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Washington | STAT 390
Professors
- Marzban,caren,
- Marzban,
- Caren,
- Caren Marzban
100 sample documents related to STAT 390
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Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics Week 1 Lecture Notes Two Types of Statistics: Data: cases, variables Descriptive and Inferential Descriptive: - Median - Mode - Range - Histogr -
Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics Boys and Girls Statistics Notes Problem: The variable is the \"percentage of time student attends lectures\", and the two groups are boys and gir -
Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics Boxplots Lecture Notes Boxplots of data are a means of summarizing the data into five numbers that capture the shape of the histogram The five -
Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics Simulation Generation of Data Notes Simulation (i.e. generation of data) from mass and density functions: Imitation of the operation of a real- -
Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics STAT 390 Homework Set 2 Bell-shaped: The book mentions height, weight, measurement errors, reaction times in psychological experiments, . numer -
Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics Chapter 7 Notes (b) The number of batches with at most 5 nonconforming items is, which is a proportion of 55/60 = .917 The proportion of batche -
Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics Numerical Summary Measures Notes The three quantities of interest are: Their relationship is as follows: For the strength observations: (a) The -
Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics Section 1.3 Notes The density curve forms a rectangle over the interval [4, 6] For this reason, uniform densities are also called rectangular d -
Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics Section 1.4 Notes a. Proportion (z 2.15) = .9842 (Table I) Proportion (z < 2.15) will also equal .9842 The z distribution is continuous b. Us -
Washington STAT 390
STAT 390: Probability and Statistics in Engineering and Science Professor: Caren Marzban Department: Statistics Week 2 Lecture Notes The following stem-and-leaf display was constructed using MINITAB: A typical data value is somewhere in the low 2000\'s The -
Washington STAT 390
Stat 390, Homework set #1 Due Wednesday 11, 2012 Problem 1. Chapter 1, Problem 13. Consider a group of 20 people. If everyone shakes hands with everyone else, how many handshakes take place? Solution to Problem 1. Suppose that we code the 20 persons with -
Washington STAT 390
Stat 390, Homework set #2 Due Wednesday 18, 2012 Problem 1. Chapter 2, Problem 12. An elementary school is oering 3 language classes: one in Spanish, one in French, and one in German. The classes are open to any of the 100 students in the school. There ar -
Washington STAT 390
Stat 390, Homework set #3 Due Wednesday 25, 2012 Problem 1. Chapter 4, Problems 5 and 6. Problem 5: Let X represent the dierence between the number of heads and the number of tails obtained when a coin is tossed n times. What are the possible values of X -
Washington STAT 390
Stat 390, Homework set #4 Due April 30, 2012 Problem 1. Chapter 5, Problem 5.11. A point is chosen at random on a line segment of length L. Interpret this statement, and nd the probability that the 1 ratio of the shorter to the longer segment is less than -
Washington STAT 390
Stat 390, Homework set #5 Due Wednesday, February 22, 2012 Problem 1. Chapter 6, Problem 6.6 A bin of 5 transistors is known to contain 2 that are defective. The transistors are to be tested, one at a time, until the defective ones are identied. Denote by -
Washington STAT 390
Stat 390, Homework set #6 Due Wednesday February 29, 2012 Problem 1. This problem consists of two problems from the textbook: Chapter 7, Problems 7.30. and 7.33. Chapter 7, Problems 7.30. If X and Y are independent and identically distributed with mean an -
Washington STAT 390
Problem 7.22 In this problem, we have n = 507 and p = 142 / 507 .28008 . For a two-sided 99% confidence interval, we use z* = 2.576 : p+ ( z *)2 2n z* 1+ p (1 p ) n ( z *)2 n + ( z *)2 4 n2 = .28008 + 2.5762 2 ( 507 ) 2.576 1+ .28008 (1.28008 ) 507 + 2. -
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Washington STAT 390
Homeworks 17-19 Solutions 5.46 x = diameter of a piston ring = 12 cm = .04 cm x = = 12 cm (a) .04 x = n = n .04 = .005 64 (b) (c) When n = 64 x = 12 cm x = The mean of a random sample of size 64 is more likely to lie within .01 cm of , since x is small -
Washington STAT 390
HW Set 6: Due 5/11 in quiz session. HW16: hw_Z, hw_AA, hw_AB (lecture 18), 7.4(a), 7.11(a,b,c). HW17: 7.15, 7.18, 7.22 (using messy eqn in lecture 19), 7.23 (using simple eqn in lecture 19; typo in answer), 7.76 (using simple eqn in lect 19). HW18: 7.27 ( -
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Washington STAT 390
A college library has five copies of a certain text on reserve. Two copies (1 and 2) are first printings, and the other three (3, 4, and 5) are second printings. A student examines these books in random order, stopping only when a second printing has been -
Washington STAT 390
Problem 4.12 (a) N = N i = 1000 = 10(1000) = 10,000 i =1 i =1 k 10 (b) Since we now require a confidence level of 90%, our z value is now z = 1.645. Without any previous information about i , we set i = 0.5 each stratum. Thus, i2 = i (1 i ) = .5(1 .5) = -
Washington STAT 390
HW Set 5: Due 5/4 in quiz session. HW12: hw_U, hw_V, hw_W (lecture 14), 5.10, 5.11 (warning: answer has typos). HW13: hw_X, hw_Y (lecture 15), 5.14, 5.59. HW14: 5.20, 5.21 (Warning: hard!), 5.24 (Note: Venn diagrams are useful for illustrating events, not -
Washington STAT 390
Problem 4.12 (a) N = N i = 1000 = 10(1000) = 10,000 i =1 i =1 k 10 (b) Since we now require a confidence level of 90%, our z value is now z = 1.645. Without any previous information about i , we set i = 0.5 each stratum. Thus, i2 = i (1 i ) = .5(1 .5) = -
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Washington STAT 390
A college library has five copies of a certain text on reserve. Two copies (1 and 2) are first printings, and the other three (3, 4, and 5) are second printings. A student examines these books in random order, stopping only when a second printing has been -
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Washington STAT 390
Obs Depth content Strength 1 8.9 31.5 14.7 2 36.6 27.0 48.0 3 36.8 25.9 25.6 4 6.1 39.1 10.0 5 6.9 39.2 16.0 6 6.9 38.3 16.8 7 7.3 33.9 20.7 8 8.4 33.8 38.8 9 6.5 27.9 16.9 10 8.0 33.1 27.0 11 4.5 26.3 16.0 12 9.9 37.0 24.9 13 2.9 34. -
Washington STAT 390
An experiment carried out to study the effect of the mole contents of cobalt (x1) and the calcination temperature (x2) on the surface area of an ironcobalt hydroxide catalyst (y) resulted in the following data (\"Structural Changes and Surface Properties o -
Washington STAT 390
# I made a ascii text file (hw_S_dat.txt) and cut/paste the data into it, # including a line of header. Then, dat = read.table(\"hw_S_dat.txt\", header=T) x1 = dat[,1] x2 = dat[,2] y = dat[,3] # a) lm.1 = lm(y ~ x1 + x2 + I(x1^2) + I(x2^2) + I(x1*x2) lm. -
Washington STAT 390
HW Set 4: Due 4/27 in quiz session. HW9: 3.21(c,d; use the print-out given in the problem as much as possible.), 3.22 (By computer. The statement \"assess the quality of the fit, .\" means \"Compute R-squared and interpret it.\") 3.25(b,c,d; by hand) . HW10: -
Washington STAT 390
Homeworks 10-12 - Solutions 3.18 (a) To obtain the least squares regression equation, first we will compute the slope and the vertical intercept using the equations provided in Section 3.3. ( 517) ( 346) = 13,047.71 S xy = 25,825 14 2 ( 517 ) = 20,002.93 -
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Washington STAT 390
Problem 3.18(a,b,c) (a) To obtain the least squares regression equation, first we will compute the slope and the vertical intercept using the equations provided in Section 3.3. ( 517) ( 346) = 13,047.71 S xy = 25,825 14 2 ( 517 ) = 20,002.93 S xx = 39,095 -
Washington STAT 390
HW Set 3: Due 4/20 in quiz session. HW8: hw_O (lecture 9), 3.18(a,b), 3.47(a,b). Graded problems (and points): TBA. 18. (a) To obtain the least squares regression equation, first we will compute the slope and the vertical intercept using the equations pro -
Washington STAT 390
Problem 3.11 (a) SSxy = 5530.92 - (1950)(47.92)/18 = 339.586667, SSxx = 251,970 - (1950)2/18 = 40,720,and SSyy = 130.6074 - (47.92)2/18 = 3.033711, so r= 339.586667 40720 3.033711 = .9662 There is a very strong positive correlation between the two variabl -
Washington STAT 390
Consider writing onto a computer disk and then sending it through a certifier that counts the number of missing pulses. Suppose this number x has a Poisson distribution with parameter lambda=0.2 . a. What proportion of disks have exactly one missing pulse -
Washington STAT 390
HW Set 2: Due 4/13 in quiz session. HW5: hw_F, hw_G, hw_H, hw_I (end of lecture 5), HW6: 2.8, 2.12, hw_J (end of lecture 6). HW7: 2.22, hw_K, hw_L (end of lecture 7), 2.39c (by computer; check homework Rules for what that means), 2.45 (make sure you compu -
Washington STAT 390
HOMEWORKS 5-8 1.64. (a) Let x denote the number of units that fracture. Then x is binomial with n = 25, = .20 and we can use Table II. Proportion(x 10) = .011 + .004 + .002 + .000 + .+ .000 = .017. (b) Proportion(x 5) = .004 + .023 + .071 + .136 + .187 + -
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Washington STAT 390
HW Set 1: Due 4/6 in quiz session. HW1: hw_A (at the end of lecture 1), 1.8 (a,b), 1.18 (a,b; by hand, or by computer). HW2: hw_B and hw_C (end of lecture 2), 1.19(a,b,c), 1.20(c), 1.24(a). HW3: 1.28, 1.30, 1.32. HW4: hw_D, hw_E (end of lecture 4), 1.34(a -
Washington STAT 390
Problem 1.8 (a) The histogram is: Frequency 800 700 600 500 400 300 200 100 0 0 2 4 6 8 10 12 14 16 18 Number of papers The most interesting feature of the histogram is the heavy positive skewness of the data. (b) From the frequency distribution (or from -
Washington STAT 390
# This last lab will discuss the two tests that arise in the context of # ANOVA and regression, namely the F-test, the t.test for the # regression coefficients, and the Confidence Interval (CI) and the # Prediction Interval (PI) applied to the predicted -
Washington STAT 390
# In this Lab, we\'ll do t.tests and chi-squared tests. It seems long, but # it\'s only 20 lines of code, many of which are very similar. So, use # the UP/DOWN arrows. # 1) Even though we\'ve used t.test() already, we\'re going to use it again, # but this t -
Washington STAT 390
# This lab looks long, but it\'s not! # Most of the material is on the web, where you can cut/paste from. # The majority of this document is explanations - lots of it. So, # read carefuly. # 1) Last time, we computed CIs in three ways: a) Using the formu -
Washington STAT 390
# This lab might seem similar to the previous lab, and it is! # But there are some subtle differences. The main difference is # the emphasis this lab places on the concept of a confidence interval, # and what it is expected/designed to do. # The first ta -
Washington STAT 390
# Sampling Distribution: # In class, we have seen the mathematical derivation of the mean and variance # of the sampling distribution of a statistic; the two statistics we have # discussed have been 1) sample mean and 2) sample proportion. Here you will -
Washington STAT 390
# Last time we did regression on hail data, but the only models we examined were # lm(size ~ diverg) r.squared = 0.2719047 # lm(size ~ rotate) r.squared = 0.2900612 # lm(size ~ diverg+rotate) r.squared = 0.3628943 # And, based on these, it appears that -
Washington STAT 390
# Try running these regression ideas on the two continuous variables # you collected in an earlier hw. Do this on your own time - not in the lab, # but if you have questions/trouble, ask me or your TA. # # In dealing with two continuous variables, we\' -
Washington STAT 390
# The following will illustrate some of the concepts we introduced during last # week. # 1) Let\'s get a feeling of what a Normal qqplot looks like for # different kinds of distributions. Recall, that if the data comes from a # Normal distribution, then i -
Washington STAT 390
# In this lab, we will look at three of the distributions we\'ve been talking # about in class; binomial, poisson, normal. But before that we\'ll also # learn how to make *density scale* histogram. # # 1) Relative frequency histogram: dat = read.table(\"htt -
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Washington STAT 390
x y time 70 60 1 72 83 1 94 85 1 80 72 2 60 74 2 55 58 2 45 63 3 50 40 3 35 54 3 -
Washington STAT 390
attendance Gender 85 1 100 0 66 1 2 0 75 1 95 1 40 1 100 0 90 0 95 0 100 0 100 1 90 0 70 0 75 1 70 1 100 1 70 1 100 1 100 1 99 1 95 1 75 1 100 1 100 1 90 1 40 1 50 1 95 1 99 1 100 0 60 1 90 1 90 0 80 1 75 0 100 1 50 1 100 1 95 1 98 1 98 1 50 1 75 1 1 -
Washington STAT 390
STAT/MATH 390 - SUMMER 2001 Final Examination Name: Student ID: Section: Please print your name, student ID and your quiz section in the above spaces. Do not open the exam until instructed to do so. This is a closed book examination. You may use -
Washington STAT 390
de ~ie ~ie ~ie ~ ie W RI`a v dU ga s`hbea VVbchR}ida w a3WRIb0i RhzS xv syRtiRpI ja w mfzyiy SW e Y Q g cI t e t Y QI S w dta i t ea x i aW St e S q SI Y gStU S YSUSt QI S W aW S e Qt a S rI | r Y sRbShWhy{sVuVya v -
Washington STAT 390
STAT/MATH 390 - SUMMER 2001 Project: Campaign 2000 1 Introduction As we know, there was a lot of uncertainty during Campaign 2000; one question was, were we satisfied with the choice of candidates? A poll, conducted by an independent opinion resea -
Washington STAT 390
STAT/MATH 390 - SUMMER 2001 Solution to Final Examination Name: Student ID: Section: Please print your name, student ID and your quiz section in the above spaces. Do not open the exam until instructed to do so. This is a closed book examination. -
Washington STAT 390
w wq l j x7u9g w d |i0Sj W iz( u ( r|26YS0Su{ d x zk t w xt et xu t t t j d j yj d g g j e e j g y d rxi d qWzj l d e t0Su{ d WdSrx d 2xSw We l j y e g yj { l j e g e d t t e t -
Washington STAT 390
STAT/MATH 390 SUMMER 2001 Project: Campaign 2000 1 Introduction As we know, there was a lot of uncertainty during Campaign 2000; one question was, were we satised with the choice of candidates? A poll, conducted by an independent opinion research -
Washington STAT 390
v~ w t yu (yE(w9`at S \"3 6 9u` du` 6Pdu26\"`0 z e (y|zw3zS`\"yu`zxz{C\"t zx(y y {~u y x y { {w w `39u ug`u fSug`u `ug`u `0 y\" -
Washington STAT 390
3s2 cHAprER Testing 8 Stotisticol Hypotheses Valuesof z at leastas contradictory llo as this are thoseeven smallerthan -6.17 to (thoseresultingfrom x valuesthat are even fartherbelow l5 than 11.3).Thus P-varue : lffi:il.-\'H.n:ff], P-v a l u e :Q< -
Washington STAT 390
An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics Version 0.99.0 (2000 February 7) R Development Core Team Copyright c 1990, 1992 W. Venables Copyright c 1997, R. Gentleman & R. Ihaka Copyright c 1997, 1998 M -
Washington STAT 390
| p y qe v e | y z rqn p wG{F Q t n kw)DSFGFnv z r td m p rm m rn~ m hv m z z s y w | y } s | fr eCQt e { hGFnv CQXFfry y )rFp Q e { z z } s y z s y v y e z z } m y w | m m m x -
Washington STAT 390
Quiz 3 (10 minutes, 3 points): In a homework problem, you came across the so-called uniform distribution, which looks \"flat\" between two numbers. In R, runif() generates a sample from such a distribution. Check USAGE in ?runif() to find out how it -
Washington STAT 390
Problem 1.8 (a) The histogram is: Frequency 800 700 600 500 400 300 200 100 0 0 2 4 6 8 10 12 14 16 18 Number of papers The most interesting feature of the histogram is the heavy positive skewness of the data. (b) From the frequency distribution -
Washington STAT 390
Divergence Rotational_velocity Hail_size 26 14 175 24 8 175 40 11 150 24 10 175 29 16 350 38 19 100 28 14 150 48 17 100 22 11 175 61 24 300 76 23 400 42 19 100 25 13 100 34 12 175 33 18 175 72 28 275 38 21 275 30 23 150 49 16 175 42 26 175 21 15 75 2 -
Washington STAT 390
Quiz 2 (10 minutes, 3 points): Write code to a) Simulate/generate the number of heads in an experiment where 50 fair coins are tossed 200 times. Assign the 200 numbers to a variable called x. Hint: this is just one line of code. b) Plot the hist -
Washington STAT 390
Frequency 0 0.6 2 4 6 8 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 grade -
Washington STAT 390
# For most of the lab sessions you will type things in on your own. This is # painful, but important. Later, you\'ll be able to cut/paste bits of R code # that you will be given. Even when you type things in, note that the up/down # arrow keys scro -
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Washington STAT 390
STAT/MATH 390, SPRING 2009 HW SET 2 SOLUTIONS Problem 2.4 The three quantities of interest are: xn , xn1 , and xn 1. Their relationship is as follows: nxn xn1 1 1 n 1 1 n xn1 xi xi xn 1 nxn xn 1 n 1 n 1 i 1 n 1 i 1 n -
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Washington STAT 390
1) Scatterplots. Let\'s look at some synthetic and some \"real\" scatterplots. - Synthetic/Simulated data: x=runif(100,0,1) # Make a uniform variable, x hist(x) # you can see that it does look uniform. e=rnorm(100,0,0.1) -
Washington STAT 390
y=c(85.06,85.25,84.87,84.99,84.28,84.88,84.48,84.72,85.10,84.10,84.55,84.05); x=c(1,1,1,2,2,2,3,3,3,4,4,4) -
Washington STAT 390
0.048650 2.258306 0.771137 0.175339 0.319856 -1.488763 -0.155922 0.340143 0.086347 1.583973 -0.101318 -0.039426 0.600431 -0.210848 -2.078016 0.118124 -0.462555 2.141250 1.937575 -0.798479 0.029495 0.692521 -0.973373 0.037913 1.063089 0.623501 0.37241 -
Washington STAT 390
\'Number of particles:\',\'Frequency:\' 0,1 1,2 2,3 3,12 4,11 5,15 6,18 7,10 8,12 9,4 10,5 11,3 12,1 13,2 14,1 -
Washington STAT 390
Obs x1 x2 y 1 92017 .0026900 278.78 2 51830 .0030000 124.53 3 17236 .0000196 22.65 4 15776 .0000360 28.68 5 33462 .0004960 32.66 6 243500 .0038900 604.70 7 67793 .0011200 27.69 8 23471 .0006400 14.18 9 13948 .0004850 20.64 10 8824 .0003660 20. -
Washington STAT 390
10 8.039999999 9.140000000 7.46 8 6.580000000 8 6.950000000 8.140000000 6.7699999999999 8 5.759999999 13 7.580000000 8.740000000 12.74 8 7.71 9 8.810000000 8.769999999 7.1100000000000 8 8.839999999 11 8.
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