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Chapter Description and Details
1 The Process of Analytics: DCOVA and I
1.1 What is analytics?
1.2 The process of Analytics
1.2.1 Define
1.2.2 Collect
1.2.3 Organize
1.2.4 Visualize
1.2.5 Analyze
1.2.6 Insight
2 Accessing SAS and R
2.1 Why SAS and R
2.2 History of SAS and R
2.3 Installing SAS and R
3 Data Manipulation using SAS and R: Collecting and Organizing the Data
3.1 Data flow from ERP to Business Analytics SaaS
3.2 Sanity check on data
3.3 Merging datasets
3.4 Missing values, Duplication , Outliers
3.5 Project datamart
4 Discover basic information about data
4.1 Descriptive statistics , measures of central tendency, measures of variation
4.2 How to generate them using:
4.2.1 SAS
4.2.2 R
5 Visualisation
5.1 Graphs and charts
5.2 How to create effective graphs and charts using:
5.2.1 SAS
5.2.2 R
5.3 Correlation and co-variance
5.4 How to create graphical and numeric outputs for correlation and co-variance using
5.4.1 SAS
5.4.2 R
6 Analyze: Probability and Distributions
6.1 Concepts of probability
6.2 How to generate probability using:
6.2.1 SAS
6.2.2 R
6.3 Concepts of distributions
6.4 Normal distributions
6.5 How to work on distributions using:
6.5.1 SAS
6.5.2 R
7 Analyze: Sampling and Sampling Distributions
7.1 Sampling and sampling distributions
7.2 Hypothesis testing
7.3 How to work on sampling and hypothesis testing using:
7.3.1 SAS
7.3.2 R8 Analyze: Confidence Interval
8.1 Concept of confidence interval
8.2 How to work in confidence intervals using
8.2.1 SAS
8.2.2 R
9 Insight Generation
9.1 What type of conclusions can be drawn by the analysis
9.2 Interpreting results generated by:
9.2.1 SAS
9.2.2 R
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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