# Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.

1 Page

### homework1

Course: STATISTICS 3601, Fall 2009
School: CSU Mont. Bay
Rating:

Word Count: 519

#### Document Preview

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...

Register Now

#### Unformatted Document Excerpt

Coursehero >> California >> CSU Mont. Bay >> STATISTICS 3601

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

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 &gt; Basic Statistics &gt;
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 &lt;= .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&amp;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 &quot;data step&quot; 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 &quot;by&quot; subcommandor the &quot;class&quot; subcommand? How to use the &quot;output&quot; 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 &amp; 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
1202.6 0830.1 0372.4 0345.5 0321.2 0244.3 0163.0 0147.8 095.0 087.0 081.2 068.5 047.3 041.1 036.6 029.0 028.6 026.3 026.1 024.4 021.7 017.3 011.5 04.9 04.9 0
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 &gt; Stem-and-Leaf To cre
CSU Mont. Bay - STATISTICS - 6250
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
001 10/21/1950 05122003 08/10/65 23Dec2005002 01/01/1960 11122009 09/13/02 02Jan1960
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 &quot;c:\books\learning\mydata.txt&quot;; 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&amp;Smith book
CSU Mont. Bay - STATISTICS - 6250
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
CSU Mont. Bay - FOLLOWUP - 2
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
CSU Mont. Bay - FOLLOWUP - 1
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
CSU Mont. Bay - CS - 4320
LIFE CYCLE OF SOFTWARE ENGINEERING =References: Software Engineering: A Practitioner's Approach by Pressman. Software Engineering by Sommerville. An Integrated Approach to Software Engineering by Jalote.
CSU Mont. Bay - CS - 4311
p i 1x 3 k ox 070 s k i x x x sn(myx 3 l jx 06 hgf H x e&amp; x 0 1&quot; x 0 0 VVx 3 P&quot;sx yw x d v u ` DG` R F F % S Y X 2 FU F 6QGehG&amp;\$Ws1@ t ` TG` R Y X 2 FU F @ S Y X 2 FU F HGWs1ThG&amp;\$WsG% r ` TG` R F
CSU Mont. Bay - CS - 3340
n oml ped c 9 e 8@ k c HeE efd jeT#fedia c i feEh`B 8 G fed c g c feE epd qefe#dT ia c efd HGd FECDcB d 77 66 g fed c SD epd Hesh2E`cB r77 66 ped c T uu w2iDpy#VWibxYC wvt 4 5321077 66 T e HGFEC qG#piI Td d h e ag`YCXWVURSQEI A c c a
CSU Mont. Bay - CS - 3340
Uccw Q @w&amp;dUvbunt#tvdx`IGs Q @ns qW0r lm lm'% (&amp;%\$nqW0g Q nqt W q W00 p w 5 px`IuGvtRQ@s9o E S (@0g Q B`IYGbEryG g E 5 jj ii d gEfe#QUdw 20 W0 W h q 0 B`IYGE x`IuGvtRQ9E q 20 Q w C w C px`IuGvtRQ9E 20 2ryC 0g E rqihgg 2xSwf `IvGutRQ
CSU Mont. Bay - CS - 3340
UECQ2hfgTHedu@ED2CQitqeyih S `S b p Q8 5 @ fC XX tI C2PQihgcH&amp;edcVWUTR fS `S b SS QaE@&amp;E`PC6YBVWUSTR C X S }hg TYXCy D pD h h| UAG8e2SQ EEPC\$tqVDBf43 (&amp;210bX F p 2bomlj wnk wF p \$uefsdw PQB h@ r C pVSS BE@BaUTR EH2CQ Q hV Dy @CaCUBx@w C
CSU Mont. Bay - CS - 3340
dz F F aI R FI DB sI a F ED0kD8r{DikEWVUTSHQEeG0FdSEbIkYwgQR ~ g ~ 0tESqFR ~ 8D0Fii8ScmbD{ikwdkIWFEqI8YSHQ0kDbFgiG yy tQ aQ Y r Da D F haQ DlQ R D F FQ a F r Y Y zibDqRw0QEbIkYweQi0ah0gbDihbgSkQi80ai8eEkDbFWYhd0rikdiS9kI0aiw9ghbRiu{i8s h R t Y
CSU Mont. Bay - CS - 3340
3yxX R GE I R pa wvVuTSQP8IhFtDQsSUrqdb` @ A9876XYWVTUSQ8IHFdSfdb` RP GEDgeca CB X RP GEDa YVWUTSi8IC BhFHb` CB X GE YWVUTSCR QBP 8IHFD 4 ) \$ ! 053210('&amp;%#&quot;
CSU Mont. Bay - CS - 3340
m klwxvut&amp;spqigecbaYWUSRQ r hf d `X VT )iiT y p d d w ` T `X V rWcbaUT SRQ )cpWTGT e tb IH PCGFE f GwQ )cp` w tX p Q t p V rT apkj)p)UiR)p` )bh&amp;giUR 4 6 6 A 9 # 6 4 2 % 0 ' # ! D77CB@)587531) (&amp;%\$&quot;
CSU Mont. Bay - CS - 4311
# # ' ) E ! # ! 8 ! 8 ! ) 5 1 70\$&amp;CDCAB&amp;A@970643 1 ) ' ) ' % # ! 200(&amp;\$&quot; ) 9#!0 643 8 &quot; ) 1 1 # ! 2C0 ! B(BC&quot;( ) ' ) ' E 8 ! ) 5 C\$0&amp;7 1 ) 9#!0 643 8 &quot; ) 1 1 # ! 2C0 ! B
CSU Mont. Bay - CS - 3340
P3Q3CBR9qGD T E9`C9 W 9 T UobUE`C9F T b S ~GdDvdCvwz`}9 T dbb S D T 9 S !RQGa`9F f A ph 9 C F I b Dn v b h I9sD Y g vx l9 C9 9 W aY S `}9 S s S { dbyD dWW cDUo)`9 f n Cuv W Uo|db@xU`ba`Y9 W ty`Fw5UUUo\$EbU`E9eRQ`v f 9 C v C 9 9 lD 9h Yb 9 C F C F
CSU Mont. Bay - CS - 3340
Q)@ 7 G 2Ag)9 6D#4t )A2#)9 UXXP361yh @ 7II p 4 ) 7p 4 hRII X6@#)9 361y3fxUH)@ 74 4 2A#)9 XUFD`b )@ 7 2A#)9 i6W#4dGc )@ 7G 4 ( wRII Ab#)9 8f616Wv43SQPH&amp;\$ %#&quot;!)@ `1 A2#)9 67vG3u )@ 74Dr qp 4 Xb#)9 XUtsP361ih ) G1 `R 5e X6@g)9 6738F4fba4Q3d1c )
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
CSU Mont. Bay - CS - 4320
CODE VERIFICATION =Reference: Software Testing in the Real World: Improving the Process by KitGENERIC CODE VERIFICATION CHECKLISTI. Data Reference Errors A. Array, string 1. Is su
CSU Mont. Bay - CS - 11
public class MyCalendar { public static String getName(int month) { switch (month) { case 1: return &quot;January&quot;; case 2: return &quot;February&quot;; case 3: return &quot;March&quot;; case 4: return &quot;April&quot;; case 5: return &quot;May&quot;;
CSU Mont. Bay - CS - 4320
FUNCTIONAL DESIGN SPECIFICATION: GOLFSCORE=BASICS OF GOLF:There are 18 holes to a course.A golfer uses a club to hit the ball from a &quot;tee&quot;.And then continues to hit the ball until it goes into the cup on the green.The number of strokes - how
CSU Mont. Bay - CS - 4320
SOFTWARE QUALITY ASSURANCE (SQA) =Reference: Software Engineering: A Practitioner's Approach by Pressman.SQA Process Goal: high-quality software.Software Quality: Conformance to 1. explicit funct