Course Overview
This course provides a comprehensive introduction to the analysis of descriptive data using two powerful tools: Microsoft Excel and IBM SPSS. Designed for beginners and intermediate users, the course covers essential concepts and practical skills necessary to effectively organize, analyze, and interpret descriptive statistics. Participants will learn how to leverage the functionalities of Excel and SPSS to manage data sets, perform descriptive analyses, and present their findings clearly and concisely.
Course Objectives:
By the end of this course, participants will be able to:
- Understand the fundamental concepts of descriptive statistics.
- Utilize Microsoft Excel to perform basic data analysis tasks.
- Employ IBM SPSS for more advanced statistical analysis.
- Interpret and present descriptive statistical results effectively.
- Develop proficiency in data visualization techniques using Excel and SPSS.
Target Audiance
- The meticulous analyst who seeks to refine their Excel skills for comprehensive data interpretation.
- The eager novice, absorbing SPSS techniques to unlock the mysteries of descriptive data analysis.
- The corporate executive, recognizing the power of data-driven decision-making and diving into Excel and SPSS with enthusiasm.
- The inquisitive researcher, eager to master both Excel and SPSS for nuanced statistical exploration.
Schedule Dates
Analysis of Descriptive data using MS excel and IBM SPSS
Analysis of Descriptive data using MS excel and IBM SPSS
Analysis of Descriptive data using MS excel and IBM SPSS
Analysis of Descriptive data using MS excel and IBM SPSS
Course Content
- Introduction to descriptive analysis
- Basics of descriptive statistics
- Types of variables
- Data collection methods
- Frequencies and percentages
- Common statistical measures
- Mean
- Median
- STDEV and variance
- Range
- Measures of associations
- Applications using MS Excel
- Applications using IBM SPSS
- Frequency tables
- Crosstabs
- Complex crosstabs
- Data summarization
- Applications using MS Excel
- Applications using IBM SPSS
- Statistical measures for grouped data
- Exercises
- Multi-response and one-response data
- Identify unusual data
- Identify duplicated data
- Merging data from multiple files
- Compare datasets
- Sort, split and select
- Restructure data
- Data aggregations
- Recoding to same and different variables
- Visual binning
- Optimal binning
- Statistical measures for grouped data
- Applications using MS Excel
- Applications using IBM SPSS
- Exercises
- Detecting missing data
- Imputation for missing data
- Single imputation
- Multiple imputation
- Outlier detection
- Applications using MS Excel
- Applications using IBM SPSS
- Exercises
- Building charts principals
- Types of charts
- Simple and complex charts
- Applications using MS Excel
- Applications using IBM SPSS
- Review course objectives and suggestions
FAQs
Descriptive data analysis involves summarizing and interpreting data to describe the basic features of the dataset. It includes measures such as mean, median, mode, standard deviation, and frequency distributions.
Descriptive analysis provides valuable insights into the characteristics of the data, helping researchers and analysts understand the central tendency, variability, and distribution of the variables under study. It forms the foundation for further inferential analysis.
MS Excel offers a range of functions and tools for descriptive analysis, including formulas for calculating basic statistics, pivot tables for summarizing data, and charts for visualizing distributions and trends.
IBM SPSS is a powerful statistical software that provides advanced capabilities for descriptive analysis. It offers a user-friendly interface for generating descriptive statistics, creating frequency tables, and conducting exploratory data analysis.
Common descriptive statistics include measures of central tendency (mean, median, mode), measures of variability (standard deviation, variance), measures of distribution shape (skewness, kurtosis), and measures of association (correlation coefficients).