0.069536689 1 0.05868035 1 0.02430064 1 0.017598196 1 0.0447438 1 0.081175228 1 0.001030564 1 0.026019576 1

• Bowling Green M 758
y 7.25325222659913 6.85652267046824 7.23643792894966 7.03343611519664 6.9186591609056 6.65649879051228 6.42308043084932 7.46636287619574 10.3497865413661 6.93593298389149 6.83974994639286 10.1477534866707 7.18844547660898 8.79161716373787 6.771351156

• Bowling Green M 758
y n sat 0 1 -34.2 0 1 -26.2 1 1 -14.2 0 1 22.8 1 1 21.8 1 1 16.8 1 1 12.8 1 1 49.8 1 1 -0.2 1 1 -16.2 1 1 16.8 1 1 -34.2 1 1 14.8 1 1 22.8 1 1 14.8 0 1 -88.2 1 1 35.8 0 1 -2.2 0 1 -2.2 1 1 24.8 1 1 39.8 0 1 -42.2 1 1 89.8 1 1 24.8 0 1 -96.2 1 1 31.8

• Bowling Green M 758
lastname firstname year bdate age Phillips Andy 2004 1977 27 Teixeira Mark 2004 1980 24 Mondesi Raul 2004 1971 33 Ligtenberg Kerry 2004 1971 33 Polanco Placido 2004 1975 29 Pride Curtis 2004 1968 36 Weaver Jeff 2004 1976 28 Thames Marcus 2004 1977 27

• Bowling Green M 758
- R INTRODUCTION PART I - In this session, we will get introduced to: concatenate command c() sequence command seq() replicate command rep() mathematical operations on vectors use of brackets logical values extracting elements #

• Bowling Green M 758
- R INTRODUCTION PART IV - In this session, we will meet some basic R commands for graphing data. Actually we will focus on a single command, plot, that will be your most useful tool. the plot command modifying plot elements adding elements

• Bowling Green M 758
MATH 758 HOMEWORK 1 Chapter 2 2.1, 2.2, 2.5 (a, b, c, e, h: apply the Gauss-Siedel algorithm)

• Bowling Green M 758
* COMPUTATIONAL STATISTICS R FILES * - OPTIMIZATION - bisection.R - illustrates bisection algorithm newton1.R - illustrates newton-raphson algorithm newton1a.R - illustrates newton-raphson algorithm with stopping criteria secant.R - illustrates

• Bowling Green M 758
60 3 Single Parameter Models Usage: pbetat(p0,prob,ab,data) Arguments: p0, the value of the proportion to be tested, prob, the prior probability of the hypothesis, ab, the vector of parameter values of the beta prior under the alternative hypothesi