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###### Note07

School: University Of Florida

Course: Engineering Statistics

Section 2.4: Random Variables Random Variable a mapping from the sample space to the real line It assigns a numerical value to each outcome in a sample space. It is customary to denote random variables with capital letters: X, Y etc. Use lowercas

• 5 Pages
###### Quiz2

School: University Of Florida

Course: Engineering Statistics

STA3032 Section 7347 Quiz 2 Solution 1-2. An appliance dealer sells three different models of upright freezers having 13.5, 15.9 and 19.1 cubic feet of storage space respectively. Let X = the amount of storage space purchased by the next customer to

• 5 Pages
###### Note08

School: University Of Florida

Course: Engineering Statistics

Chapter 4 Section 2: The Binomial Distribution Terminology n! = n (n - 1) (n - 2) 1 (n factorial) e.g.) a) 5! b) 5 3 n x = n! x!(n-x)! (n choose x) Note: 0! = 1, 1! = 1 Conditions on a Binomial Experiment Experiment consists of n ident

• 1 Page

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 2 James Keesling 1 Numerical Dierentiation Problem 1.1. Determine a formula to estimate the third derivative of a function f (x) at the point x0 using the points cfw_x0 3h, x0 2h, x0 h, x0 , x0 + h, x0 + 2h, x0 + 3h. Problem 1.2. In Problem

• 1 Page

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 1 James Keesling 1 Bisection Method Problem 1.1. Determine a solution to cos(x) = x using the Bisection Method. Problem 1.2. Determine the real roots of the polynomial p(x) = x10 25 x5 + 10 x 15 using the Bisection Method. 2 Newtons Method P

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

Name MAD 4401, Numerical Analysis Keesling Test 2 Due 10/21/11 Do all problems. Do all work in the space provided. Each problem is worth 10 points. Due at the beginning of class on Friday, Oct 21, 2011. 1. Assume a means of generating random numbers that

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

Name MAD 4401, Numerical Analysis Keesling Test 1 9/21/11 Do all problems. Show your work and explain your answers. Each problem is worth 20 points. 1. 1 dx using Romberg Integration. Use up to 32 4 1 + x 2 subdivisions in the Composite Trapezoidal Rule.

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 12 1. Solve the equation x 2 2 = 0 using Newtons Method. 2. For which x in the real line will the above equation converge? For each x for which the Newton Method converges, what value does it converge to? 3. Solve the equation cos( x ) = x u

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 11 1. Let f ( x ) be continuous and suppose that the sequence x0 , f ( x0 ), f ( f ( x0 ), f ( f ( f ( x0 ), converges to z. Show that f ( z ) = z . This sequence can also be defined as cfw_ xn n=0 where x0 is arbitrary and xn+1 = f ( xn ) f

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 10 1. Do three iterations of the bisection method in solving the following equations. (a) sin x = cos x (b) x 5 + 3x 4 + 2 x 5 = 0 2. From the intervals that you started with in the previous problem, how many steps would be necessary to have

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 9 1. Determine a formula for estimating the derivative of f ( x ) at the point x0 using the following points. (a) cfw_ x0 2 h, h, x0 , x0 + h, x0 + 2 h (b) cfw_ x0 3h, x0 2 h, x0 h, x0 , x0 + h, x0 + 2 h, x0 + 3h 2. What is the optimal h to

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 8 1. Determine a formula for estimating the derivative of f ( x ) at the point x0 using the following points. (a) cfw_ x0 h, x0 + h (b) cfw_ x0 h, x0 + 2 h (c) cfw_ x0 2 h, x0 , x0 + h, x0 + 3h 2. What is the optimal h to use in each of the

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 7 Name 1. Give a formula for generating random numbers having the probabilty density function e t assuming that a random number generated is available giving numbers from the uniform distribution on [ 0,1] . Denote the numbers from this dist

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 6 Name 1. Use the Monte-Carlo method to estimate 0 sin( x ) dx . (a) Write a formula for the method using 100 points? Use rand() to represent a random number selected from the uniform distribution on [ 0, 1] . (b) What is the mean of the est

• 1 Page

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 3 James Keesling 1 Gaussian Quadrature Problem 1.1. Compute the following integrals using Gaussian Quadrature with eight points. (a) sin(x) dx 0 1 x15 dx (b) 0 4 (c) 4 1 dx 1 + x2 Problem 1.2. Give an example of a function on the interval [0

• 1 Page

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 4 James Keesling 1 Linear Ordinary Dierential Equations Problem 1.1. Solve the following systems of ordinary dierential equations using matrix methods. d2 x = 4x dt2 d2 x dx 3 + 2x = 0 dt2 dt 2 Matrix Norms and Condition Number Problem 2.1.

• 2 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 5 James Keesling 1 Linear Ordinary Dierential Equations Problem 1.1. Let dx =Ax dt be a dierential equation where x1 (t) x2 (t) x(t) = . . . xn (t) is the solution being sought and A is an n n matrix with constant coecients. Prove that th

• 2 Pages
###### Oct28_examples

School: University Of Florida

Course: Engineering Statistics

October 28th Examples (10.20) A manufacturer of resistors claims that 10% fail to meet the established tolerance limits. A random sample of resistance measurements for 60 such resistors reveals eight to lie outside the tolerance limits. Is there sucient e

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###### Oct26_examples

School: University Of Florida

Course: Engineering Statistics

October 26th Examples 10.13 The output voltage for a certain electric circuit is specied to be 130. A sample of 40 independent readings on the voltage for this circuit gave a sample mean of 128.6 and a standard deviation of 2.1. Test the hypothesis that t

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###### Exam1_GforR

School: University Of Florida

Course: Engineering Statistics

Sheet1 UFID_2nd4, SCORE xxxx,65 9159,35 1190,75 5776,90 5980,65 9066,80 1134,65 4968,85 3678,70 4659,65 3730,45 1309,65 4851,60 2917,65 5861,95 8851100 8074,65 1659100 4312,90 1360,80 xxxx,65 1471,90 6043,85 8192,40 3317,60 9199,80 9453,60 3651,75 1181,95

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

Quiz 1 Grades Min. 1st Qu. 0.0 STA 3032 Median 13.0 17.0 Mean 3rd Qu. Max. 15.6 20.0 20.0 10 30 5 20 0 10 0 Frequency 40 15 50 20 Histogram of Grades 0 5 10 Grades 15 20

• 3 Pages
###### Normal

School: University Of Florida

Course: Engineering Statistics

Standard Normal Cumulative Probability Table Cumulative probabilities for POSITIVE z-values are shown in the following table: z 0.0 0.1 0.2 0.3 0.4 0.00 0.5000 0.5398 0.5793 0.6179 0.6554 0.01 0.5040 0.5438 0.5832 0.6217 0.6591 0.02 0.5080 0.5478 0.5871 0

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Review for Test 3 James Keesling April 10, 2009 1 Linear Ordinary Dierential Equations Problem 1.1. Let A be an n n matrix. Dene exp(tA). Problem 1.2. Let A= 12 10 and compute exp(tA). Problem 1.3. Let x1 (t) x2 (t) x(t) = . . . xn (t) and sup

• 2 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Review for Test 2 James Keesling March 4, 2009 For each of the problems below, be sure that you get the correct answers and can explain the theory behind the calculations. 1 Theory of Dierential Equations Problem 1.1. Sketch a proof that dx = f (

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Review for Test 1 James Keesling February 9, 2009 1 Bisection Method Problem 1.1. State the Intermediate Value Theorem. Problem 1.2. Using the Intermediate ValueTheorem, explain how the Bisection Method works. Problem 1.3. Determine a solution to

• 3 Pages
###### TI-89Workbook

School: University Of Florida

Course: Engineering Statistics

TI-89 WORKBOOK for HONORS CALCULUS by Professor James Keesling Department of Mathematics University of Florida Table of Contents Introduction 1. Elementary Calculations. 2. Functions and Graphs. 3. Limits. 4. The Derivative and Tangent Lines. 5. Applicati

• 2 Pages
###### Problem2.6

School: University Of Florida

Course: Engineering Statistics

Convergence to the 2 Using Newtons Method James Keesling Theorem 0.1. Let f (x) = x2 2 = 0 and suppose that x0 > 0. Let g (x) = x x2 2 2x be the Newton function for f (x). Let x0 > 0 be any positive number. Then cfw_g n (x0 ) n=1 converges to 2. Proof. S

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• ###### Solution to Problem 20 in Chap 10
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###### Solution To Problem 20 In Chap 10

School: University Of Florida

Course: Engineering Statistics

Solution to Problem 10.20 Problem 10.20: A manufacturer of resistors claims that 10% fail to meet the established tolerance limits. A random sample of resistance measurements for 60 such resistors revel eight to lie outside the tolerance limits. Is there

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 5 1. Determine the Newton-Cotes coefficients for the following n. Note that there are n +1 points and n + 1 coefficients for each of these n. n =1 n=2 n=3 n=4 n=5 n=6

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 3 1. Suppose that p( x ) is a polynomial of degree n or less. Show that b a n p( x ) dx = ai p( xi ) i=0 where the right hand side of the equation is the Newton-Cotes approximation of the integral. 2. Suppose that f ( x ) = ci x i is analyti

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 2 To approximate the integral of a function over an interval we will use some points on its graph of the function, fit a polynomial through those points, and then integrate the polynomial over the interval. This will serve as an approximatio

• 4 Pages
###### Romberg

School: University Of Florida

Course: Engineering Statistics

A Short Proof for Romberg Integration T. von Petersdorff The American Mathematical Monthly, Vol. 100, No. 8. (Oct., 1993), pp. 783-785. Stable URL: http:/links.jstor.org/sici?sici=0002-9890%28199310%29100%3A8%3C783%3AASPFRI%3E2.0.CO%3B2-O The American Mat

• 14 Pages
• ###### Chapter 9 & 10 One sample case
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###### Chapter 9 & 10 One Sample Case

School: University Of Florida

Course: Engineering Statistics

Revised: August 22, 2011 Statistical Inferences from One Sample (Sections 9.1, 9.2, 10.1, 10.2 and 10.7) Methods of Statistical Inference: Point Estimation Interval Estimation Significance Tests Point Estimation A point estimator is a statistic that sp

• 14 Pages
• ###### Chapter 9 & 10 One sample case
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###### Chapter 9 & 10 One Sample Case

School: University Of Florida

Course: Engineering Statistics

Revised: November 12, 2011 Statistical Inferences from One Sample (Sections 9.1, 9.2, 10.1, 10.2 and 10.7) Methods of Statistical Inference: Point Estimation Interval Estimation Significance Tests Point Estimation A point estimator is a statistic that

• 12 Pages
###### Chapter 8

School: University Of Florida

Course: Engineering Statistics

Chapter 8 Sampling Distribution of Sample Statistics A parameter is a function of population data. Parameters are numerical characteristics of a population. They are fixed numbers, i.e., their values do not change from sample to sample. Some examples are

• 12 Pages
###### Chapter 8

School: University Of Florida

Course: Engineering Statistics

Chapter 8 Sampling Distribution of Sample Statistics A parameter is a function of population data. Parameters are numerical characteristics of a population. They are fixed numbers, i.e., their values do not change from sample to sample. Some examples are

• 8 Pages
###### Chapter 5 And 6

School: University Of Florida

Course: Engineering Statistics

Revised on August 22, 2011 Chapters 5 and 6 Some Commonly Used Distributions Sections 5.1, 5.2, 6.1 and 6.2 Random Variables Definition: A random variable is a function that assigns numerical value to each outcome in a sample space. Random variables are u

• 8 Pages
###### Chapter 5 And 6

School: University Of Florida

Course: Engineering Statistics

Revised on November 12, 2011 Chapters 5 and 6 Some Commonly Used Distributions Sections 5.1, 5.2, 6.1 and 6.2 Random Variables Definition: A random variable is a function that assigns numerical value to each outcome in a sample space. Random variables are

• 4 Pages
###### Chapter 4

School: University Of Florida

Course: Engineering Statistics

Chapter 4 Probability 4.1 Definitions of Some Basic Terms Definition: A Statistical Experiment is an experiment that has Two or more outcomes and Uncertainty as to which outcome will be observed. Definition: Probability is the study of uncertainty in a

• 4 Pages
###### Chapter 4

School: University Of Florida

Course: Engineering Statistics

Chapter 4 Probability 4.1 Definitions of Some Basic Terms Definition: A Statistical Experiment is an experiment that has Two or more outcomes and Uncertainty as to which outcome will be observed. Definition: Probability is the study of uncertainty in a

• 10 Pages
###### Chapter 1

School: University Of Florida

Course: Engineering Statistics

8/22/2011 10:45 AM Chapter 1 Introduction 1.1 A model for problem solving Definition: Statistics is the science of (or a collection of techniques for) Collecting (sampling, census) Classifying (descriptive statistics) Analyzing (e.g., regression analysis)

• 10 Pages
###### Chapter 1

School: University Of Florida

Course: Engineering Statistics

11/12/2011 13:00 a11/p11 Chapter 1 Introduction 1.1 A model for problem solving Definition: Statistics is the science of (or a collection of techniques for) Collecting (sampling, census) Classifying (descriptive statistics) Analyzing (e.g., regression ana

• 20 Pages
• ###### Chapter 9 & 10 Two sample case
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###### Chapter 9 & 10 Two Sample Case

School: University Of Florida

Course: Engineering Statistics

Inferences from two samples (Sections 9.3, 9.4, 10.3) Some new concepts The framework: Until now we had one population, one random sample from that population and just one parameter ( or ) with an unknown value and we made inference about the unknown valu

• 20 Pages
• ###### Chapter 9 & 10 Two sample case
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###### Chapter 9 & 10 Two Sample Case

School: University Of Florida

Course: Engineering Statistics

Inferences from two samples (Sections 9.3, 9.4, 10.3) Some new concepts The framework: Until now we had one population, one random sample from that population and just one parameter ( or ) with an unknown value and we made inference about the unknown valu

• 3 Pages

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Quiz 1 1. Determine a polynomial of minimal degree that passes through the following points. cfw_(1,1), (2, 3), (3, 0), (4, 2)

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• ###### MAD 4401 Study Guide for Test 1
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###### MAD 4401 Study Guide For Test 1

School: University Of Florida

Course: Engineering Statistics

MAD 4401 Keesling 1. Study Guide for Test 1 Test on 9/21/11 Have the following programs debugged on your TI-89 and ready for use on Test 1. Lagrange for a vector of x values and a vector of y values Vandermonde matrix for a vector of points Newton Cotes c

• 5 Pages
###### Which Test Blank

School: University Of Florida

Course: Engineering Statistics

For each combination of response and predictor, a) What do we want to infer? b) Which test(s) should we use? c) What are the assumptions needed? (Factor)CategoricalPredictor is Response has a Normal Distribution One Group Two Independent Groups Matched Pa

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• ###### STA3032 Quiz 8 Solutions
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###### STA3032 Quiz 8 Solutions

School: University Of Florida

Course: Engineering Statistics

STA3032 QUIZ 8 SOLUTIONS FALL 2011 Problem 10.95 [page 566] The mean breaking strength of cotton threats must be at least 215 grams, for the threat to be used in a certain garment. A random sample of 50 measurements on a certain threat gave a mean break

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• ###### Destructive Testing Tasks and Schedule
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###### Destructive Testing Tasks And Schedule

School: University Of Florida

Course: Engineering Statistics

Destructive Testing Tasks and Schedule 279 Miller Rd, Orange City, FL Testing Dates: Friday, November 11th through Sunday November 13th Team Members: Craig Dixon, David Roueche, Karl Kremser, Justin Henika, Abraham Alende (possibly), Brian Wemple (possibl

• 22 Pages
• ###### Chapter 10 Chi-Square Tests
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###### Chapter 10 Chi-Square Tests

School: University Of Florida

Course: Engineering Statistics

The Chi-Square Tests (Section 10.4) This section deals with independence/association between two categorical variables. We will cover three tests that are very similar in nature but differ in the conditions when they can be used. These are A) Goodness-of-

• 21 Pages
• ###### Chapter 10 Chi-Square Tests
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###### Chapter 10 Chi-Square Tests

School: University Of Florida

Course: Engineering Statistics

The Chi-Square Tests (Section 10.4) This section deals with independence/association between two categorical variables. We will cover three tests that are very similar in nature but differ in the conditions when they can be used. These are A) Goodness-of-

• 20 Pages
###### Chapter 11 Slr

School: University Of Florida

Course: Engineering Statistics

Chapter 11 Inferences for Regression Parameters 11.1 Simple Linear Regression (SLR) Model This topic is covered in Chapter 2 (which we skipped). In these notes we are going to cover sections 2.3 to 2.10 (which in a sense describe the relation between two

• 20 Pages
###### Chapter 11 Slr

School: University Of Florida

Course: Engineering Statistics

Chapter 11 Inferences for Regression Parameters 11.1 Simple Linear Regression (SLR) Model This topic is covered in Chapter 2 (which we skipped). In these notes we are going to cover sections 2.3 to 2.10 (which in a sense describe the relation between two

• 22 Pages
###### Chapter 11 Mlr

School: University Of Florida

Course: Engineering Statistics

Multiple Regression Analysis The basic ideas are the same as in SLR We have one response (dependent) variable, Y. The response (Y) is a quantitative variable, Y ~ N ( Y |X , ) There are more than one predictors (independent variables): X1, X2, , Xk where

• 23 Pages
###### Chapter 11 Mlr

School: University Of Florida

Course: Engineering Statistics

Multiple Regression Analysis The basic ideas are the same as in SLR We have one response (dependent) variable, Y. The response (Y) is a quantitative variable, Y ~ N ( Y |X , ) There are more than one predictors (independent variables): X1, X2, , Xk where

• 3 Pages
###### Romberg2

School: University Of Florida

Course: Engineering Statistics

Romberg Integration James Keesling 1 The Trapezoidal Rule for Estimating the Integral A common way of estimating an integral is to use the Trapezoidal Rule. Let f (x) be b the given function over the interval [a, b]. We want to estimate the integral a f (

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• ###### STA3032 Quiz5 Solutioon
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###### STA3032 Quiz5 Solutioon

School: University Of Florida

Course: Engineering Statistics

STA3032 QUIZ 5 SOLUTION Spring 2013 If 100 test welds are to be measured for each type, find the approximate probability that the two sample means differ by at most 1 psi. Assume that both types of welds have the same mean shear strength. Let X = strength

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###### Document 10

School: University Of Florida

Course: Statistics For Engineers

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###### Document 9

School: University Of Florida

Course: Statistics For Engineers

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###### Document 8

School: University Of Florida

Course: Statistics For Engineers

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###### Document 7

School: University Of Florida

Course: Statistics For Engineers

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###### Document 6

School: University Of Florida

Course: Statistics For Engineers

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###### Document 5

School: University Of Florida

Course: Statistics For Engineers

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###### Document 4

School: University Of Florida

Course: Statistics For Engineers

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###### Document 3

School: University Of Florida

Course: Statistics For Engineers

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###### Document 2

School: University Of Florida

Course: Statistics For Engineers

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###### Document 1

School: University Of Florida

Course: Statistics For Engineers

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###### Problem Identification

School: University Of Florida

For each combination of response and predictor, a) What do we want to infer? b) Which test(s) should we use? c) What are the assumptions needed? (Factor)CategoricalPredictor is Response has a Normal Distribution Response is Categorical One Group Inference

• 2 Pages
###### CIpractice

School: University Of Florida

1. An electrical engineer wishes to compare the mean lifetimes of two types of transistors in an application involving high-temperature performance. A sample of 60 transistors of type A were tested and were found to have a mean lifetime of 1827 hours and

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###### Q3Formulas

School: University Of Florida

STA 3032 - Section 1054 - Summer A 2009 - Quiz 3 Formulas Test Statistics z= x / n z= pp0 p0 (1p0 )/n tn1 = x s/ n z= (y 0 ) x z= 2 2 X /nX +Y /nY pX pY p(1p)(1/nX +1/nY ) 2 t = tn1 = (y )0 x s2 /nX +s2 /nY X Y D0 sD / n , where = [(s2 /nX )i+(hs2 /nY

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• ###### STA3032 Quiz-2 Solutions
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###### STA3032 Quiz-2 Solutions

School: University Of Florida

Course: Engineering Statistics

UNIVERSITY OF FLORIDA DEPARTMENT OF STATISTICS STA3032 QUIZ 2 Solutions Spring 2013 Problem 4.10 A manufacturing company has two retail outlets. It is known that 30% of the potential customers buy products from outlet I alone, 50% buy from outlet II alone

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• ###### STA3032 Quiz-1 SOLUTION
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###### STA3032 Quiz-1 SOLUTION

School: University Of Florida

Course: Engineering Statistics

STA3032 QUIZ 1 SOLUTION Spring 2013 Problem 1.27 The environmental lead impact studies on the quality of water often measure the level of chemical toxicants by LC50, concentration lethal to 50% of the organisms of a particular species under a given set of

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• ###### STA3032 Quiz 4 Solutions
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###### STA3032 Quiz 4 Solutions

School: University Of Florida

Course: Engineering Statistics

STA3032 QUIZ - 4 Solutions Spring 2013 Instructions: Before answering the questions below, specify the following: 1. Define the random variable of interest In both problems the random variable is X = The weekly amount spent for maintenance and repairs in

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• ###### STA3032 Quiz 3 SOLUTIONS
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###### STA3032 Quiz 3 SOLUTIONS

School: University Of Florida

Course: Engineering Statistics

STA3032 1. 2. 3. 4. 5. a) b) c) d) e) QUIZ - 3 SOLUTIONS Spring 2013 Problem 5.29 According to an article, 55% of all US corporations say that one of the most important factors in locating a corporate headquarters is the quality of life for the employees.

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###### Assignment3Solutions

School: University Of Florida

STA 3032 - Section 1054 - Summer A 2009 Assignment 3 This Assignment is worth 45 points and will be due in class on Wednesday June 16th. You are expected to show all work. Assignments must be done in a neat and professional manner. Illegible work will not

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###### Assignment3

School: University Of Florida

STA 3032 - Section 1054 - Summer A 2009 Assignment 3 This Assignment is worth 45 points and will be due in class on Wednesday June 16th. You are expected to show all work. Assignments must be done in a neat and professional manner. Illegible work will not

• 2 Pages
###### Assignment2Solutions

School: University Of Florida

STA 3032 - Section 1054 - Summer A 2010 Assignment 2 Solutions 1. Given the normally distributed variable X with mean 18 and standard deviation 2.5, nd (a) P (17 < X < 21). (4 pts) Answer: 0.8849 - 0.3446 = 0.5403 (b) the value of k such that P (X > k ) =

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###### Assignment1Solutions

School: University Of Florida

STA 3032 - Section 1054 - Summer A 2010 Assignment 1 Solutions This Assignment is worth 45 points and will be due in class on Thursday May 21st. You are expected to show all work. Assignments must be done in a neat and professional manner. Illegible work

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###### Quiz3Solutions

School: University Of Florida

STA 3032 - Section 1054 - Summer A 2010 - Quiz 3 Solutions 1. A scientist computes a 90% condence interval for a population mean to be (4.38, 6.02). Using the same data, she also computes a 95% condence interval to be (4.22, 6.18), and a 99% condence inte

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###### Quiz2Solutions

School: University Of Florida

STA 3032 - Section 1054 - Summer A 2010 - Quiz 2 Solutions Please answer all questions on the paper that is provided to you. Do not write on the quiz sheet. Anything you write on the quiz sheet will not be graded. Show all work. 1. The strength of an alum

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###### Quiz1Solutions

School: University Of Florida

STA 3032 - Section 1054 - Summer A 2010 - Quiz 1 Name: Key UF ID: Please answer all questions on the paper that is provided to you. Please show all the steps. If any terms can be written in factorial notation please include at least one step where your an

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• ###### STA3032 QUIZ 5 Solutions
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###### STA3032 QUIZ 5 Solutions

School: University Of Florida

Name (IN CAPITALS): STA 3032 UFID Engineering Statistics Quiz 5 Spring 2011 Problem 10.30 [page 522] A YSW poll reported on November 30, 1984, showed that 54% of 2207 people surveyed thought the US income tax system was too complicated. Can we conclude sa

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• ###### STA3032 Quiz 3 Solutions
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###### STA3032 Quiz 3 Solutions

School: University Of Florida

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###### Quiz 2 Solutions

School: University Of Florida

STA3032 QUIZ 2 Solutions SPRING 2012 Problem 5.28 (modified): The US statistical Abstract reports that, the median family income, in the US, in 1989, was \$34,200. Suppose 4 families were randomly selected from the population of all families in the US in 1

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###### Test2_cover

School: University Of Florida

Course: Engineering Statistics

STA 3032 - Section 7661 - Fall 2011 - Exam 2 Write all answers on the scantron. Nothing you write on the test paper will be graded. Tests will not be graded without picture ID. If you do not have a picture ID with you at the testing site, please bring a p

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###### Test1FormulaSheet

School: University Of Florida

Course: Engineering Statistics

STA 3032 - Section 7661 - Fall 2011 - Test 1 Please write your name and UF ID number on the scantron and answer all questions on the scantron that is provided to you with a # 2 pencil only. You may write on the test sheet. Anything you write on the test s

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###### Quiz9solns

School: University Of Florida

Course: Engineering Statistics

STA 3032 - Section 7661 - Fall 2011 - Quiz 9 Please write your name and UF ID number and answer all questions on the paper that is provided to you. Do not write on the quiz sheet. Anything you write on the quiz sheet will not be graded. Show all work. The

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###### Quiz6_solutions_all

School: University Of Florida

Course: Engineering Statistics

+ r f . = q ,Js + , l/\ T.J.cfw_ v1: q ( (1,+1s + 8"1f,^- ry 381) @ 7 1 turr)= r (W < 7J^ L%J) x ?( tqql X -.-1 l Lr"ro?r _?( f f i t\ ba- m z 4 - T ,r o ,?J 2 rb \ I I i ) =| -ff(rr r -%+F(1, > .-zrrf :/- 1 76,s"@nr) +P( o .rrr)] F"^"*-cfw_4*lt- +J.A L J

• 2 Pages
###### Quiz5_Solutions

School: University Of Florida

Course: Engineering Statistics

C hapler 7 M ultivariate robability istributions P D Figure7 .6 The j oint d ensityf unction for E xample .5 7 The question refers to the marginal behavior of d. Thus, it is necessaryto find - ,1) 0 < x 2 I < rt@z):_1o,x2)dx1: / 1 ,'r,n*,:11*1),',:(t It f

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###### Quiz4Solutions

School: University Of Florida

Course: Engineering Statistics

STA 3032 - Section 7661 - Fall 2011 - Quiz 4 Please write your name and UF ID number and answer all questions on the paper that is provided to you. Do not write on the quiz sheet. Anything you write on the quiz sheet will not be graded. Show all work. Con

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###### Quiz3Summary

School: University Of Florida

Course: Engineering Statistics

Quiz 3 Grades Min. 1st Qu. 0.00 11.00 STA 3032 Median Mean 3rd Qu. 17.00 14.45 Max. 20.00 Histogram for Quiz 2 Histogram for Quiz 3 30 Frequency 40 Frequency 30 20 20 20 0 5 10 quiz1 15 20 0 0 10 10 0 Frequency 40 60 40 50 80 50 60 Histogram for Quiz 1 20

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###### Quiz3Solutions

School: University Of Florida

Course: Engineering Statistics

STA 3032 - Section 7661 - Fall 2011 - Quiz 3 Please write your name and UF ID number and answer all questions on the paper that is provided to you. Do not write on the quiz sheet. Anything you write on the quiz sheet will not be graded. Show all work. Dis

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###### Quiz2Solutions

School: University Of Florida

Course: Engineering Statistics

STA 3032 - Section 7661 - Fall 2011 - Quiz 2 Please write your name and UF ID number and answer all questions on the paper that is provided to you. Do not write on the quiz sheet. Anything you write on the quiz sheet will not be graded. Show all work. Eac

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School: University Of Florida

Course: Engineering Statistics

Quiz 2 Grades STA 3032 Min. 1st Qu. 0.00 Median 15.00 20.00 Mean 3rd Qu. 16.76 20.00 Max. 20.00 Boxplot for Quiz 2 0 5 10 15 20 40 60 80 0 Frequency 20 Histogram for Quiz 2 0 5 10 15 20 quiz.2 0.00 0.10 Estimated Density Curve for Quiz 2 Density 0.08 0.04

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###### Quiz1Solutions

School: University Of Florida

Course: Engineering Statistics

STA 3032 - Section 7661 - Fall 2011 - Quiz 1 Please write your name and UF ID number and answer all questions on the paper that is provided to you. Do not write on the quiz sheet. Anything you write on the quiz sheet will not be graded. Show all work. 1.

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###### Quiz7_solutions

School: University Of Florida

Course: Engineering Statistics

D d "= 6-, h , = A .j= , O ? ?( r r,-f. rJ =? l I t\ = ] oo 1b , Y -r l. : /) -/ =?(z < z =?(z ) "13r^ [ ?(z>t:sflf .(t 3 1 ,7t t)\ [ t - pt* 5 t z 'ilJ =)(a =@ 1- ? : ( o , q q t i l- L = o ,1q3l @ [ ,=G ^ ' = ' 1 = 2 'b ,tt, = ' 15o ' u> - 1 ,J19 \ : 44

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###### Assignment1

School: University Of Florida

Course: Engineering Statistics

STA 3032 - Section 7661 -Fall 2011 Assignment 1 This Assignment is worth 50 points and will be due in class on Friday September 30th. You are expected to show all work, and explain steps with sentences where necessary. Assignments must be done in a neat a

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###### Quiz 1 Solutions

School: University Of Florida

STA 3032 Engineering Statistics Quiz 1 Solutions Spring 2011 Problem 1.29 [Page 38]: According to the Society of Civil Engineers, many bridges in the US are either structurally deficient or functionally obsolete. The table below shows the available data f

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###### New Quiz 1 Solutions

School: University Of Florida

STA 3032 Engineering Statistics New Quiz 1 Solutions Spring 2011 Problem 4.10 [Page 166]: A manufacturing company has two retail outlets. It is known that 30% of the potential customers buy products from outlet I alone = P(ABc) 50% buy from outlet II al

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• ###### Chapter 9 & 10 Two sample case
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###### Chapter 9 & 10 Two Sample Case

School: University Of Florida

Inferences from two samples (Sections 9.3, 9.4, 10.3) Some new concepts The framework: Until now we had one population, one random sample from that population and just one parameter ( or ) with an unknown value and we made inference about the unknown valu

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