Welcome to ERP-Statistics Lab!


STATA / Enroll on this course




Course info

SPSS is a widely used program for statistical analysis in social science. It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, data miners, and others.


  • First, tips and hints for the best WORKFLOW showing how to use commands step-by-step.
  • Manage, edit and structure large databases ready for data analysis, up to 2 billion observations*.
  • Generate descriptive statistics, summary tables, cross tabulations, frequencies and much more.
  • Create powerful publication-quality graphs showcasing hidden info insights, edit and combine them in order to send clear messages.
  • Do your own statistical models in order to predict and/or forecast your key interest variables or events.
  • Complete guidance and orientation, the course is made into sections that show a natural progression order.
  • Quizzes after each section, so you have a way to keep practicing on your own.
  • The course is made in sections with lectures that grow into several Data Analysis/Estimations Projects considering all lectures and workflow taught in order for full grasp of STATA capabilities.
  • Last but not least, be sure to look at the previews.
  • According to STATA official capabilities (http://www.stata.com/new-in-stata/huge-datasets/)

Requirements

  • STATA (IC, SE or MP) version 12 or higher.
  • Microsoft Office (2003 or higher) or OpenOffice
  • Desire to master Data Analysis and STATA !

Who is the target audience ?

• Undergraduate and graduate students needing to do quantitative analysis for your own requirements
• Some of the careers that nowadays need quantitative analysis are : Biostatistics, Business Administration, Economics, Education, Epidemiology, Finance, Marketing Research, Medical Research, Political Science, Public Health, Public Policy, Sociology and many others.


STATA Course outlines :

1. Creating a working dataset from raw data

  • Stata environment, help files, and third-party packages
  • Using do-files : commenting
  • Importing data : excel, CSV, public data source
  • Data structure : wide vs long
  • Cleaning the dataset : missing values, recode, renaming, and labeling
  • Utilizing loops
  • Describing the data : summarize
  • Using log files

2. Data manipulation

  • Generating new variables : gen, egen, xtiles
  • Using the by command
  • Logical expressions
  • Using preserve and restore
  • Keeping or dropping variables
  • Creating dummy variables
  • Combining datasets
  • Using the collapse command

3. Basic statistical routines

  • Creating a summary statistics
  • Correlation
  • Student t-test
  • Cross tabulation and Chi-squared test

4. Graphics

  • Scatter plot
  • Line plot
  • Histogram
  • Bar graph
  • Pie chart
  • Fitted regression line
  • Overlaying two graphs : twoway
  • Combining two graphs : graph combine

5. Regression Analysis

  • Model specification
  • Using the reg command
  • Analytical weights
  • Estimation of standard errors : robust, cluster, bootstrap
  • Interaction terms
  • Analysis of marginal effects : margins and marginsplot
  • Using esttab to export regression results to .docx or LaTex

Price

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