QM

Betas (s)

possible answers:
1. Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.

2. The rise of Big Data is due to new technologies and the generation of more information (e.g. from customer cards, in social media, mobile applications etc.)

3. The volumes and varieties of data require new analytic technologies („Analytics“). I.e. it is not just about collecting and storing bigger amounts of data

Data, models, transformation.
Data is the creative use of internal and external data to give you a broader view on what is happening to your operations or your customer.
Modeling is all about using that data to get workable models that can either help you predict better or allow you to optimize better in terms of your business.
And the third success factor is about transforming the company to take advantage of that data in models.

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Two possible solutions listed:
1. For example, you can conduct a survey in which participants are asked to select one of several competing products as their favorite. Using multinomial logistic regression, you can create profiles of people who are most likely to be interested in your product, and plan your advertising strategy accordingly (use case).
2. A telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, you can customize offers for individual prospective customers (use case).

1. Data Management: sample size was n=850 bank clients, consisting out of 700 clients with a default outcome and 150 without. We created a random sample of 70% from the 700 as a model sample and the rest 30% as the validation sample.

2. Model: We entered the dependent binary variable (default) and the other independent variables into the analysis window. Within the optional model items we defined the categorical variable (education) and selected probabilities and group membership to be saved information. We selected Forward Method LR as a stepwise binary regression procedere

We want to be able to identify characteristics that are indicative of people who are likely to default on loans, and use those characteristics to identify good and bad credit risks.

Results:
R-squared value: What constitutes a “good” R-squared value varies between different areas of application. While these statistics can be suggestive on their own, they are most useful when comparing competing models for the same data. The model with the largest R-squared statistic is “best” according to this measure.
The classification table: showed the practical results of using the logistic regression model. For each case, the predicted response is “Yes” if that cases's model-predicted probability is greater than the cutoff value specified in the dialogs (in this case the default of 0.5). Overall, the model showed good results (82% correct classifications).
Parameter estimates: Four significant predictors and coefficient values shown in the fourth step are used by the procedure to make predictions. The meaning of a logistic regression coefficient is not as straightforward as that of a linear regression coefficient.

While B is convenient for testing the usefulness of predictors, Exp(B) is easier to interpret.
Exp(B) represents the ratio-change in the odds of the event of interest for a one-unit change in the predictor.