Regression in biostatistics example. Ø For the design of experiments.
Use logistic regression to classify subjects and assess the quality of a classification rule with its sensitivity, specificity and ROC curve. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables 4 days ago · Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. From the data find out the regression equation and draw a regression line on the graph paper. Instructor: John McGready, PhD, MS. In this example, if an individual was 70 inches tall, we would predict his weight to be: Weight = 80 + 2 x (70) = 220 lbs. 9 years, smaller than the sample mean of 10. Understand the concept of the least squares criterion. Jan 26, 2023 · prediction. Biostatistics — that portion of statistics that is most relevant to the biological sciences. This formula is linear in the parameters. May 19, 2022 · The application of simple linear regression analysis was discussed. regression. The purpose of a multiple regression is to find an equation that best predicts the iv. This is an example statistical plan for project proposals where the goal is to develop a biostatistical model for prediction, and to do external or strong internal validation of the model. , N, where is Jan 14, 2021 · Logistic regression is actually an extension of linear regression. A value of -1 signifies a perfect negative, or Jul 9, 2020 · There are 3 main types of descriptive statistics: The distribution concerns the frequency of each value. There is Poisson regression (count data), Gamma regression (outcome strictly greater than 0), Multinomial regression (multiple categorical outcomes), and many, many more. Enroll for Free. Agricultural scientists often use linear regression to measure the effect of fertilizer and water on crop yields. 1 Prepregnancy Weights of Mothers and Birthweights of their Infants. H0 : b1 = 0 vs. Frank Harrell. The result is the impact of each variable on the odds ratio of the observed event of interest. Example: pets and stress We are interested in testing H 0: P = F = N against the alternative that at least one mean is di erent from the others. f (E [Y]) = log [ y/ (1 - y) ]. 2 days ago · A linear regression line equation is written as-. Some common inferential statistical tests include t-tests, ANOVA, chi-square, correlation and regression. Regression analysis is used to understand the relationship between two or more variables and make predictions. Feb 20, 2020 · Multiple linear regression formula. There is a big confusion between subscript i and n in the formula for mean and standard deviation (p 71-72). These rules limit the form to just one type: Dependent variable = constant + parameter * IV + … + parameter * IV. ¯y) ∑n 1 (x−¯. 2 suggest a weak, negative association. Nov 1, 2016 · Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may 11. 12. A good model fit has a high R 2 value. bootstrap. Multiple Regression Line Formula: y= a +b1x1 +b2x2 + b3x3 +…+ btxt + u. Topics include: the simple regression model, estimation of regression parameters-least squares estimation, maximum likelihood estimation, residuals and their properties, expectation and variance of the parameter estimators, student t-test in testing hypothesis about the parameters, confidence interval of the parameters, mean Biostatistics. Module 1 • 4 hours to complete. If you are interested in these topics, SPH offers. Few applications of biostatistics are summarized below. Day #02: Review 2: Simple Correlation and Regression. Additionally, examples and applications will be examined. Y = a + bX. 6 kg for a larger than average height H = 175 cm and then predict height H . The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. Affiliation. 2,3 Rather than modeling a linear relationship between the independent variable (X) and the probability of the outcome (Figure A), which is unnatural since it would allow predicted probabilities outside the range of 0–1, it assumes a linear (straight line) relationship with the logit (the natural logarithm of the odds) of the For example, using multivariable logistic regression to analyze data from a nested case-control study revealed that some carotenoids were inversely associated with breast cancer suggesting that plasma levels of α- or β-carotene may play a role in reducing breast cancer risk . A regression analysis of measurements of a dependent variable Y on an independent variable X produces a statistically significant association between X and Y. In this section we discuss correlation analysis which is a technique used to quantify the associations between two continuous variables. The equations of two lines of regression obtained in a correlation analysis are the following 2X=8–3Y and 2Y=5–X . 01165 + 0. Drawing upon your education in introductory biostatistics, the theory of epidemiology, the scientific method, etc – see how many Use linear regression or correlation when you want to know whether one measurement variable is associated with another measurement variable; you want to measure the strength of the association ( r2 ); or you want an equation that describes the relationship and can be used to predict unknown values. Starting at just $144. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. A regression equation is used in stats to find out what relationship, if any, exists between sets of data. Application of statistical methods in biomedical research began more than 150 years ago. If height were the only determinant of PubH 7405: Biostatistics Regression (Fall 2019) Day #01: Review 1: Basics of Statistical Inferences; plus Course Information , Syllabus , and Presentations . Prerequisites: S&DS 542 and BIS 623 or their equivalents. Jun 28, 2024 · Biostatistics is crucial in conducting research and clinical trials, as it helps to ensure the validity and reliability of the results. Methods range from the elementary, including descriptive statistics, study design, statistical interference, categorical variables, evaluation of diagnostic tests, comparison of means, linear regression, and logistic regression. : BIOSTATISTICS REGRESSION, 2011 . Find the mean values and coefficient of correlation between X and Y. In the blue equation, m = 1 and b = 5. For example, if you measure a child’s height every year you might find that they grow about 3 inches a year. 9 videos 2 readings 2 quizzes. Interpret the effects of length of stay and employee salary on per capita health care expenditures. One way to do this would be to use t-tests on all possible pairs of tests (here there are just three). E-Book. Logistic regression has probably been underutilized in clinical investigations of personality because of its relatively recent development (dictated by the need for computer programs to obtain maximum likelihood estimates), and the fact that use has been largely confined to the fields of biostatistics, epidemiology, and economics Its Jan 6, 2016 · Logistic regression is just one such type of model; in this case, the function f (・) is. However, despite the name linear regression, it can model The Poisson is different than the binomial, Bin(n, π), which takes on numbers only up to some n, and leads to a proportion (out of n). a. This lesson reviews correlation and linear regression. The document discusses regression analysis and its key concepts. In the Komodo dragon example, if all the eggs at \(30^{\circ}C\) were laid by one mother, and all the eggs at \(32^{\circ}C\) were laid by a different mother, that would make the observations non-independent. Study Table 13. My main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and May 21, 2018 · Biostatistics is just a tool – those who conduct clinical trials should make sure that it is used properly and that the results are aligned with critical thinking. = the y-intercept (value of y when all other parameters are set to 0) = the regression coefficient () of the first independent variable () (a. Logistic Regression Model: p = 1 / (1 + e^- (β0 + β1X1 + β2X2 + … + βnXn)) In the formula: p represents the Free B Pharmacy Handwritten Notes for Biostatistics And Research Methodology Unit 2 - 8th Semester In this example, the sample mean of the 14 observations is 9. Logistic regression is a statistical method for modeling the dependence of a categorical (binomial) outcome variable on one or more categorical and continuous predictor variables (Bewick et al 2005). The two regression lines were found to be 4X–5Y+33=0 and 20X–9Y–107=0 . a Regression || Biostatistics and Research Methodology || BP801TT ||Join my Telegram Channel and Download Study Materia We expand our discussion of logistic regression here. Basic Biostatistics presents a multidisciplinary survey of biostatics methods, each illustrated with hands-on examples. We can find the probability of the dependent variable from this odds ratio. It is the predicted value of y when x = 0. A multiple linear regression is performed to predict blood pressure (mm Hg) from age and weight. The exponential beta value in the logistic regression output denotes the odds ratio of the dependent variable. The correlation coefficient r is a dimensionless number ranging from -1 to +1. To identify and measure causal relationships, you need a very specific One criterion for a good model is that the coefficients in the model, the intercept and the slope (s) are all statistically significant. The authors cover t-tests, ANOVA and Use multiple regression when you have three or more measurement variables. A value of R 2 close to 1 means that the regression “explains” nearly all of the variation in the response Jan 8, 2024 · Simple logistic regression assumes that the observations are independent; in other words, that one observation does not affect another. Jun 5, 2012 · For example, individual genes or clinical findings can be assigned protective or risk values expressed as log odds. Print. The sample standard deviation of the 14 observations is 3. m is the slope, which tells us the predicted increase BiBTeX EndNote RefMan. Linear, GLM, and Cox regression models are explained with a We explain why traditional regression adjustment fails and discuss the methods of g-computation, sequential stratification, marginal structural models, and structural nested models. Department of Biostatistics Vanderbilt University School of Medicine. This tutorial covers many facets of regression analysis including selecting the correct type of regression analysis, specifying the best model, interpreting the results, assessing the fit of the model, generating predictions, and checking the assumptions. One of the measurement variables is the dependent ( Y) variable. The examples I cite are illustrative of some of the errors in the book. It is important to note that there may be a non-linear association between two The estimated regression equation is that average FEV = 0. Request Digital Evaluation Copy. The special cases of regression with Technical Condition 1, Line: The Poisson model implies that the log of the mean will be a linear function of the explanatory variable: ln(μi) = β0 +β1Xi, ln. Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. Basics of Biostatistics. May 9, 2024 · A parameter multiplied by an independent variable (IV) Then, you build the linear regression formula by adding the terms together. The slope of the line is b, and a is the intercept (the value of y when x = 0). Using data of the given example the straight line is drawn but the point of interception to y-axis is lacking and, therefore, precise nature of the Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. But the Poisson is similar to the binomial in that it can be show that the Poisson is the limiting distribution of a Binomial for large n and small π. Some important formulas used in inferential statistics for regression analysis are as follows: Regression Coefficients: The straight line equation is given as y = α α + βx β x, where α α and β β are regression coefficients. In our example above, R 2 = 0. Taught in English. This site provides detailed information about performing common statistical tests and procedures in Excel including t-tests, ANOVA, repeated measure ANOVA, Correlation, Simple and Multiple Linear Regression, calculating confidence intervals and other descriptive statistics. Oct 7, 2021 · Introduction. The rest of the variables are the independent ( X) variables; you think they may have an effect on the dependent variable. If b xy is positive, then b yx is also positive and vice versa. value of y when x=0. ¯. Day #03: Review 3: Experimental Designs. X is an independent variable and Y is the dependent variable. 1947-2022. The first three chapters cover basic concepts of Statistics focusing on Mean of Y = Pr(Y=1) = π 0≤ π ≤1, and fitted values may fall outside of (0,1). 2. . Ali S. 22 languages available. This model explains 58% of the variation in home prices. Day #04: Correlation Analysis. PRACTICE PROBLEMS FOR SIMPLE LINEAR REGRESSION (Some are new & Some from Old exams; last 4 are from 2010 Midterm) Problem 1: The Pearson Correlation Coefficient (r) between two variables X and Y can be expressed in several equivalent forms; one of which is n i y i x i s y y s x x n r X Y 1 _ _ ( )( ) 1 ( , ) Dec 6, 2006 · These programs typically have one to three courses that focus on biostatistics methods, and Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models is designed to be the primary biostatistics textbook for the second or third of these courses. . Recall the slope-intercept form of a line, y = mx + b. In general, the method of least squares is applied to obtain the equation of the regression line. Keywords: binomial distribution, Hosmer–Lemeshow test, likelihood, likelihood ratio test, logit function, maximum Nov 17, 2014 · The problem of combining data from multiple assays is an important topic in modern biostatistics. Day #05: Estimation of Parameters. Interpret the results of a correlation analysis. Like ANOVA, linear regression is a special case of the general linear model. The logistic function may be used to transform a sigmoidal curve to a more or less straight Feb 15, 2014 · Abstract. . For example, scientists might use different amounts of fertilizer and water on different fields and see how it affects crop yield. Regression analysis can be determined using tools such as R or SPSS to find a relationship between independent variables and outcome. Jul 31, 2023 · d) Regression analysis. This course is part of Biostatistics in Public Health Specialization. Inferential statistics alone do not prove causation. Manuscript Sections. May 31, 2016 · We could use the equation to predict weight if we knew an individual's height. Regression Analysis By Example Using R, 6th Edition. For example if the outcome is mortality, the two outcomes possible are alive or dead. For instance, in the red equation, m = 1 and. Linear regression is a toolkit for developing linear models of cause and effect between a ratio scale data type, response or dependent variable, often labeled “ Y ,” and one or more ratio scale data type, predictor or independent variables, X. validation. e. You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in The regression analysis will then indicate the best set of predictors, from those included, and one can use these to predict the outcome for new cases. For example, we might want to quantify the association between body mass index and systolic blood pressure, or between hours of exercise per week and percent body fat. Model In the current presentation, we consider the data in the form ( ,)T Xy i i, for i = 1, 2, . Biostatistics (also known as biometry) is the application of statistical methods to biology. Section 4 illustrates some practical applications of quantile regression in biostatistics. 00. In summary, while both Statistics and Biostatistics involve the use of statistical methods, the main difference lies in the application of these methods. 57%. Briefly speaking, statistics plays a key role in all phases of a research project starting from the design stage and continuing through the monitoring, data collection, data analysis and interpretation of the Inferential stats allow you to assess whether patterns in your sample are likely to be present in your population. The only difference between simple linear regression and multiple regression is in the number of predictors (“x” variables) used in the regression. Furthermore, because of the simple form of the Poisson Apr 1, 2014 · Logistic regression: In situations in which the response of interest is dichotomous (binary) rather than continuous, linear regression cannot be used to explore the nature of the relationship. 26721 for each one year increase in age (in the observed age range). To know more about Correlation and regression formulas, the difference between correlation and regression with examples, you can visit us at BYJU’S – The Learning App. C regression. - An example uses home size and price data from 10 houses to generate a linear regression equation predicting that price increases by around $110 for each additional square foot. Suppose we have the following dataset with one response variable y and two predictor variables X 1 and X 2: Use the following steps to fit a multiple linear regression model to this dataset. The formula for a multiple linear regression is: = the predicted value of the dependent variable. For instance, for an 8 year old we can use the equation to estimate that the average FEV = 0. HA : b1 ≠ 0. Midterm exam -censored regression regression, in case-control prediction. Step 1: Calculate X 1 2, X 2 2, X 1 Definition of biostatistics Statistics — the science of collecting, describing, analyzing, and interpreting data, so that inferences (conclusions about a population based on data from merely a sample) can be made with quantifiable certainty. Statistics is a broader field that is used in various Simple Regression Analysis in Public Health. One of the early pioneers, Florence Nightingale, the icon of nursing, worked during the Crimean war of the 1850s to improve the methods of constructing mortality tables. 15. ¯x)(y−¯. May 19, 2020 · Linear Regression Real Life Example #3. Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Financial aid available. However, in practice, we hardly go beyond the degree 3. Learn how to solve basic statistical problems with Ron Cody's easy-to-follow style using the point-and-click SAS Studio tasks. However, if we have more groups, this becomes quite complicated. Within this module, an overview of multiple regression will be provided. Use multiple logistic models to understand how risk of kyphosis (curvature of the spine) depends on several predictor variables. Regression analysis is a Regression analysis can be thought of as being sort of like the flip side of correlation. For example: (x 1, Y 1). In this simple linear regression, we are examining the impact of one independent variable on the outcome. However, that’s a minor problem. For example, a researcher studying cancer outcomes may have both gene expression and copy number data for a set of patients. The conclusions from her tables helped to change the practices LINEAR REGRESSION. ISBN: 978-1-119-83089-4 October 2023480 Pages. Interpret the intercept b 0 and slope b 1 of an estimated regression equation. Oct 29, 2015 · A common misinterpretation of linear regression is the 'regression fallacy'. Abstract. At the end of this lesson, you will be able to: Define correlation. Ø For the design of experiments. Obtain the value of the regression coefficients and correlation coefficient. where X is the independent variable and plotted along the x-axis. 5 years for the original seven observations. 6811 which is more than 1. There are two main types: simple linear regression, which involves two variables, and multiple regression, which involves more than two variables. It specifically helps determine how much a dependent variable (Y) is affected by one or more independent variables (X), where: Y – is the dependent variable. They might fit a multiple linear regression model using Correlation and regression analysis are applied to data to define and quantify the relationship between two variables. Describe the Pearson correlation coefficient and the Spearman correlation coefficient. A correlation close to zero suggests no linear association between two continuous variables. The geometric mean between the two regression coefficients is equal to the correlation coefficient. Learning objectives. In their example (p 297), RR=3. Final exam (8:30am – 10:30am) be communicated a tentative may schedule of. where X is plotted on the x-axis and Y is plotted on the y-axis. Objectives. M 1pm-2:50pm Mar 25, 2024 · Logistic Regression: Logistic regression is used when the dependent variable is binary or categorical. 42. Chapter 4: Reading Data from Text Files . 2 we discuss quantile regression model, and section 3 focuses on the inference related to model parameter(s). What's included. Examples are drawn from various biomedical and health-related studies. For example, with 10 groups we R 2, the square of the simple product moment correlation r, can take on values between 0 and 1 (0% to 100%). Starting at just $116. Determine when to use a linear regression analysis. 43. 1 years, larger than the sample standard deviation of 2. 95. One equation for R 2 is given by. Important steps in using this approach include estimation and inference, variable selection in model building, and assessing model fit. Simple regression analysis uses a single x variable for each dependent “y” variable. The central tendency concerns the averages of the values. Finally, section 5 concludes. Starts Jul 16. The book includes many biological and health-related problem sets and is fully compatible with SAS University Edition. Biostatistics. Cox proportional Cox proportional hazards and hazard Adaptive Regression No clas (Memorial Day) design in HIV prevention in statistical Overview. Here, b is the slope of the line and a is the intercept, i. Unfortunately, there are no textbooks which could appropriately fulfill the requirements of the Biostatistics course at the undergraduate level for Health officer and Medical students. Least-Squares Line (Regression Line or Line of Best Fit) 0 1 0 1 11 22 2 0 22 01 note that is the y-intercept aˆ nd is the slope ( )( ) where or ( )( ) ( )( ) ( )( ) where or ( )( ) y x y b bx b n xy x y s b br nx x s and y x x xy b b y bx nx x = + ∑ −∑ ∑ == ∑ −∑ ∑ ∑ −∑ ∑ = = − ∑ −∑ 0 0 00 2 /2 2 2 1 1 11 /2 2 2 Biostatistics has applications in all the branches of life sciences. 47 . Apr 17, 2024 · The regression formula in statistics is a method to estimate or calculate the relation between two or more variables. A linear regression line equation is written in the form of: Y = a + bX. Critically evaluate a multiple linear regression analysis to ensure that substantive findings are appropriate given the data. Table 13. What is the purpose of the Central Limit Theorem in statistics? a) To determine the sample size needed for a study b) To describe the shape of a normal distribution c) To estimate population parameters from sample statistics d) To calculate the variance of a sample. Dec 23, 2017 · Biostatistics: Linear regression and correlation. For many studies, the researchers have more data than they know how to handle. This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. X – is the independent (explanatory) variable. Using the regression equation y x = 2. ( μ i) = β 0 + β 1 X i, which means we’d really like to plot μi μ i as a function of the explanatory variable. Continuous and categorical explanatory variables are considered. 1 below briefly and then continue. 26721 × (8) = 2. It includes the design of biological experiments, the analysis of data from those experiments and the interpretation of the results. The interpretation of the slope is that the average FEV increases 0. π(1- π) Welcome to the third edition of the Handbook of Biological Statistics! This online textbook evolved from a set of notes for my Biological Data Analysis class at the University of Delaware. The objective of a study is to understand the factors that are associated with systolic blood pressure in infants. For example, a correlation of r = 0. Research. k. the effect that increasing the value of the independent variable This chapter describes multiple linear regression, a statistical approach used to describe the simultaneous associations of several variables with one continuous outcome. Digital Evaluation Copy. We firmly believe that this lecture note will fill that gap. After reading this Biostatistics refers to the application of statistical techniques to biologic data collected prospectively and/or retrospectively. Y is the dependent variable and plotted along the y-axis. 8057 or 80. 47 Jan 17, 2023 · Example 2: Logistic Regression Models Biostatisticians often use logistic regression models to predict whether or not individuals or animals will have a particular binary outcome (yes or no). Ø Research is incomplete without the statistics. Aimed specifically at the health sciences, Biostatistics by Example Using SAS Studio, provides an introduction to SAS Studio tasks. For the statistical of the slope, b1, we generally treat the test as a two-tailed test of the null hypothesis that the regression slope is equal to zero. so they can fit a logistic regression model that tells My tutorial helps you go through the regression content in a systematic and logical order. b is the y-intercept, or where the line crosses the y-axis. The variability or dispersion concerns how spread out the values are. Some content may not be translated. That trend (growing three inches a year) can be modeled with a regression Jan 13, 2005 · This review introduces logistic regression, which is a method for modelling the dependence of a binary response variable on one or more explanatory variables. (1). Example: Multiple Linear Regression by Hand. The authors reported that they had limited ability to conclude Introduction. Jan 24, 2022 · Abstract. e. 2 b = 2. The logistic regression model applies a logistic or sigmoid function to the linear combination of the independent variables. For example, we might predict weight W = 71. The author has attempted a mathematical exposition but has not succeeded. Sep 29, 2013 · Regression analysis outputs include coefficients, residuals, and measures of fit like the R-squared value. The book includes many biological and health-related problem Dec 7, 2019 · B. 6+1. Know how to obtain the estimates b 0 and b 1 from Minitab's Topics studied in biostatistics are too numerous to list (for example multivariate regression, analysis of covariance, linear discriminant analysis, principal component analysis, and so forth; therefore, a sample that reflects the kind of tools that are used and most prevalent techniques will be given. Introduction . Hadi, Samprit Chatterjee. The Variance (around the regression line) is not constant (a model violation that we learn in diagnostics); variance is function of the Mean π of Y (which is a function of predictors): σ2 =. There is a free resource pack and example workbooks available to download. Nov 18, 2020 · This tutorial explains how to perform multiple linear regression by hand. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in 2. What is the “Regression” of Y on X ? — Average Y at each value of X. Upon completion of this lesson, you should be able to: Distinguish between a deterministic relationship and a statistical relationship. For example, researchers can measure a variety of variables including weight, height, age, etc. Choose the option "This Collection" under t. A practice quiz is provided to test your knowledge before completing the graded quiz. As will be discussed in Chapters 8 and 9, there are problems with interpreting any regression models, yet compared to the other statistical learning machines we eventually discuss, logistic regression is far easier to interpret. Whereas when data were generated from a perfect additive Cox proportional hazards regression model the inclusion of the interaction between the two covariates resulted in only 2% estimated bias in main effect regression coefficients estimates, but did not alter the main findings of no significant interactions. It has to do with finding the equation for the kind of straight lines you were just looking at. 7 years for the original seven observations. Review: slope-intercept form of a line. #drcjtank#pharmaplanet#biostatistics2. Multiple regression analysis is almost the same as simple linear regression. Author. β = ∑n 1(x−¯. 9 suggests a strong, positive association between two variables, whereas a correlation of r = -0. Ø Every result (data) in the research need to be statistically validated. Systolic blood pressure, weight (ounces) and age (days) are measured in 100 infants. Jul 2, 2024 · Regression is a statistical measure used in finance, investing and other disciplines that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Each additional term can be viewed as another predictor in the regression equation: \(y=\beta_0 + \beta_1x + \beta_2x^2 + \dots + \beta_px^p + \varepsilon\) This allows the fit of more flexible models representing the association between the outcome and some continuous predictors. 26721 × age. 48x the actual values of dependent variable can be worked out. Correlation analysis is used to estimate the strength of a relationship between two variables. Harvard affiliates with an ID number and PIN can get free download of dissertations, both Harvard and other, on the Digital Access to Scholarship at Harvard site via DASH . Click on "By Collections" in the left hand menu under Statistics, and select "FAS Theses and Dissertations" in the list. iq pz ce yl hx wg nb vd eh qd
