배울 내용 ; Enjoy CLEAR and CONCISE step by step lessons from a best selling Udemy instructor in FULL HD 1080 P ; Transform Data into INSIGHT and INTELLIGENCE using powerful methods of analysis, techniques and tools ; Learn data analysis using easy to master drag and drop techniques - NO CONFUSING FORMULAS, macros or VBA ; Learn 8 different techniques for DATA ANALYSIS that can be easily implemented in Excel (2010 - Office 365)
원서번역서 내용 엿보기 ; ▶ 이 책은 범주형 자료분석 개론을 다룬 이론서입니다. 범주형 자료분석 개론의 기초적이고 전반적인 내용을 학습할 수 있습니다.
Download Citation | An Introduction to Categorical Data Analysis, Second Edition | Interpreting the Logistic Regression ModelInference for Logistic RegressionLogistic Regression with Categorical Pr...
An Introduction to categorical data analysis. Wiley series in probability and statistics (2nd... Solutions manual to accompany modern regression methods. Wiley series in probability and...
Download Citation | Analysis of Categorical Data | Introduction Statistical Inference for One Sample Inference of Independent Samples Ordered Categorical Data Combining Categorical Data Model-Based...
자료유형 : 단행본, 서명/저자사항 : An Introduction to Categorical Data Analysis AGRESRI,ALAN. 개인저자 : AGRESRI,ALAN, 발행사항 : 미국 : JOHN WILEY & SONS,INC, 1996, 형태사항 : 296 p. ISBN : 0471113387, 분류기호 : 310.112, 언어 : 영어
This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categoric...
A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics.H...
학습 내용 ; Create an account with Microsoft Office 365 online and upload a document. ; Use sorting and filtering tools to perform simple data analysis. ; Use functions such as IF, VLOOKUP and create PivotTables to perform more advanced data analysis.
Clustering and dimension reduction techniques, which help create graphical displays of high-dimensional data containing many variables. ; Univariate visualization of each field in the raw dataset, with summary statistics. ; Bivariate visualizations and summary statistics that allow you to assess the relationship between each variable in the dataset and the target variable you’re looking at. ; Multivariate visualizations, for mapping and understanding interactions between different fields in the data.