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Download Business Analytics Using SAS Enterprise Guide and SAS by Olivia Parr-Rud PDF

By Olivia Parr-Rud

This educational for facts analysts new to SAS company advisor and SAS firm Miner offers useful adventure utilizing strong statistical software program to accomplish the categories of industrial analytics universal to so much industries.

Today’s companies more and more use info to force judgements that maintain them aggressive. specifically with the inflow of huge facts, the significance of knowledge research to enhance each size of commercial can't be overstated. info analysts are accordingly famous; although, many hires and potential hires, even if gifted with recognize to enterprise and facts, lack the information to accomplish company analytics with complicated statistical software program.

Business Analytics utilizing SAS company advisor and SAS company Miner is a beginner’s advisor with transparent, illustrated, step by step directions that would lead you thru examples in accordance with enterprise case stories. you'll formulate the company goal, deal with the information, and practice analyses so that you can use to optimize advertising and marketing, possibility, and buyer dating administration, in addition to company approaches and human assets. issues comprise descriptive research, predictive modeling and analytics, consumer segmentation, industry research, share-of-wallet research, penetration research, and company intelligence.

This publication is a part of the SAS Press software.

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Extra resources for Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner: A Beginner's Guide

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Although the model might be large and difficult to interpret, the extra variables do not detract from the power or fit of the model. In some businesses where analytic resources are scarce and CPU power is abundant, using every variable is often the best option. Chapter 3: Overview of Descriptive and Predictive Analyses 45 Multicollinearity Another common question raised among predictive modelers is, should I worry about multicollinearity? Multicollinearity exists when two or more independent, or predictive, variables are correlated with each other.

33 Predictive Analyses ........................................................................................... 35 Linear Regression ............................................................................................................. 36 Logistic Regression .......................................................................................................... 39 Neural Networks ...............................................................................................................

Explore the data. Modify the data and variables. Build the model. Assess the model. If you follow this sequence, you will likely be successful in your model-building efforts. Step 1: Select the Data Once you have defined your objective, you need to get data for developing your model. Be sure it correlates with the business purpose for building the model. The data can come from a variety of sources. In some cases, data will need to be combined. The specific details of the data selection are highly specific to the project.

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