A self-guided tour to help you find and analyze data using Stata, R, Excel and SPSS. The goal is to provide basic learning tools for classes, research and/or professional development

- Introduction to Econometrics byCall Number: HB139 .S765 2019ISBN: 9780134461991Publication Date: 2018-11-06For courses in introductory econometrics. This package includes MyLab Economics. Engaging applications bring the theory and practice of modern econometrics to life Ensure students grasp the relevance of econometrics with Introduction to Econometrics -- the text that connects modern theory and practice with motivating, engaging applications. The 4th Edition maintains a focus on currency, while building on the philosophy that applications should drive the theory, not the other way around. The text incorporates real-world questions and data, and methods that are immediately relevant to the applications. With very large data sets increasingly being used in economics and related fields, a new chapter dedicated to Big Data helps students learn about this growing and exciting area. This coverage and approach make the subject come alive for students and helps them to become sophisticated consumers of econometrics. Also available with MyLab Economics By combining trusted author content with digital tools and a flexible platform, MyLab(tm) personalizes the learning experience and improves results for each student. Note: You are purchasing a standalone product; MyLab Economics does not come packaged with this content. Students, if interested in purchasing this title with MyLab Economics, ask your instructor to confirm the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab Economics, search for: 0134610989 / 9780134610986 Introduction to Econometrics Plus MyLab Economics with Pearson eText -- Access Card Package, 4/e Package consists of: 0134461991 / 9780134461991 Introduction to Econometrics 0134543939 / 9780134543932 MyLab Economics with Pearson eText -- Access Card -- for Introduction to Econometrics
- Data analysis using regression and multilevel / hierarchical models byCall Number:

HA31.3 .G45 2007ISBN: 052168689X - An R Companion to Applied Regression byCall Number: QA278.2 .F628 2011ISBN: 141297514XPublication Date: 2011
- Unifying political methodology : the likelihood theory of statistical inference byISBN: 9780472022519Publication Date: 1998
- Econometric analysis of panel data byCall Number: HB139 .B35 2021ISBN: 9783030539528Publication Date: 2021
- Longitudinal and panel data : analysis and applications in the social sciences byCall Number:

HA29 .F6816 2004ISBN: 0521535387 - Statistical analysis : an interdisciplinary introduction to univariate & multivariate methods byCall Number:

QA278 .K323 1986ISBN: 0942154991

- Cubic interpolation in RHow and When to use Cubic Interpolation in R
- Data Analysis Examples: R"The pages below contain examples (often hypothetical) illustrating the application of different statistical analysis techniques using different statistical packages"
- Descriptive Statistics in RThis guide focuson on descriptive analysis in R and touch on some basic data cleaning prior to your analysis.
- Differences-in-differences using RBasic differences-in-differences estimation (R)
- Econometrics using RBrief intro to cross-sectional regression, time series, and data manipulation in R
- Fitting distributions wiht R"Fitting distributions consists in finding a mathematical function which represents in a good way a statistical variable."
- Granger causality"A variable X Granger-causes Y if Y can be better predicted using the histories of both X and Y than it can using the history of Y alone."
- Introducing R"R is a powerful environment for statistical computing which runs on several platforms. These notes are written specially for users running the Windows version, but most of the material applies to the Mac and Linux versions as well. "
- Introduction to RStudioRStudio is an integrated development environment (IDE) for R. It provides a user friendly interface to R.
- Linear Regression using RIntroduction to linear regression and diagnostics using R.
- Logit, Ordered Logit, and Multinomial Logit Models in R: A Hands-on TutorialAn introductory guide to estimate logit, ordered logit, and multinomial logit models using R
- Merge/Append using RMerge, append using R
- Missing Data/Imputation - RBrief guide to handle missing data and imputation in R
- Quick-R: Data Management"Once you have access to your data, you will want to massage it into useful form. This includes creating new variables (including recoding and renaming existing variables), sorting and merging datasets, aggregating data, reshaping data, and subsetting datasets (including selecting observations that meet criteria, randomly sampling observeration, and dropping or keeping variables). "
- Quick-R: Frequencies"This section describes the creation of frequency and contingency tables from categorical variables, along with tests of independence, measures of association, and methods for graphically displaying results."
- Quick-R: Generalized Linear ModelsExamples for logistic, Poisson, survival
- Quick-R: Home Page"R is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve. I created this website for experienced users of popular statistical packages such as SAS, SPSS, Stata, and Systat (although current R users should also find it useful). My goal is to help you quickly access this language in your work. "
- Random Coefficient Models for Longitudinal DataExamples using Stata, SPSS, SAS and R
- Reshape data using RReshape long to wide and wide to long in R
- Resources to help you learn and use R (UCLA)Data analysis using R
- R Programming/Data Management"In this section, we deal with methods to read, manage and clean the data. Data can be stored in a large variety of R objects (vectors, lists, dataframes, etc). However, it is usual to store them in a dataframe and this is the reason why we focus here on dataframes."
- The R Project for Statistical Computing"R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. "
- R Time Series TutorialBrief intro to time series analysis
- Time Series in R (Princeton)TIme series analysis in R
- Unit Roots and Cointegration"This issue focuses on time series models, with special emphasis on the tests of unit roots and cointegration. I am providing instructions for both R and STATA. I would like to remark that the theoretical background given in class is essential to proceed.
- Using stargazer in R to make nice output tablesUsing Stargazer in R to report regression outputs, summary statistics.
- Video tutorials for R"How to do stuff in r. two minutes or less"
- Zelig tutorialData analysis using Zelig (R) by Gary King, Harvard University

- Data Visualization in RIntroduction to data visualization in R.
- Quick-R: Basic Graphs"One of the main reasons data analysts turn to R is for its strong graphic capabilities."
- R Graph GalleryA collection of graphs using R (code provided). Previous site http://rgraphgallery.blogspot.com/

- Getting started with the `boot' package in R for bootstrap inference"The package boot has elegant and powerful support for bootstrapping. In order to use it, you have to repackage your estimation function as follows."
- Quick-R: Bootstrapping"The boot package provides extensive facilities for bootstrapping and related resampling methods. You can bootstrap a single statistic (e.g. a median), or a vector (e.g., regression weights). This section will get you started with basic nonparametric bootstrapping."

- Zelig tutorial"Zelig is a single, easy-to-use program that can estimate, help interpret, and present the results of a large range of statistical methods. It literally is "everyone's statistical software" because Zelig uses (R) code from many researchers."
- Zelig: Everyone’s Statistical SoftwareIntroduction to Zelig based on the paper by Kosuke Imai, Gary King, and Olivia Lau. "Toward A Common

Framework for Statistical Analysis and Development", Journal of Computational and Graphical Statistics, Vol. 17, No. 4 (December), pp. 892-913

- First Steps in RBy Seth Falcon & Martin Morgan.
- The Split-Apply-Combine Strategy for Data Analysis"Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. This insight gives rise to a new R package that allows you to smoothly apply this strategy, without having to worry about the type of structure in which your data is stored.

The paper includes two case studies showing how these insights make it easier to work with batting records for veteran baseball players and a large 3d array of spatio-temporal ozone measurements."