މި އެޖެންސީގެ މުވައްޒަފުންނަށް "ސްޓެޓިސްޓިކަލް ޕެކޭޖް ފޯރ ސޯޝަލް ސައިންސެސް (އެސް.ޕީ.އެސް.އެސް) ޓްރެއިނިންގ" ކަމުގައިވާ "ބޭސިކް ޓްރެއިނިންގ އޮފް އެސް.ޕީ.އެސް.އެސް" އަދި "އެޑްވާންސް ޓްރެއިނިންގ އޮފް އެސް.ޕީ.އެސް.އެސް" 01 އޮކްޓޫބަރު 2015 އިން 30 ނޮވެންބަރު 2015 އަށް މި އެޖެންސީގައި ހިންގުމަށް ބޭނުންވެއެވެ. ވީމާ، މިކަމަށް ޝައުޤުވެރިވެލައްވާ ފަރާތްތަކުން 30 އޮގަސްޓު 2015 ވާ އާދީއްތަ ދުވަހުގެ 10:00 އަށް މި އެޖެންސީއަށް ވަޑައިގެން މަޢުލޫމާތު ސާފުކުރެއްވުމަށްފަހު 01 ސެޕްޓެންބަރު 2015 ވާ ބުރާސްފަތި ދުވަހުގެ 10:00 އަށް މި އެޖެންސީއަށް ވަޑައިގެން އަންދާސީހިސާބު ޕްރޮޕޯޒަލް ހުށަހެޅުއްވުން އެދެމެވެ. އަދި މަޢުލޫމާތު ސާފުކުރެއްވުމަށް ވަޑައިނުގަންނަވާ ފަރާތްތަކާއި އަންދާސީހިސާބު ޕްރޮޕޯޒަލް ހުށަހެޅުމަށް ދީފައިވާ ގަޑިއަށް ވަޑައިނުގަންނަވާ ފަރާތްތަކަށް އަންދާސީހިސާބު ޕްރޮޕޯޒަލް ހުށަނޭޅުއްވޭނެ ވާހަކަ ދަންނަވަމެވެ.

އަދި ދަންނަވަމެވެ. މި އިޢުލާނާ ގުޅޭ އިތުރު މަޢުލޫމާތު ތިރީގައި މިވަނީއެވެ. 

SPSS-BASIC TRAINING CONTENTS

Introduction to Statistics Using SPSS

• Introduction to estimate and hypothesis testing
• One sample tests: t-test, sign and signed rank tests
• Two-sample tests: t-test, Mann-Whitney test
• Three or more samples
• To enter categorical and continuous data
• To define and label variables
• To add variables and cases
• To transform, recode and compute variables
• To select appropriate simple plots and descriptive statistics
• To do cross tabulation and the chi-square test.
• Introduction to statistical testing and estimation.
• Exploratory data analysis and checking the assumptions: box-plots, histograms, PP plots.
• One sample tests: t-test, sign and signed rank tests.
• 2-sample tests: t-test, Mann-Whitney test.

Data Management & Statistical Analysis Using SPSS

Data entry and manipulation

• Enter categorical and continuous data
• Define and label variables
• Transform, recode and compute variables
• Help files

Restructuring data

• Merging files
• Syntax and output

 Graphs and tables

• Graphs such as histograms, QQ plots and box-plots
• Descriptive statistics
• Cross tabulation (two-way tables)

SPSS ADVANCED TRAINING CONTENTS

Exploratory data analysis: ways of presenting Data
Tables.
'Summary statistics' for describing data numerically.
Histograms, frequency plots, boxplots, scatterplots.

Statistical inference
Estimation.
Confidence intervals.
Principles of hypothesis testing.

Z-tests/ scores and t-tests
Extends the comparison of two sets of data from large data sets (studied in the first year) to the small data sets more often met in real research.

Analysis of Variance (i.e. 'ANOVA') and Chi2 tests
Comparisons between more than two sets of data (ANOVA).
Use of Chi2 to test categorical data (that is data such as 'how many people received the new drug, how many were cured, how many died').

Linear regression
How to draw the best line of fit to data.
How to describe how well the data fit the line, and how reliable the line could be in predictions.

Binary/ Multivariate Logistic Regression

How to compare two groups of data and factors related to each group

How to put confounding factors and covariates into the model and analyze data.

I Survival Analysis.

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