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School: UCLA
Biostatistics 100B, Midterm 2 Practice Problems General Comments: These problems are taken from old exams from some of my past classes. I have picked a selection of problems that I think will be useful. I do not guarantee that I have here a problem
School: UCLA
Specialized Bacterial Culture Media 1. Minimal Salts Base (use glucose for Burkholderia) Per Liter NH4Cl KH2PO4 MgSO4.7H2O Trace Salts Solution* Carbon source (glucose, sucrose, mannose, etc.) 1g 1.09 g 0.2 g 10 mL 0.1% pH to 7.0 and add 15 g agar p
School: UCLA
Competency Assessment Lab 1 Biostatistics allows statisticians to analyze the public health of the population through various methods. After collecting data about the population's health through methods such as polling or surveys, the data can be analyzed
School: UCLA
Lecture Notes Tuesday, March 31, 2015 2:01 PM mlee@prolacta.com Office hours- room:51-239A Statistics; making inferences about the population based on sampling -science of uncertainty 1. Define population (what you're studying) 2. Taking a good sample 3.
School: UCLA
CourseTitle IntroductiontoBiostatistics CourseNumber Biostatistics100A Credit 4units Term Spring2015 MeetingDay/ Location Instructor Information Monday/Wednesday:45:50,WGYoungCS24(firstlectureis inCHS43105);Friday:1111:50am,FowlerA103B MartinL.Lee,CStat,C
School: UCLA
. prtest cases=.25 One-sample test of proportion cases: Number of obs = 294 -Variable | Mean Std. Err. [95% Conf. Interval] -+-cases | .170068 .0219108 .1271236 .2130125 -p = proportion(cases) z = -3.1651 Ho: p = 0.25 Ha: p < 0.25 Pr(Z < z) = 0.0008 Ha: p
School: UCLA
Lecture Notes Tuesday, March 31, 2015 2:01 PM mlee@prolacta.com Office hours- room:51-239A Statistics; making inferences about the population based on sampling -science of uncertainty 1. Define population (what you're studying) 2. Taking a good sample 3.
School: UCLA
Course: Regression Analysis
Sheet1 1.0708 1.0853 1.0414 1.0751 1.034 1.0502 1.0549 1.0704 1.09 1.0722 1.083 1.0812 1.0513 1.0505 1.0484 1.0512 1.0333 1.0468 1.0622 1.061 1.0551 1.064 1.0631 1.0584 1.0668 1.0911 1.0811 1.0468 1.091 1.079 1.0716 1.0862 1.0719 1.0502 1.0263 1.0101 1.04
School: UCLA
Course: Intro-biostatistics
Gjertson/Fall07 Biostatistics 100A: Lecture Schedule and Outline LECTURE (Mon. CHS 63-105; Wed. & Fri. CHS 73-105) 1. Introduction and Descriptive Statistics (Monday, October 1, 2007) Introduction 1. Population and parameters 2. Sample and statistic
School: UCLA
School: UCLA
Course: Basic Biostatistics
Biostat 100A Introduction to Biostatistics Dr. Karabi Nandy, knandy@sonnet.ucla.edu 1 Biostat 100A Course * Outline Introduction Descriptive statistics Probability and sampling Introduction to Statistical Inference *A detailed outline is available on cour
School: UCLA
School: UCLA
School: UCLA
School: UCLA
School: UCLA
Final Exam Solutions General Comments: The final was a little harder than the midtermsthe median was 76, the low score was 42, and the high score was 100.5 (wow!). I was a little disturbed at how much trouble people had with the last question as th
School: UCLA
Final Exam Practice SOlutionsANOVA and Logistic Regression ANOVA (1) ANOVA Basics: (a) The completed ANOVA table is shown below. The degrees of freedom must add so we have that the within group degrees of freedom is 28. We know that mean squares are
School: UCLA
Course: Regression Analysis
religion gender y1 y2 y3 y4 1 0 21 52 24 15 1 1 34 67 30 25 2 0 30 52 18 11 2 1 41 83 23 14 3 0 64 50 16 11 3 1 58 63 15 12
School: UCLA
Course: Regression Analysis
dose death vstate major minor good 1 59 25 46 48 32 2 48 21 44 47 30 3 44 14 54 64 31 4 43 4 49 58 41
School: UCLA
Course: Regression Analysis
IQ|Parent|SES|CollegeYes|CollegeNo L|L|L|4|349 L|L|LM|2|232 L|L|UM|8|166 L|L|H|4|48 L|H|L|13|64 L|H|LM|27|84 L|H|UM|47|91 L|H|H|39|57 LM|L|L|9|207 LM|L|LM|7|201 LM|L|UM|6|120 LM|L|H|5|47 LM|H|L|33|72 LM|H|LM|64|95 LM|H|UM|74|110 LM|H|H|123|90 UM|L|L|12|12
School: UCLA
Course: Regression Analysis
x|Y 3.36|65 2.88|156 3.63|100 3.41|134 3.78|16 4.02|108 4.00|121 4.23|4 3.73|39 3.85|143 3.97|56 4.51|26 4.54|22 5.00|1 5.00|1 4.72|5 5.00|65
School: UCLA
Course: Regression Analysis
A|1|15.0 A|1|17.0 A|1|13.8 A|1|15.5 A|2|15.7 A|2|15.6 A|2|17.6 A|2|17.1 A|3|14.8 A|3|15.8 A|3|18.2 A|3|16.0 A|4|14.9 A|4|14.2 A|4|15.0 A|4|12.8 A|5|13.0 A|5|16.2 A|5|16.4 A|5|14.8 A|6|15.9 A|6|15.6 A|6|15.0 A|6|15.5 B|1|18.2 B|1|16.8 B|1|18.1 B|1|17.0 B|2
School: UCLA
Competency Assessment Lab 1 Biostatistics allows statisticians to analyze the public health of the population through various methods. After collecting data about the population's health through methods such as polling or surveys, the data can be analyzed
School: UCLA
. prtest cases=.25 One-sample test of proportion cases: Number of obs = 294 -Variable | Mean Std. Err. [95% Conf. Interval] -+-cases | .170068 .0219108 .1271236 .2130125 -p = proportion(cases) z = -3.1651 Ho: p = 0.25 Ha: p < 0.25 Pr(Z < z) = 0.0008 Ha: p
School: UCLA
. ci sbp, by(shock) -> shock = 1 Variable | Obs Mean Std. Err. [95% Conf. Interval] -+-sbp | 34 127.5588 4.015165 119.3899 135.7277 -> shock = 2 Variable | Obs Mean Std. Err. [95% Conf. Interval] -+-sbp | 17 91.94118 7.500721 76.04036 107.842 -> shock = 3
School: UCLA
. ttest sbp1=sbp2 Paired t test -Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] -+-sbp1 | 7 143.3571 2.567363 6.792605 137.075 149.6393 sbp2 | 7 142.6571 2.630305 6.959132 136.221 149.0933 -+-diff | 7 .6999969 1.482917 3.923431 -2.928571 4.3
School: UCLA
2 .04 0 .02 Density .06 .08 1 0 20 40 60 80 0 20 income Density normal income Graphs by gender income -Percentiles Smallest 1% 2 2 5% 5 2 10% 6 2 Obs 183 25% 8 2 Sum of Wgt. 183 50% 75% 90% 95% 99% 13 23 37 45 65 Largest 65 65 65 65 Mean Std. Dev. 18.4316
School: UCLA
95% confidence interval ciup cilo 12.8897 7.02579 1 99% Confidence Interval sample 100 ciup cilo 13.2786 6.63683 1 sample 100 . summarize chol Variable | Obs Mean Std. Dev. Min Max -+-chol | 80 318.05 255.5745 2 1252 . ci chol, level(99) Variable | Obs Me
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 11 Response Surface Methods and Designs Solutions 11.1. A chemical plant produces oxygen by liquefying air and separating it into its component gases by fraction
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 8 Two-Level Fractional Factorial Designs Solutions 8.1. Suppose that in the chemical process development experiment in Problem 6.7, it was only possible to run a
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 12 Robust Parameter Design and Process Robustness Studies Solutions 12.1. Reconsider the leaf spring experiment in Table 12.1. Suppose that the objective is to f
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 13 Experiments with Random Factors Solutions 13.1. An experiment was performed to investigate the capability of a measurement system. Ten parts were randomly sel
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 9 Three-Level and Mixed-Level Factorial and Fractional Factorial Design Solutions 9.1. The effects of developer strength (A) and developer time (B) on the densit
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 5 Introduction to Factorial Designs Solutions 5.1. The following output was obtained from a computer program that performed a two-factor ANOVA on a factorial exp
School: UCLA
CourseTitle IntroductiontoBiostatistics CourseNumber Biostatistics100A Credit 4units Term Spring2015 MeetingDay/ Location Instructor Information Monday/Wednesday:45:50,WGYoungCS24(firstlectureis inCHS43105);Friday:1111:50am,FowlerA103B MartinL.Lee,CStat,C
School: UCLA
Course: Regression Analysis
Spring 2014 Course Syllabus: Math-664 Course Title Textbook Prerequisites Lecture 1 2 3 4 Date 1/23/2014 1/30/2014 2/6/2014 2/13/2014 5 2/20/2014 6 7 8 9 10 11 12 13 14 15 2/27/2014 3/6/2014 3/13/2014 3/27/2014 4/3/2014 4/10/2014 4/17/2014 4/24/2014 5/1/2
School: UCLA
Course: Causal Inference
Course outline Biostatistics 235: Causal Inference Spring quarter 2006 Prof. Thomas R. Belin Course goals 1. Understanding of statistical perspectives on causal inference, especially related to observational studies. Specific focus on selection bias,
School: UCLA
Course: Appld Mltvrt Biostt
Biostatistics 406 Professor A. A. Afifi Course Topics: Various topics in applied multivariate analysis including multiple linear regression, logistic regression, principal components and factor analysis, cluster analysis and survival analysis. Prereq
School: UCLA
Biostatistics 100B, Midterm 2 Practice Problems General Comments: These problems are taken from old exams from some of my past classes. I have picked a selection of problems that I think will be useful. I do not guarantee that I have here a problem
School: UCLA
Specialized Bacterial Culture Media 1. Minimal Salts Base (use glucose for Burkholderia) Per Liter NH4Cl KH2PO4 MgSO4.7H2O Trace Salts Solution* Carbon source (glucose, sucrose, mannose, etc.) 1g 1.09 g 0.2 g 10 mL 0.1% pH to 7.0 and add 15 g agar p
School: UCLA
Competency Assessment Lab 1 Biostatistics allows statisticians to analyze the public health of the population through various methods. After collecting data about the population's health through methods such as polling or surveys, the data can be analyzed
School: UCLA
Lecture Notes Tuesday, March 31, 2015 2:01 PM mlee@prolacta.com Office hours- room:51-239A Statistics; making inferences about the population based on sampling -science of uncertainty 1. Define population (what you're studying) 2. Taking a good sample 3.
School: UCLA
CourseTitle IntroductiontoBiostatistics CourseNumber Biostatistics100A Credit 4units Term Spring2015 MeetingDay/ Location Instructor Information Monday/Wednesday:45:50,WGYoungCS24(firstlectureis inCHS43105);Friday:1111:50am,FowlerA103B MartinL.Lee,CStat,C
School: UCLA
. prtest cases=.25 One-sample test of proportion cases: Number of obs = 294 -Variable | Mean Std. Err. [95% Conf. Interval] -+-cases | .170068 .0219108 .1271236 .2130125 -p = proportion(cases) z = -3.1651 Ho: p = 0.25 Ha: p < 0.25 Pr(Z < z) = 0.0008 Ha: p
School: UCLA
. ci sbp, by(shock) -> shock = 1 Variable | Obs Mean Std. Err. [95% Conf. Interval] -+-sbp | 34 127.5588 4.015165 119.3899 135.7277 -> shock = 2 Variable | Obs Mean Std. Err. [95% Conf. Interval] -+-sbp | 17 91.94118 7.500721 76.04036 107.842 -> shock = 3
School: UCLA
. ttest sbp1=sbp2 Paired t test -Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] -+-sbp1 | 7 143.3571 2.567363 6.792605 137.075 149.6393 sbp2 | 7 142.6571 2.630305 6.959132 136.221 149.0933 -+-diff | 7 .6999969 1.482917 3.923431 -2.928571 4.3
School: UCLA
2 .04 0 .02 Density .06 .08 1 0 20 40 60 80 0 20 income Density normal income Graphs by gender income -Percentiles Smallest 1% 2 2 5% 5 2 10% 6 2 Obs 183 25% 8 2 Sum of Wgt. 183 50% 75% 90% 95% 99% 13 23 37 45 65 Largest 65 65 65 65 Mean Std. Dev. 18.4316
School: UCLA
95% confidence interval ciup cilo 12.8897 7.02579 1 99% Confidence Interval sample 100 ciup cilo 13.2786 6.63683 1 sample 100 . summarize chol Variable | Obs Mean Std. Dev. Min Max -+-chol | 80 318.05 255.5745 2 1252 . ci chol, level(99) Variable | Obs Me
School: UCLA
1.5 1 Density .5 0 0 .2 .4 x .6 .8 1 0 2 Density 4 6 8 In this graph, X has a uniform distribution, thus it appears that every value of x has a roughly equal chance of being generated. .3 .4 .5 xbar .6 .7 .3 .4 xbar .5 .6 .7 The histogram of xbar displays
School: UCLA
Histogram of Income For Men 0 .01 Density .02 .03 Elizabeth Yang Ben Rogers 0 20 income 40 60 Histogram of Income For Women 0 .01 Density .02 .03 .04 Elizabeth Yang Ben Rogers 0 20 40 20 0 income 60 1 Graphs by gender income 40 60 2 By examining the first
School: UCLA
-> group = 1 Stem-and-leaf plot for cholesterol 1* | 95 2* | 2* | 32 2* | 49,49,51,56,57 2* | 68,75 2* | 80,82,89,91,93,94 3* | 01,05,06,11 3* | 22,27 3* | 3* | 68 3* | 81,85 4* | 11 -> group = 2 Stem-and-leaf plot for cholesterol 2* | 24,28,29,34 2* | 41
School: UCLA
Male Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Female 41.098 0.211843 41.1 41 1.4979564 2.2438735 5.7290859 -1.909313 7.7 35.5 43.2 2054.9 50 Mean Standard Error Median Mode Standa
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 11 Response Surface Methods and Designs Solutions 11.1. A chemical plant produces oxygen by liquefying air and separating it into its component gases by fraction
School: UCLA
Course: Regression Analysis
religion gender y1 y2 y3 y4 1 0 21 52 24 15 1 1 34 67 30 25 2 0 30 52 18 11 2 1 41 83 23 14 3 0 64 50 16 11 3 1 58 63 15 12
School: UCLA
Course: Regression Analysis
dose death vstate major minor good 1 59 25 46 48 32 2 48 21 44 47 30 3 44 14 54 64 31 4 43 4 49 58 41
School: UCLA
Course: Regression Analysis
IQ|Parent|SES|CollegeYes|CollegeNo L|L|L|4|349 L|L|LM|2|232 L|L|UM|8|166 L|L|H|4|48 L|H|L|13|64 L|H|LM|27|84 L|H|UM|47|91 L|H|H|39|57 LM|L|L|9|207 LM|L|LM|7|201 LM|L|UM|6|120 LM|L|H|5|47 LM|H|L|33|72 LM|H|LM|64|95 LM|H|UM|74|110 LM|H|H|123|90 UM|L|L|12|12
School: UCLA
Course: Regression Analysis
x|Y 3.36|65 2.88|156 3.63|100 3.41|134 3.78|16 4.02|108 4.00|121 4.23|4 3.73|39 3.85|143 3.97|56 4.51|26 4.54|22 5.00|1 5.00|1 4.72|5 5.00|65
School: UCLA
Course: Regression Analysis
A|1|15.0 A|1|17.0 A|1|13.8 A|1|15.5 A|2|15.7 A|2|15.6 A|2|17.6 A|2|17.1 A|3|14.8 A|3|15.8 A|3|18.2 A|3|16.0 A|4|14.9 A|4|14.2 A|4|15.0 A|4|12.8 A|5|13.0 A|5|16.2 A|5|16.4 A|5|14.8 A|6|15.9 A|6|15.6 A|6|15.0 A|6|15.5 B|1|18.2 B|1|16.8 B|1|18.1 B|1|17.0 B|2
School: UCLA
Course: Regression Analysis
x1|x2|x3|obs 250|8|40|674 250|8|45|370 250|8|50|292 250|9|40|338 250|9|45|266 250|9|50|210 250|10|40|170 250|10|45|118 250|10|50|90 300|8|40|1414 300|8|45|1198 300|8|50|634 300|9|40|1022 300|9|45|620 300|9|50|438 300|10|40|442 300|10|45|332 300|10|50|220
School: UCLA
Course: Regression Analysis
C|D|T1|T2|S|PR|NE|CT|BW|N|PT 460.05|68.58|14|46|687|0|1|0|0|14|0 452.99|67.33|10|73|1065|0|0|1|0|1|0 443.22|67.33|10|85|1065|1|0|1|0|1|0 652.32|68.00|1|67|1065|0|1|1|0|12|0 642.23|68.00|11|78|1065|1|1|1|0|12|0 345.39|67.92|13|51|514|0|1|1|0|3|0 272.37|68.
School: UCLA
Course: Regression Analysis
Romans|1|386|141|34|17|282 Corinth1|2|424|152|35|16|281 Corinth2|3|192|86|28|13|185 Galat|4|128|48|5|6|82 Philip|5|42|29|19|12|107 Colos|6|23|32|17|9|99 Thessal1|7|34|23|8|16|99 Timothy1|8|49|38|9|10|91 Timothy2|9|45|28|11|4|68 Hebrews|10|155|94|37|24|253
School: UCLA
Course: Regression Analysis
Spring 2014 Course Syllabus: Math-664 Course Title Textbook Prerequisites Lecture 1 2 3 4 Date 1/23/2014 1/30/2014 2/6/2014 2/13/2014 5 2/20/2014 6 7 8 9 10 11 12 13 14 15 2/27/2014 3/6/2014 3/13/2014 3/27/2014 4/3/2014 4/10/2014 4/17/2014 4/24/2014 5/1/2
School: UCLA
Course: Regression Analysis
Sheet1 1.0708 1.0853 1.0414 1.0751 1.034 1.0502 1.0549 1.0704 1.09 1.0722 1.083 1.0812 1.0513 1.0505 1.0484 1.0512 1.0333 1.0468 1.0622 1.061 1.0551 1.064 1.0631 1.0584 1.0668 1.0911 1.0811 1.0468 1.091 1.079 1.0716 1.0862 1.0719 1.0502 1.0263 1.0101 1.04
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 8 Two-Level Fractional Factorial Designs Solutions 8.1. Suppose that in the chemical process development experiment in Problem 6.7, it was only possible to run a
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 12 Robust Parameter Design and Process Robustness Studies Solutions 12.1. Reconsider the leaf spring experiment in Table 12.1. Suppose that the objective is to f
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 13 Experiments with Random Factors Solutions 13.1. An experiment was performed to investigate the capability of a measurement system. Ten parts were randomly sel
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 9 Three-Level and Mixed-Level Factorial and Fractional Factorial Design Solutions 9.1. The effects of developer strength (A) and developer time (B) on the densit
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 5 Introduction to Factorial Designs Solutions 5.1. The following output was obtained from a computer program that performed a two-factor ANOVA on a factorial exp
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 6 k The 2 Factorial Design Solutions 6.1. An engineer is interested in the effects of cutting speed (A), tool geometry (B), and cutting angle on the life (in hou
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 10 Fitting Regression Models Solutions 10.1. The tensile strength of a paper product is related to the amount of hardwood in the pulp. Ten samples are produced i
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 3 Experiments with a Single Factor: The Analysis of Variance Solutions 3.1. An experimenter has conducted a single-factor experiment with four levels of the fact
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2008) Design and Analysis of Experiments, Wiley, NY Chapter 4 Randomized Blocks, Latin Squares, and Related Designs Solutions 4.1. The ANOVA from a randomized complete block experiment output is shown below. Source DF SS
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 2 Simple Comparative Experiments Solutions 2.1. Computer output for a random sample of data is shown below. Some of the quantities are missing. Compute the value
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 7 Blocking and Confounding in the 2k Factorial Design Solutions 7.1 Consider the experiment described in Problem 6.1. Analyze this experiment assuming that each
School: UCLA
Course: Basic Biostatistics
Biostat 100A Introduction to Biostatistics Dr. Karabi Nandy, knandy@sonnet.ucla.edu 1 Biostat 100A Course * Outline Introduction Descriptive statistics Probability and sampling Introduction to Statistical Inference *A detailed outline is available on cour
School: UCLA
School: UCLA
School: UCLA
School: UCLA
School: UCLA
Course: Intro-biostatistics
Biostatistics 100A / Introduction to Biostatistics Lecture: Discussion: Lab: 1A 1B 1C 1D 1E 1F 1G 1H M/W F M W F W F M W W 12-1:50pm 12-1:50pm 11-11:50am 11-11:50am 11-11:50am 2-2:50pm 2-2:50pm 2-2:50pm 9-9:50am 8-8:50am Gjertson: Fall 2007 CHS 63-1
School: UCLA
Course: Intro-biostatistics
Gjertson/Fall07 Biostatistics 100A: REFERENCES COURSE BOOK Rosner. 2006. Fundamentals of Biostatistics, 6th ed. Thomson Brooks/Cole, Belmont, CA. INTRODUCTORY BIOSTATISTICS/STATISTICS BOOKS The following texts provide information at the same level a
School: UCLA
Course: Intro-biostatistics
Gjertson/Fall07 Biostatistics 100A: Lecture Schedule and Outline LECTURE (Mon. CHS 63-105; Wed. & Fri. CHS 73-105) 1. Introduction and Descriptive Statistics (Monday, October 1, 2007) Introduction 1. Population and parameters 2. Sample and statistic
School: UCLA
Course: Basic Biostatistics
Biostat 110A/F08 Solution key for Assignment #7 2.(partial answer)a. Length of 95% CI of no longer than 10, i.e 21.96(1600/n) 10 or, 1600/n 6.50770. So, n 245.86. Hence the smallest n would be 246. 5 a. Here we want to test H0 : = 65 vs. Ha :
School: UCLA
Course: Appld Mltvrt Biostt
PROC MI and MIANALYZE Documentation as of May 29, 2003 Here are various web sites for SAS documentation: Brief summary: http:/support.sas.com/rnd/app/da/new/dami.html Good documentation: http:/support.sas.com/rnd/app/papers/multipleimputation.pdf Com
School: UCLA
Course: Causal Inference
Course outline Biostatistics 235: Causal Inference Spring quarter 2006 Prof. Thomas R. Belin Course goals 1. Understanding of statistical perspectives on causal inference, especially related to observational studies. Specific focus on selection bias,
School: UCLA
Course: Appld Mltvrt Biostt
Biostatistics 406 Professor A. A. Afifi Course Topics: Various topics in applied multivariate analysis including multiple linear regression, logistic regression, principal components and factor analysis, cluster analysis and survival analysis. Prereq
School: UCLA
Standard Media Used In Nodulation Studies 1. Jensen's Agar Slants Recipe for one liter: CaHPO4 K2HPO4 (or 0.26 g K2HPO4.3H2O) MgSO4.7H2O NaCl FeCl3 Hoagland's micronutrients (see next page) pH to 6.8; add 12 g agar 1.0 g 0.2 g 0.2 g 0.2 g 0.1 g 5 mL
School: UCLA
Aseptic Procedures Necessity. Plants have certain basic requirements that enable them to live, grow, and reproduce. The same is true for tissues and cells. One of these requirements is a source of energy, which for the intact plant, is light for phot
School: UCLA
Biostatistics 100B Solutions To Homework Assignment 4 Homework Solutions 4 February 5th, 2007 Warmup Problems (1) Interval Basics: A confidence interval gives you a range of values which you are (reasonably) sure includes the AVERAGE value of Y as
School: UCLA
Final Exam Solutions General Comments: The final was a little harder than the midtermsthe median was 76, the low score was 42, and the high score was 100.5 (wow!). I was a little disturbed at how much trouble people had with the last question as th
School: UCLA
Biostatistics 100B Solutions To Homework Assignment 3 Homework Solutions 3 January 22nd, 2007 Warmup Problems (1) Basic Relationships: (a) There are many pairs of variables with positive linear relationships. For instance we saw in class that if C
School: UCLA
Biostatistics 100B Solutions To Homework Assignment 2 Homework Solutions 2 January 16th, 2007 Warmup Problems (1) Statistics and Agriculture: (a) By looking at the table it seems as if fertilizer 2 is doing a better job. We applied each of the fer
School: UCLA
Midterm One Solutions General Comments: Overall I was quite happy with the performance on this exam. The mean was 84.5, the median was 87, the high score was 100.5 and the low score was 45.5 I have given approximate grade ranges below. However, reme
School: UCLA
Biostatistics 100B Solutions To Homework Assignment 5 Homework Solutions 5 February 12th, 2007 Warmup Problems (1) Interpreting A Multiple Regression Equation: (a) False. It is never safe to say that a change in X causes a change in Y. Just becaus
School: UCLA
Biostatistics 100B Solutions To Homework Assignment 6 Homework Solutions 6 February 20th, 2007 Warmup Problems (1) Regression Assumptions Practice 1: (a) The first figure shows a residual plot, which is essentially a scatterplot turned on it's sid
School: UCLA
Midterm One Friday the 2nd of February, 2007 Name: General Comments: This exam is closed book. However, you may use two pages, front and back, of notes and formulas. Write your answers on the exam sheets. If you need more space, continue your answe
School: UCLA
Final Exam Practice SOlutionsANOVA and Logistic Regression ANOVA (1) ANOVA Basics: (a) The completed ANOVA table is shown below. The degrees of freedom must add so we have that the within group degrees of freedom is 28. We know that mean squares are
School: UCLA
Lecture Notes Tuesday, March 31, 2015 2:01 PM mlee@prolacta.com Office hours- room:51-239A Statistics; making inferences about the population based on sampling -science of uncertainty 1. Define population (what you're studying) 2. Taking a good sample 3.
School: UCLA
Course: Regression Analysis
Sheet1 1.0708 1.0853 1.0414 1.0751 1.034 1.0502 1.0549 1.0704 1.09 1.0722 1.083 1.0812 1.0513 1.0505 1.0484 1.0512 1.0333 1.0468 1.0622 1.061 1.0551 1.064 1.0631 1.0584 1.0668 1.0911 1.0811 1.0468 1.091 1.079 1.0716 1.0862 1.0719 1.0502 1.0263 1.0101 1.04
School: UCLA
Course: Intro-biostatistics
Gjertson/Fall07 Biostatistics 100A: Lecture Schedule and Outline LECTURE (Mon. CHS 63-105; Wed. & Fri. CHS 73-105) 1. Introduction and Descriptive Statistics (Monday, October 1, 2007) Introduction 1. Population and parameters 2. Sample and statistic
School: UCLA
School: UCLA
Course: Basic Biostatistics
Biostat 100A Introduction to Biostatistics Dr. Karabi Nandy, knandy@sonnet.ucla.edu 1 Biostat 100A Course * Outline Introduction Descriptive statistics Probability and sampling Introduction to Statistical Inference *A detailed outline is available on cour
School: UCLA
School: UCLA
School: UCLA
School: UCLA
School: UCLA
Final Exam Solutions General Comments: The final was a little harder than the midtermsthe median was 76, the low score was 42, and the high score was 100.5 (wow!). I was a little disturbed at how much trouble people had with the last question as th
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Final Exam Practice SOlutionsANOVA and Logistic Regression ANOVA (1) ANOVA Basics: (a) The completed ANOVA table is shown below. The degrees of freedom must add so we have that the within group degrees of freedom is 28. We know that mean squares are
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Final Exam PracticeANOVA and Logistic Regression General Comments: This file contains some practice problems on ANOVA and logistic regression. For practice on contingency tables, regression, etc. see the practice files from our first two midterms. I
School: UCLA
FINAL EXAM STUDY GUIDE: (1) The final will NOT take place in our usual classroom. Rather it will be in CHS 43105. This is the same room as ours except on the 4 th floor. We also have CHS 41-268 available as an overflow room but I do not think we will
School: UCLA
Course: Regression Analysis
religion gender y1 y2 y3 y4 1 0 21 52 24 15 1 1 34 67 30 25 2 0 30 52 18 11 2 1 41 83 23 14 3 0 64 50 16 11 3 1 58 63 15 12
School: UCLA
Course: Regression Analysis
dose death vstate major minor good 1 59 25 46 48 32 2 48 21 44 47 30 3 44 14 54 64 31 4 43 4 49 58 41
School: UCLA
Course: Regression Analysis
IQ|Parent|SES|CollegeYes|CollegeNo L|L|L|4|349 L|L|LM|2|232 L|L|UM|8|166 L|L|H|4|48 L|H|L|13|64 L|H|LM|27|84 L|H|UM|47|91 L|H|H|39|57 LM|L|L|9|207 LM|L|LM|7|201 LM|L|UM|6|120 LM|L|H|5|47 LM|H|L|33|72 LM|H|LM|64|95 LM|H|UM|74|110 LM|H|H|123|90 UM|L|L|12|12
School: UCLA
Course: Regression Analysis
x|Y 3.36|65 2.88|156 3.63|100 3.41|134 3.78|16 4.02|108 4.00|121 4.23|4 3.73|39 3.85|143 3.97|56 4.51|26 4.54|22 5.00|1 5.00|1 4.72|5 5.00|65
School: UCLA
Course: Regression Analysis
A|1|15.0 A|1|17.0 A|1|13.8 A|1|15.5 A|2|15.7 A|2|15.6 A|2|17.6 A|2|17.1 A|3|14.8 A|3|15.8 A|3|18.2 A|3|16.0 A|4|14.9 A|4|14.2 A|4|15.0 A|4|12.8 A|5|13.0 A|5|16.2 A|5|16.4 A|5|14.8 A|6|15.9 A|6|15.6 A|6|15.0 A|6|15.5 B|1|18.2 B|1|16.8 B|1|18.1 B|1|17.0 B|2
School: UCLA
Course: Regression Analysis
x1|x2|x3|obs 250|8|40|674 250|8|45|370 250|8|50|292 250|9|40|338 250|9|45|266 250|9|50|210 250|10|40|170 250|10|45|118 250|10|50|90 300|8|40|1414 300|8|45|1198 300|8|50|634 300|9|40|1022 300|9|45|620 300|9|50|438 300|10|40|442 300|10|45|332 300|10|50|220
School: UCLA
Course: Regression Analysis
C|D|T1|T2|S|PR|NE|CT|BW|N|PT 460.05|68.58|14|46|687|0|1|0|0|14|0 452.99|67.33|10|73|1065|0|0|1|0|1|0 443.22|67.33|10|85|1065|1|0|1|0|1|0 652.32|68.00|1|67|1065|0|1|1|0|12|0 642.23|68.00|11|78|1065|1|1|1|0|12|0 345.39|67.92|13|51|514|0|1|1|0|3|0 272.37|68.
School: UCLA
Course: Regression Analysis
Romans|1|386|141|34|17|282 Corinth1|2|424|152|35|16|281 Corinth2|3|192|86|28|13|185 Galat|4|128|48|5|6|82 Philip|5|42|29|19|12|107 Colos|6|23|32|17|9|99 Thessal1|7|34|23|8|16|99 Timothy1|8|49|38|9|10|91 Timothy2|9|45|28|11|4|68 Hebrews|10|155|94|37|24|253
School: UCLA
Biostatistics 100B Solutions To Homework Assignment 4 Homework Solutions 4 February 5th, 2007 Warmup Problems (1) Interval Basics: A confidence interval gives you a range of values which you are (reasonably) sure includes the AVERAGE value of Y as
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Biostatistics 100B Solutions To Homework Assignment 3 Homework Solutions 3 January 22nd, 2007 Warmup Problems (1) Basic Relationships: (a) There are many pairs of variables with positive linear relationships. For instance we saw in class that if C
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Biostatistics 100B Solutions To Homework Assignment 2 Homework Solutions 2 January 16th, 2007 Warmup Problems (1) Statistics and Agriculture: (a) By looking at the table it seems as if fertilizer 2 is doing a better job. We applied each of the fer
School: UCLA
Biostatistics 100B Solutions To Homework Assignment 5 Homework Solutions 5 February 12th, 2007 Warmup Problems (1) Interpreting A Multiple Regression Equation: (a) False. It is never safe to say that a change in X causes a change in Y. Just becaus
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Biostatistics 100B Solutions To Homework Assignment 6 Homework Solutions 6 February 20th, 2007 Warmup Problems (1) Regression Assumptions Practice 1: (a) The first figure shows a residual plot, which is essentially a scatterplot turned on it's sid
School: UCLA
Competency Assessment Lab 1 Biostatistics allows statisticians to analyze the public health of the population through various methods. After collecting data about the population's health through methods such as polling or surveys, the data can be analyzed
School: UCLA
. prtest cases=.25 One-sample test of proportion cases: Number of obs = 294 -Variable | Mean Std. Err. [95% Conf. Interval] -+-cases | .170068 .0219108 .1271236 .2130125 -p = proportion(cases) z = -3.1651 Ho: p = 0.25 Ha: p < 0.25 Pr(Z < z) = 0.0008 Ha: p
School: UCLA
. ci sbp, by(shock) -> shock = 1 Variable | Obs Mean Std. Err. [95% Conf. Interval] -+-sbp | 34 127.5588 4.015165 119.3899 135.7277 -> shock = 2 Variable | Obs Mean Std. Err. [95% Conf. Interval] -+-sbp | 17 91.94118 7.500721 76.04036 107.842 -> shock = 3
School: UCLA
. ttest sbp1=sbp2 Paired t test -Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] -+-sbp1 | 7 143.3571 2.567363 6.792605 137.075 149.6393 sbp2 | 7 142.6571 2.630305 6.959132 136.221 149.0933 -+-diff | 7 .6999969 1.482917 3.923431 -2.928571 4.3
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2 .04 0 .02 Density .06 .08 1 0 20 40 60 80 0 20 income Density normal income Graphs by gender income -Percentiles Smallest 1% 2 2 5% 5 2 10% 6 2 Obs 183 25% 8 2 Sum of Wgt. 183 50% 75% 90% 95% 99% 13 23 37 45 65 Largest 65 65 65 65 Mean Std. Dev. 18.4316
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95% confidence interval ciup cilo 12.8897 7.02579 1 99% Confidence Interval sample 100 ciup cilo 13.2786 6.63683 1 sample 100 . summarize chol Variable | Obs Mean Std. Dev. Min Max -+-chol | 80 318.05 255.5745 2 1252 . ci chol, level(99) Variable | Obs Me
School: UCLA
1.5 1 Density .5 0 0 .2 .4 x .6 .8 1 0 2 Density 4 6 8 In this graph, X has a uniform distribution, thus it appears that every value of x has a roughly equal chance of being generated. .3 .4 .5 xbar .6 .7 .3 .4 xbar .5 .6 .7 The histogram of xbar displays
School: UCLA
Histogram of Income For Men 0 .01 Density .02 .03 Elizabeth Yang Ben Rogers 0 20 income 40 60 Histogram of Income For Women 0 .01 Density .02 .03 .04 Elizabeth Yang Ben Rogers 0 20 40 20 0 income 60 1 Graphs by gender income 40 60 2 By examining the first
School: UCLA
-> group = 1 Stem-and-leaf plot for cholesterol 1* | 95 2* | 2* | 32 2* | 49,49,51,56,57 2* | 68,75 2* | 80,82,89,91,93,94 3* | 01,05,06,11 3* | 22,27 3* | 3* | 68 3* | 81,85 4* | 11 -> group = 2 Stem-and-leaf plot for cholesterol 2* | 24,28,29,34 2* | 41
School: UCLA
Male Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Female 41.098 0.211843 41.1 41 1.4979564 2.2438735 5.7290859 -1.909313 7.7 35.5 43.2 2054.9 50 Mean Standard Error Median Mode Standa
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 11 Response Surface Methods and Designs Solutions 11.1. A chemical plant produces oxygen by liquefying air and separating it into its component gases by fraction
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 8 Two-Level Fractional Factorial Designs Solutions 8.1. Suppose that in the chemical process development experiment in Problem 6.7, it was only possible to run a
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 12 Robust Parameter Design and Process Robustness Studies Solutions 12.1. Reconsider the leaf spring experiment in Table 12.1. Suppose that the objective is to f
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 13 Experiments with Random Factors Solutions 13.1. An experiment was performed to investigate the capability of a measurement system. Ten parts were randomly sel
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 9 Three-Level and Mixed-Level Factorial and Fractional Factorial Design Solutions 9.1. The effects of developer strength (A) and developer time (B) on the densit
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 5 Introduction to Factorial Designs Solutions 5.1. The following output was obtained from a computer program that performed a two-factor ANOVA on a factorial exp
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 6 k The 2 Factorial Design Solutions 6.1. An engineer is interested in the effects of cutting speed (A), tool geometry (B), and cutting angle on the life (in hou
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 10 Fitting Regression Models Solutions 10.1. The tensile strength of a paper product is related to the amount of hardwood in the pulp. Ten samples are produced i
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 3 Experiments with a Single Factor: The Analysis of Variance Solutions 3.1. An experimenter has conducted a single-factor experiment with four levels of the fact
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2008) Design and Analysis of Experiments, Wiley, NY Chapter 4 Randomized Blocks, Latin Squares, and Related Designs Solutions 4.1. The ANOVA from a randomized complete block experiment output is shown below. Source DF SS
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 2 Simple Comparative Experiments Solutions 2.1. Computer output for a random sample of data is shown below. Some of the quantities are missing. Compute the value
School: UCLA
Course: Regression Analysis
Solutions from Montgomery, D. C. (2012) Design and Analysis of Experiments, Wiley, NY Chapter 7 Blocking and Confounding in the 2k Factorial Design Solutions 7.1 Consider the experiment described in Problem 6.1. Analyze this experiment assuming that each
School: UCLA
MIDTERM 2 STUDY GUIDE: Here are some notes and study suggestions for Friday's exam-I will also post this on the web as the "Midterm 2 Study Guide" in the Exam section: (1) I was asked if you could bring your cheat sheets from midterm 1 in addition to
School: UCLA
CourseTitle IntroductiontoBiostatistics CourseNumber Biostatistics100A Credit 4units Term Spring2015 MeetingDay/ Location Instructor Information Monday/Wednesday:45:50,WGYoungCS24(firstlectureis inCHS43105);Friday:1111:50am,FowlerA103B MartinL.Lee,CStat,C
School: UCLA
Course: Regression Analysis
Spring 2014 Course Syllabus: Math-664 Course Title Textbook Prerequisites Lecture 1 2 3 4 Date 1/23/2014 1/30/2014 2/6/2014 2/13/2014 5 2/20/2014 6 7 8 9 10 11 12 13 14 15 2/27/2014 3/6/2014 3/13/2014 3/27/2014 4/3/2014 4/10/2014 4/17/2014 4/24/2014 5/1/2
School: UCLA
Course: Causal Inference
Course outline Biostatistics 235: Causal Inference Spring quarter 2006 Prof. Thomas R. Belin Course goals 1. Understanding of statistical perspectives on causal inference, especially related to observational studies. Specific focus on selection bias,
School: UCLA
Course: Appld Mltvrt Biostt
Biostatistics 406 Professor A. A. Afifi Course Topics: Various topics in applied multivariate analysis including multiple linear regression, logistic regression, principal components and factor analysis, cluster analysis and survival analysis. Prereq