Training on “Analysis of Statistical Data and Presentation of Findings Using SPSS”

Training on “Analysis of Statistical Data and Presentation of Findings Using SPSS”

Dates: 25/07/2013 / 12:00 AM
Location: Abovyan 52, YSU VI building, room 305
Training on “Analysis of Statistical Data and Presentation of Findings Using SPSS”
Training on
“Analysis of Statistical Data and Presentation of Findings Using SPSS” 
to be delivered by
 
Vahe Movsisyan
(PhD in Economics, Central Bank of Armenia)
Dates: July 25 and 26, 2013
Time: 10:00 – 14:30
Location: CRRC-Armenia, 52 Abovyan St., YSU 6th building, 3rd floor, room 305
Language: Armenian

 

Course description. The goal of this course is to teach statistical analysts and other
interested participants how to use SPSS tools for solving different applied
problems and familiarize them with the basics of SPSS software.
About the trainer. Vahe Movsisyan works as Economist-Statistician of Statistics Department at the Central Bank of Armenia. In 2007, Mr. Movsisyan received his Ph.D from Moscow State University of Economics, Statistics and Informatics (MESI). Mr. Movsisyan is a member of International Statistical Institute (ISI) and International Association of Survey Statisticians (IASS).
 
 
Registration. CLOSED

 

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Course outline
Day 1
 
Session 1 (10:00 – 12:00)
Types of statistical data and scopes of statistical methods. Inputting and importing data to SPSS. Definition of data (variables) properties. Missing values exploration and replacement. Qualitative variables’ recoding in SPSS. Computing new variables in SPSS. Splitting files and selecting cases in SPSS.
Coffee – Break (12:00 – 12:30)
Session 2 (12:30 – 14:30)
Descriptive statistics. Frequencies, Central tendency and variation measures in SPSS. Crosstabs and hypothesis testing of qualitative variables. Graphical presentation of different types of data.
 
Day 2 
Session 1 (10:00 – 12:00)
Mean difference hypothesis testing in SPSS. One-Way ANOVA analysis.
Coffee – Break (12:00 – 12:30)
Session 2 (12:30 – 14:30)
Linear relationship between variables (correlation analysis). Bivariate, multivariate and partial coefficients of correlation. Simple and multiple linear regressions. Curve fitting.