Documents about Data Analysis
Note_Set_7
Rochester, ADA 2006
Excerpt: ... Applied Data Analysis PSC 200 Fall 2006 Note Set 7 Inference: Means Applied Data Analysis Fall 2006 Note Set 7 Page 2 Outline of Lecture Overview of Hypothesis Testing Method 1: P-Values Method 2: Critical Values Method 3: Confidence Intervals One-Sided Tests Applied Data Analysis Fall 2006 Note Set 7 Page 3 Overview of Hypothesis Testing We form some hypothesis about the data Typically, our hope is to reject a null hypothesis We hope to establish beyond a reasonable doubt that the null hypothesis is false Applied Data Analysis Fall 2006 Note Set 7 Page 4 Overview of Hypothesis Testing Example: A pharmaceutical company (PharCo) has developed a new medication (NewMed) that it believes is superior to the best existing medication (OldMed) In particular, PharCo believes that the population proportion, N , who will recover using NewMed is greater than the proportion, O , who will recover using OldMed The burden of proof is on PharCo to demonstrate that ...
|
|
Note_Set_2
Rochester, ADA 2007
Excerpt: ... Applied Data Analysis PSC 200 Fall 2007 Note Set 2 Descriptive Statistics Applied Data Analysis Fall 2007 Note Set 2 Page 2 Outline of Lecture Sampling Random variables Graphical description of data Numerical description of data Describe some data sets we will use in this class Applied Data Analysis Fall 2007 Note Set 2 Page 3 Sampling Population: The set of all objects of interest in a given analysis The SAT scores of all high school students taking the SAT in a given year The presidents approval rating among all American adults Parameter: A constant that characterizes a population The average SAT score in the population ( ) The proportion of adults who approve of the President ( ) Applied Data Analysis Fall 2007 Note Set 2 Page 4 Sampling Sample: A subset of the population for which we collect data The SAT scores of 1,000 random selected high school students The SAT scores of 500 randomly selected female high school students and 500 r ...
|
|
GCH-601-S09
George Mason, GCH 601
Excerpt: ... GEORGE MASON UNIVERSITY COLLEGE OF HEALTH AND HUMAN SERVICES COURSE TITLE: GCH 601 Introduction to Biostatistics INSTRUCTOR: Scott D. Barnett, PhD, MSPH MEETING TIMES: Monday 4:30 7:10 P.M. OFFICE: Robinson Hall B 420 CLASS LOCATION: Enterprise # 177 PHONE: OFFICE HOURS: EMAIL: scott.barnett@inova.org Spring 2009 COURSE DESCRIPTION: This course applies selected biostatistics techniques to public health and health system management issues. Includes univariate and bivariate statistics and regression analysis. TEXTBOOKS: 1. Gerstman, B. B. (2008).Basic biostatistics: Statistics for public health practice. Boston: Jones and Bartlett. 2. Norusis, M. J. (2008). SPSS 16.0 Guide to Data Analysis . Upper Saddle River, NJ: Prentice-Hall, Inc. (Required) 3. SPSS Inc. (2008). SPSS Student Version 16.0 for Windows. Upper Saddle River, NJ: Prentice-Hall, Inc. (Optional) SPSS SOFTWARE: SPSS 16.0 is the most up-to-date software currently available. If you do not have access to GMU campus computers with SPSS (SPSS is avai ...
|
|
wk9boards2x2
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 9 Professor Matt Wand University of Wollongong 1st May, 2007 ...
|
|
wk7boards2x2
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 7 Professor Matt Wand University of Wollongong 17th April, 2007 ...
|
|
wk2boards
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 2 Professor Matt Wand University of Wollongong 6th March, 2007 ...
|
|
wk3boards
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 3 Professor Matt Wand University of Wollongong 13th March, 2007 ...
|
|
wk11boards2x2
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 11 Professor Matt Wand University of Wollongong 15th May, 2007 ...
|
|
wk5boards2x2
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 5 Professor Matt Wand University of Wollongong 27th March, 2007 ...
|
|
wk7boards
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 7 Professor Matt Wand University of Wollongong 17th April, 2007 ...
|
|
wk5boards
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 5 Professor Matt Wand University of Wollongong 27th March, 2007 ...
|
|
wk11boards
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 11 Professor Matt Wand University of Wollongong 15th May, 2007 ...
|
|
wk3boards2x2
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 3 Professor Matt Wand University of Wollongong 13th March, 2007 ...
|
|
wk2boards2x2
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 2 Professor Matt Wand University of Wollongong 6th March, 2007 ...
|
|
wk9boards
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 9 Professor Matt Wand University of Wollongong 1st May, 2007 ...
|
|
wk6boards
Allan Hancock College, WEB 902
Excerpt: ... STAT 902: Advanced Data Analysis Whiteboard Notes; Week 6 Professor Matt Wand University of Wollongong 3rd April, 2007 ...
|
|
3251 Notes Apr 17
Minnesota, JOUR 3251
Excerpt: ... Qualitative Data analysis vs Quantitative Data Analysis Few Externally objective analysis technique No Formulas for determining statistical significance No perfect replication of the analysis process Use an inductive model while quantitative data analysis follows a deductive model Analyzing Qualitative Data But, there are guidelines 4 Stages in Data Analysis 1. Activities before data examination: Review the research problem and informational needs, confirmation and final listing of areas in which the analysis will focus, Evaluation of the study sample. 2. Data examination: Examine audio, video tapes, transcripts and notes, The goal is not to determine what the data means but what the data says, review the data with an open mind and with a critical eye and ear, try to understand the reasons underlying attitudes and behaviors, try to understand the intensity of respondents feelings or beliefs, try to understand the totality of response, reflection after the initial review- quiet contemplation. 3. Theme identifi ...
|
|
Midterm study guide2006
Niagara University, BIO 124
Excerpt: ... ilution Lab 1 - Photosynthesis Effects of light intensity, light color and bicarbonate concentration on the rate of photosynthesis Experimental design, methods, and data analysis Lab 2 Diffusion Observation dye molecules, diffusion in elodea leaf and red onion epidermis Effect of sucrose concentration on the change in weight of dialysis tubing (determination of unknown) Experimental design, methods, and data analysis Lab 3 - Enzymes Determining the concentration of amylase in your saliva Effect of pH on the activity of amylase Experimental design, methods, and data analysis (t-test and unknown) Lab 4 - Respiration Effects of temperature, glucose concentration and bicarbonate concentration on the rate of respiration Experimental design, methods, and data analysis (ANOVA) Lab 6 Mitosis Examination of mitotic cells to create karyotype Phases of mitosis Use of standard error in data analysis . ...
|
|
eda1
Duke, STA 290
Excerpt: ... Introduction to R and Exploratory Data Analysis August 27, 2008 Introduction to RandExploratory Data Analysis p.1/18 Agent Orange Case Study (SS Ch 3) Dioxin concentrations in parts per trillion (ppt) for 646 Vietnam veterans and 97 veterans who did not serve in Vietnam. sample (nonrandom) of Vietnam vets who served during 1967-1968 sample (nonrandom) of vets who served in the US and Germany between 1965-1971 Dioxin measurements taken in 1987 Observational Study Introduction to RandExploratory Data Analysis p.2/18 Startup R Unix command line: R Windows/Mac double-click R GUI Under emacs/ESS enter M-x R Introduction to RandExploratory Data Analysis p.3/18 Creating a Dataframe in R > vets = read.table("case0302.csv", header=T, sep=",") > names(vets) [1] "DIOXIN" "VETERAN" Notes: 1. header=T tells R that the rst line of the le contains variable names 2. sep="," tells R that columns of data are separated by a comma (the csv format) 3. the names function extracts the names of variables ...
|
|
Lecture8_handout
Cornell, FDA 2008
Excerpt: ... BTRY 6150: Applied Functional Data Analysis : Constrained Smoothing BTRY 6150: Applied Functional Data Analysis : Constrained Smoothing Constrained Functions Positive Smoothing We know that angular acceleration must be positive: Text: Chapter 6 There are some situations in which we want to include known restrictions about x(t). x(t) is always positive x(t) is always increasing (or decreasing) x(t) is a density Idea: Enforce these conditions by transforming x(t). a(t) = [D 2 x(t)]2 + [D 2 y (t)]2 But negative values can occur because of smoothing/basis bias. BTRY 6150: Applied Functional Data Analysis : Constrained Smoothing BTRY 6150: Applied Functional Data Analysis : Constrained Smoothing Positive Smoothing We want to ensure that x(t) > 0. Observation: e w : (, ) (0, ) So try the transformation x(t) = e W (t) with W (t) = (t)c and penalize the roughness of W (t) Estimating a Positive Smooth We now want to minimize n PENSSE (W ) = i=1 yi e W (ti ) 2 + [LW (t)]2 dt This ...
|
|
Note_Set_3
Rochester, ADA 2007
Excerpt: ... Applied Data Analysis PSC 200 Fall 2007 Note Set 3 Discrete Probability Distributions Applied Data Analysis Fall 2007 Note Set 3 Page 2 Outline of Lecture Review of Sampling Discrete Random Variables Expected Values Some Discrete Distributions Applied Data Analysis Fall 2007 Note Set 3 Page 3 Review of Sampling Population: The set of all objects of interest in a given analysis Parameter: A constant that characterizes a population Sample: A subset of the population for which we collect data Statistic: A number computed from the sample A statistic summarizes the sample A parameter summarizes the population Learn the parameters of the population using statistics from the sample Applied Data Analysis Fall 2007 Note Set 3 Page 4 Review of Sampling Example: - Are people who watch cable news more likely to be political extremists? Applied Data Analysis Fall 2007 Note Set 3 Page 5 Review of Sampling Simple Random Sample: Each sample of size N has an ...
|
|
E10.2082
NYU, E10 2082
Excerpt: ... E10.2082 Advanced Quantitative Methods II Marc Scott Lecture: Tuesdays 3:30-6:10 pm Location: 25 W. 4th St., Room C-1 Office Hours: Tuesdays 2:30-3:30 pm, and by appointment Webpage: http:/home.nyu.edu/~ms184 Main text: Stevens, Applied Multivariate Statistics for the Social Sciences (4th Ed.) Optional Texts: Tabachnick & Fidell, Using Multivariate Statistics (4 th Ed.) Brian F. Manly, Multivariate Statistical Methods, A Primer (2nd Ed.) Everitt & Dunn, Applied Multivariate Data Analysis . Chap T. Le , Applied Categorical Data Analysis Software: SPSS version 12 or 13. This course will use Blackboard. Spring 2005 Office: 318E Kimball Hall Phone: 212-992-9407 email: marc.scott@nyu.edu COURSE OVERVIEW: This is a course on models for categorical data. Examples will come from health, social, and behavioral science. COURSE REQUIREMENTS: Participation: 10% You are expected to attend class and participate in class discussions Computer problems: 15% There will be several assigned problems intended to give you prac ...
|
|
Note_Set_6
Rochester, ADA 2006
Excerpt: ... Applied Data Analysis PSC 200 Fall 2006 Note Set 6 Sampling Distributions Applied Data Analysis Fall 2006 Note Set 6 Page 2 Outline of Lecture Point Estimators Law of Large Numbers Sampling Distributions The Central Limit Theorem Applied Data Analysis Fall 2006 Note Set 6 Page 3 Point Estimation A point estimator is a best guess of a population parameter Some examples: 1 - The sample mean X = N X n is an estimator of the N population mean = E[ X ] 2 n =1 - The sample variance s X = N1-1 ( X n - X )2 is an estimator of the population variance X 2 = Var ( X ) 1 - The sample proportion p = N Y is an estimator of the population proportion = P ( X = 1) n =1 N Applied Data Analysis Fall 2006 Note Set 6 Page 4 Point Estimation - The sample covariance s XY = 1 N -1 (X n =1 N n - X )(Yn - Y ) is an estimator of the population covariance XY = Cov ( X , Y ) - The sample correlation rXY is an estimator of the population correlation XY Applied Data Analysis Fall 2006 Note ...
|
|
Note_Set_4
Rochester, ADA 2007
Excerpt: ... ` Applied Data Analysis PSC 200 Fall 2007 Note Set 4 Continuous Probability Distributions Applied Data Analysis Fall 2007 Note Set 4 Page 2 Outline of Lecture Continuous Random Variables Expected Values Some Continuous Distributions Applied Data Analysis Fall 2007 Note Set 4 Page 3 Continuous Random Variables Recall: A continuous random variable takes on an infinite continuum of values Examples: Income Weight of newborn babies Sale price of a home GDP of a country Voter turnout in a district Applied Data Analysis Fall 2007 Note Set 4 Page 4 Continuous Random Variables In note set 2, we explored ways of describing and summarizing the sample Like note set 3, note set 4 will explore ways of describing and summarizing the population Once again, we will use probability theory Applied Data Analysis Fall 2007 Note Set 4 Page 5 Continuous Random Variables We describe probability distributions using their probability density function We denot ...
|
|
EDA1
Duke, STA 290
Excerpt: ... Exploratory Data Analysis August 26, 2004 Exploratory Data Analysis p. 1/? Agent Orange Case Study (SS: Ch 3) Dioxin concentrations in parts per trillion (ppt) for 646 Vietnam veterans and 97 veterans who did not serve in Vietnam. sample (nonrandom) of Vietnam vets who served during 1967-1968 sample (nonrandom) of vets who served in the US and Germany between 1965-1971 Dioxin measurements taken in 1987 Exploratory Data Analysis p. 2/? Creting a Dataframe in R > vets = read.table("case0302.csv", header=T, sep=",") > names(vets) [1] "DIOXIN" "VETERAN" > attach(vets} Notes: 1. sep="," tells R that columns of data are separated by a comma (the csv format) 2. header=T tells R that the rst line of the le contains variable names 3. the names function extracts the names variables and cases in a dataframe 4. the attach function allows us to refer to variables in the dataframe vets directly Exploratory Data Analysis p. 3/? Graphical Views 1. Univariate: histograms, density curves, boxplots ...
|