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Course: STATISTICS 3601, Fall 2009School: CSU Mont. Bay
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STATE CALIFORNIA UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS Statistics 3601 Introductory Statistics for Scientists and Engineers Homework #1 Additional Problems: 1. 2. 3. Four men and four women will be seated at random in eight seats arranged in a row. Find the probability that the men and women sit in alternative seats. Suppose your friend tosses a fair coin twice, but does not let you see the outcomes. If she...
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STATE CALIFORNIA UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS Statistics 3601 Introductory Statistics for Scientists and Engineers Homework #1 Additional Problems: 1. 2. 3. Four men and four women will be seated at random in eight seats arranged in a row. Find the probability that the men and women sit in alternative seats. Suppose your friend tosses a fair coin twice, but does not let you see the outcomes. If she tells you that at least one of the two coins came up heads, then what is the probability that the other toss came up heads? It is common for bits of data in computing systems, stored or transmitted, to be in error. One method for dealing with this involves adding a parity bit. Suppose our data is coded into a 7-bit number. For example, the letter C might be coded 1000011. A parity bit could be added to this number to make it an 8-bit number. If we adopt even parity we arrange it so that all numbers have an even number of bits. For example, we would store C as 10000111 so that it has an even number of 1 bits. As another example, suppose the letter A is coded 1000001. We would store this as 01000001. This time we add a 0 in front of the number since A already had an even number of 1s. When a character is examined, we can count the number of 1 bits and determine its parity. If the parity is odd then there must be an error in at least one bit. Note that if the party is even there still may be errors. If there are exactly two then errors the parity will remain even. The same is true for exactly 4 or 6 errors. Consider the letter C. Suppose that each bit has a probability 0.01 of being in error (note that this includes the parity bit), and that the bits are independent. a. What is the probability that an error of some type occurs? b. What is the probability that, when the number is examined, its parity is odd? c. Suppose that the parity turns out to be odd. What is the probability that the original 7-bit character is correct (i.e. the parity bit is the only bit in error)? d. Comment on the usefulness of this parity bit method under the conditions described. In particular what is the implication of you answer to part c., considering that many, many numbers will be stored and transmitted over a short period of time.
Problems for Simulation: 1. Use simulation to approximate the probabi...
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CSU Mont. Bay - STATISTICS - 3601
CALIFORNIA STATE UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS Statistics 3601 Introductory Statistics for Scientists and Engineers Winter 2003 Lecture: TTh 8-10, NSc 207 Instructor: Prof. Eric A. Suess Office: ScN 319 Phone: 885-3879 Office Hours: TT
CSU Mont. Bay - STATISTICS - 3601
Statistics 6501, Winter 20021Generating Pseudo-random NumbersLinear Congruential Pseudo-random Number Generators Consider the function g(x) = (Cx + D) mod M where C, D and M are constants. Starting with an initial value x0 , we generate a sequen
CSU Mont. Bay - STATISTICS - 3601
Statistics 6501, Winter 20021Simulation ExampleRandom Number Generator I will assume that Random is a function that returns a random number between 0 and 1. As discussed in class a method for creating such a function is: const C = 25173; D = 138
CSU Mont. Bay - STATISTICS - 3601
CALIFORNIA STATE UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS Statistics 3601 Introductory Statistics for Scientists and Engineers Winter 2001 Box-Muller Method: How to simulate two independent Normal random variables with mean and variance 2 . Gen
CSU Mont. Bay - STATISTICS - 3601
CALIFORNIA STATE UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS STATISTIC 3601 Seminar in Econometrics SPRING 2003 Histograms and Boxplots Lottery Data: The lottery data set contains the numbers drawn in each supper lotto. The numbers have been sorted
CSU Mont. Bay - STATISTICS - 3601
CALIFORNIA STATE UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS Statistics 3601 Introductory Statistics for Scientists and Engineers Winter 2003 Lecture: TTh 8-10, NSc 207 Instructor: Prof. Eric A. Suess Office: ScN 319 Phone: 885-3879 e-mail: esuess@c
CSU Mont. Bay - STATISTICS - 3601
CALIFORNIA STATE UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS Statistics 3601 Introductory Statistics for Scientists and Engineers Homework #3 Additional Problems: Suppose an auto manufacturer receives large deliveries of seat belt buckles every Mond
CSU Mont. Bay - STATISTICS - 3601
CALIFORNIA STATE UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS Statistics 3601 Introductory Statistics for Scientists and Engineers Homework #2 Additional Problems:1. In the roll of two fair dice, let Z be the product of the two up faces. a. Find th
CSU Mont. Bay - STATISTICS - 3601
CALIFORNIA STATE UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS STATISTIC 3601 Seminar in Econometrics SPRING 2003 Examples: Minitab and R Air Pollution: To get the descriptive statistics and side-by-side boxplots in Minitab: Stat > Basic Statistics >
CSU Mont. Bay - STATISTICS - 3601
0.7 1-1.6 1-0.2 1-1.2 1-0.1 1 3.4 1 3.7 1 0.8 1 0.0 1 2.0 1 1.9 2 0.8 2 1.1 2 0.1 2-0.1 2 4.4 2 5.5 2 1.6 2 4.6 2 3.4 2
CSU Mont. Bay - STATISTICS - 39004950
Statistics 3900/4950 Spring 2005 Prof. SuessName:_ (print: first last ) NetID #:_ Take-home Midterm - Project 1Instructions: This is a take-home part of the Midterm exam. You may use your books, notes and a computer. Give concise but detailed ans
CSU Mont. Bay - STATISTICS - 39004950
CALIFORNIA STATE UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS STATISTICS 3900/4950 Fall 2004 Lecture: MW 4:00-4:50, ScN207 Instructor: Prof. Eric A. Suess Office: ScN 319 Phone: 885-3879 Office Hours: MW 3:00-3:50 or by appointment Class Website: htt
CSU Mont. Bay - STATISTICS - 39004950
Statistics 6501, Winter 20021Simulation ExampleRandom Number Generator I will assume that Random is a function that returns a random number between 0 and 1. As discussed in class a method for creating such a function is: const C = 25173; D = 138
CSU Mont. Bay - STATISTICS - 39004950
CSU Mont. Bay - STATISTICS - 39004950
Handout09 Stat3900/4950-Data GRE_GPA;Input GRE GPA;Datalines;2100419203.822903.815803.914003.7713003.9520203.810603.54150031900419003.718003.52200419903.512000416503.816403.7518003.923003.9120003.752000
CSU Mont. Bay - STATISTICS - 39004950
Handout Stat3900/4950 DENSITY = 0 + 1 PBFAT + Regressiona Variables Entered/RemovedModel 1Variables EnteredVariables Removedpbfat.Method Forward (Criterion: Probabilit y-ofF-to-enter <= .050)a. Dependent Variable: densityModel Summ
CSU Mont. Bay - STATISTICS - 39004950
Nonlinear Regression Example: Consider data on the U.S. farm population as a percent of the total population for the 44 years 1948-1991. Compare the following regression models. a. Linear: b. Quadratic: c. Cubic: d. Log-Linear: e. Linear-log: f. Log-
CSU Mont. Bay - STATISTICS - 39004950
Handout Stat3900/4950Density = beta_0 + beta_1 * PBFAT + e Regression Analysis 58
CSU Mont. Bay - STATISTICS - 39004950
Handout05 Stat3900/4950The first program has two new lines of code at the top that clears the log and the output files and renumbers the output starting at page 1 after you each runof you SAS program. The program is from page 80 in the Cody&Smit
CSU Mont. Bay - STATISTICS - 39004950
Handout04 Stat3900/4950Today we will discuss PROC FORMAT and the data set optionsLABEL and FORMAT. We will also discuss PROC FREQ and the Chi-Square Test.As an example we will consider the CSU Hayward Faculty Development Technology Survey 200
CSU Mont. Bay - STATISTICS - 39004950
Handout15 Stat3900/4950How to create .rtf and .pdf output using Proc ODS-*Creating an RTF file;ODS RTF BODY='D:\Handout1.rft';data example;input subject gender $ exam1 exam2hwgrade $;final = (exam1 + exam2)/2;If final ge 0 and final
CSU Mont. Bay - STATISTICS - 39004950
Handout01 Stat3900/4950Simple SAS Example. Note the "data step" at the top, the use of the if-then-elsestatements, the datalines command, and the Procs.See pages 3, 7, --data example;input subject gender $ exam1 exam2hwgrade $;final
CSU Mont. Bay - STATISTICS - 39004950
Handout13 Stat3900/4950How to take a Stratified random sample from a dataset.source: UCLA Academic Technology Services, Stat Computing, Statistical Computing Resourceshttp:/www.ats.ucla.edu/stat/http:/www.ats.ucla.edu/stat/sas/faq/http:/www
CSU Mont. Bay - STATISTICS - 39004950
Handout03 Stat3900/4950When using proc means what is the difference between using the "by" subcommandor the "class" subcommand? How to use the "output" subcommand to create a newdata set.--data school;length gender $ 1 teacher $5;input s
CSU Mont. Bay - STATISTICS - 39004950
Handout02 Stat3900/4950How to make comments? What is the missing data symbol in SAS? How dowe create new variables? Procs for descriptive statistics, simple plots, and options.--* Comment rest of the line ;/* Comment what is between *//
CSU Mont. Bay - STATISTICS - 39004950
Handout06 Stat3900/4950The following program from page 142 of Cody & Smith shows how to use uniformrandom number generation to randomize subjects into two different randomly selected groups.--DM 'LOG;CLEAR;OUT;CLEAR';OPTIONS PAGENO = 1 LINES
CSU Mont. Bay - STATISTICS - 39004950
chocsales.txtC865 15K086 9A536 21S163 34K014 1A206 12B713 29chocolate.txtA206 Mokka Coffeee buttercream in dark chockolateA536 Walnoot Walnut halves in bed of dark chocholateB713 Frambozen Rasberry maripan covered in milk chocol
CSU Mont. Bay - STATISTICS - 39004950
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CSU Mont. Bay - STATISTICS - 6011
CALIFORNIA STATE UNIVERSITY, HAYWARD DEPARTMENT OF STATISTICS STATISTICS 6011 Statistical Modeling for Management and Economics WINTER 2005 Handout Case Study: Coca-Cola Goes Small in Russia To create a stem-and-leaf-plot Graph > Stem-and-Leaf To cre
CSU Mont. Bay - STATISTICS - 6250
CountyCodeDistrictCodeCharterNumberSchoolCodeCountyNameDistrictNameSchoolNameRecordTypeSummaryTypeAdministrationMathTestDateMathNumberTestedMathNumberPassedMathPercentPassedMathNumberNotPassedMathPercentageNotPassedMathMeanScaleScore
CSU Mont. Bay - STATISTICS - 6250
Statistics 6250 Spring 2008 Prof. Suess Project 2Name:_ (print: first last ) NetID #:_Instructions: Produce the code and output for the questions below in one MS Word document with the file name lastname_firstname_project2.doc Please print out yo
CSU Mont. Bay - STATISTICS - 6250
Stat. 3910/4910 A Monte Carlo Simulation study of the independent two-sample t-test Notes: In this study the three assumptions of the independent two-sample t-test will be examined. The three assumptions are: 1. independent samples 2. samples from no
CSU Mont. Bay - STATISTICS - 6250
001 M 23 28000 1 2 1 2 3002 F 55 76123 4 5 2 1 1003 M 38 36500 2 2 2 2 1004 F 67 128000 5 3 2 2 4005 M 22 23060 3 3 3 4 2006 M 63 90000 2 3 5 4 3007 F 45 76100 5 3 4 3 3
CSU Mont. Bay - STATISTICS - 6250
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CSU Mont. Bay - STATISTICS - 6250
1 Female AB Young 7710 7.4 2582 Male AB Old 6560 4.7 .3 Male A Young 5690 7.53 1844 Male B Old 6680 6.85 .5 Male A Young . 7.72 1876 Male A Old 6140 3.69 1427 Female A Young 6550 4.78
CSU Mont. Bay - STATISTICS - 6250
chocsales.txtC865 15K086 9A536 21S163 34K014 1A206 12B713 29chocolate.txtA206 Mokka Coffeee buttercream in dark chockolateA536 Walnoot Walnut halves in bed of dark chocholateB713 Frambozen Rasberry maripan covered in milk chocol
CSU Mont. Bay - STATISTICS - 6250
00112/25/1944210 8016010000205/11/1966102 88122 7600308/03/2000 66 90102 62
CSU Mont. Bay - STATISTICS - 6250
Handout02 Stat6250Simple SAS Example from Ch. 2Example of proc freq and proc meansSee pages 12 -*Program 2-1 Your first SAS program;data demographic; infile "c:\books\learning\mydata.txt"; input Gender $ Age Height Weight;run;tit
CSU Mont. Bay - STATISTICS - 6250
Handout01 Stat6250Simple SAS Example from Ch. 1Note how SAS uses the path on the computer to load a data file using the input statement.See pages 5, 6 -*Program 1-1 A sample SAS program;*SAS Program to read veggie data file and to produce
CSU Mont. Bay - STATISTICS - 6250
Handout Stat6250The first program has two new lines of code at the top that clears the log and the output files and renumbers the output starting at page 1 after you each runof you SAS program. The program is from page 80 in the Cody&Smith book
CSU Mont. Bay - STATISTICS - 6250
CountyCodeDistrictCodeCharterNumberSchoolCodeCountyNameDistrictNameSchoolNameRecordTypeSummaryTypeAdministrationMathTestDateMathNumberTestedMathNumberPassedMathPercentPassedMathNumberNotPassedMathPercentageNotPassedMathMeanScaleScore
CSU Mont. Bay - STATISTICS - 6250
2672911 2 37.856808094154 0.398036909690539 22.9690507129434 12.9278871031354 2.63189406320453 7.36933168735275 15.2154034149218 5.28801925480366125937 3 12.7534535669519 0.918888797983527 0.49468294903636 8.1024199944354 1.19102634039137 0.33709933
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Understanding the Correlation Coefficient, rGoal: To understand that r, the correlation coefficient, is most useful in describing the direction of a linear relationship while r2, the coefficient of determination, is most useful in describing the str
CSU Mont. Bay - INSTITUTES - 2007
ACCLAIM Summer Mathematics Institute Final version Statistics, Probability and Number TheoryMonday Day 1 ~ July 16, 2007 8:30 Sign in, get materials, enter data 9:00 Logistics Standards Descriptive Statistics Visual displays Basic Probability LCM, f
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ACCLAIM Elementary Statistics Institute An introduction to organizing and graphing data.Handout: To investigate how good of a player Barry Bonds is we will compute some descriptive statistics: 1. Calculate the Mean and Median as measures of the cent
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CSU Mont. Bay - CS - 4311
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CSU Mont. Bay - CS - 3340
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CSU Mont. Bay - CS - 3340
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CSU Mont. Bay - CS - 3340
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CSU Mont. Bay - CS - 3340
Overview of Swings MVC ArchitectureBy Geoffrey Steffens (BCSi), Socket Software, Australia Geoff@SocketSoftware.com Copyright Socket Software, 2002. This document is released into the Public Domain. Reproduce at will.What is MVC? MVC is an acro
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public class MyCalendar { public static String getName(int month) { switch (month) { case 1: return "January"; case 2: return "February"; case 3: return "March"; case 4: return "April"; case 5: return "May";
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