Getting Started with This Product

What are the major differences between Subscription and Version 25?

Subscription is the newest way to buy SPSS Statistics. It has simpler packaging, easier downloads, easier licensing via IBMID and monthly subscription pricing. New features are added to Subscription first, making it easier to keep software up-to-date and enjoy the latest product features.

Are all add-ons included in the trial? What features are included in the trial?

The trial includes all add-ons as well as all the features and functionality of SPSS Statistics Subscription.

How long does the trial last?

The trial period is for 14 days, beginning from the time you complete registration.  However should you need longer, you have the choice of the subscription model which allows you to pay by month, therefore allowing you to conclude at any point should you decide it will not meet your needs.

Is it possible to extend my trial?

Trials cannot be extended past the 14 day trial period. It begins from the time you complete registration.  However should you need longer, you have the choice of the subscription model which allows you to pay by month, therefore allowing you to conclude at any point should you decide it will not meet your needs.

Can I buy SPSS Statistics Subscription for a single month?

Yes.  Subscription renews automatically each month by default. You can prevent this by logging into MyIBM at myibm.ibm.com.  Find SPSS Statistics Subscription and click on “Manage.” In the "Overview" section under "Plan details" click "Cancel plan" and follow the prompts.

How can I check the status of my trial or subscription?

To check the status of your subscription, log into MyIBM at myibm.ibm.com.

After my Subscription trial has ended, do I need to re-install the purchased version of the software?

No, you do not need to re-install as long as you use the same IBMID for both trial and purchase. Subscription will automatically convert from the trial to the purchased version.

Pricing & Purchase

How much does IBM SPSS Statistics cost?

There are a number of purchase options for your consideration.  Please check them out at the IBM Marketplace (https://www.ibm.com/marketplace/spss-statistics/purchase)

Where do I go to Buy SPSS Statistics?

Visit the IBM Marketplace (https://www.ibm.com/marketplace/spss-statistics/purchase)

Can I buy SPSS Statistics Subscription for a single month?

Yes.  Subscription renews automatically each month by default. You can prevent this by logging into MyIBM at myibm.ibm.com.  Find SPSS Statistics Subscription and click on “Manage.” In the "Overview" section under "Plan details" click "Cancel plan" and follow the prompts.

My previous subscription ended (or I cancelled).  How do I reactivate my subscription?

You can reactivate a cancelled or lapsed subscription by repurchasing the software using the same IBMID as before.

Can I buy Subscription by paying per year instead of per month?

Yes. Please contact an IBM seller and they will assist with additional purchase options. You can find contact options in the “Lets Talk” option on the right hand side of your screen at the IBM Marketplace (https://www.ibm.com/marketplace/spss-statistics)

Logistic Regression

“What is logistic regression?”:

Logistic regression (also known as logit model) is a type of statistical analysis often used for predictive analytics and modeling, and extends to applications in machine learning. In logistic regression the dependent variable is finite or categorical, either A or B (binary logistic regression) or a range of finite options A, B, C or D (multinomial logistic regression). It is used in statistical software to understand the relationship between the dependent variable and one or more independent variables by estimating probabilities using a mathematical logistic function.

How does logistical regression help in prediction?

Logistic regression analysis can help you predict the likelihood or probability of an event happening or a choice being made. For example, you may want to know the likelihood of a visitor of choosing the offer made on the site—or not (dependent variable). Logistic regression can look at known characteristics of visitors—sites they came from, repeat visits to your site, behavior on your site (independent variables) and determine a probability that tells you what type of visitors are likely to accept your offer—or not. As a result, you can make better decisions about promoting your offer or the offer itself.

Machine learning uses statistical concepts to enable machines (computers) to “learn” without explicit programming. Logistic regression fits in best when the task that the machine is learning is based on two values or a binary classification. Using the example above, your computer could use logistic regression to make determinations about promoting your offer and take actions all by itself. And, it could learn how to do it better over time as more data is provided.

What are some types of predictive models that use logistic regression:

• Generalized linear model
• Discrete choice
• Multinomial logit
• Mixed logit
• Probit
• Multinomial probit
• Ordered logit

Is logical regression critical in predictive models?

Predictive models built using logistic regression can make a positive difference in your business or organization by helping you understand relationships and predict outcomes to improve decision making. For example, a manufacturer can use logistic regression as part of a statistics software package to discover a probability between part failures in machines and the length of time those parts are held in inventory, they can decide to adjust delivery schedules or installation times to eliminate future failures.

How is logistic regression useful for businesses?

In medicine, logistic regression can be used to predict the likelihood of disease or illness for a given population and preventative care can be put in place. Businesses can use it to uncover patterns that lead to higher employee retention or create more profitable products by analyzing buyer behavior. In the business world, logistic regression, and an array of other analyses, is applied by data scientists whose role is to analyze and interpret complex digital data.

Although multinomial logistic regression can help when you are looking at a range of categorical outcomes, A, B, C or D. It is binary logistic regression—yes or no, present or absent—that is more often used. Although the outcomes are constrained, the possibilities are not. Binary logistic regression can be used to examine everything from baseball statistics to landslide susceptibility to handwriting analysis.

Logistic regression also proves useful for a range of statistical concepts and applications:

• Text analytics
• Chi-square automatic interaction detection (CHAID)
• Conjoint analysis
• Bootstrapping statistics
• Nonlinear regression
• Cluster statistics and cluster analysis software
• Monte Carlo simulation
• Descriptive statistics

When applying statistical analysis software for approaches such as logistic regression, multivariate analysis, neural networks, decision trees and linear regression, hardware and cloud-computing solutions should also be considered to accommodate large data sets either on premises, in the cloud or in a hybrid cloud configuration.

When do we use different types of logistic regression?

Multinomial can be used to classify subjects into groups based on a categorical range of variables to predict behavior. For example, you can conduct a survey in which participants are asked to select one of several competing products as their favorite. You can create profiles of people who are most likely to be interested in your product, and plan your advertising strategy accordingly.

Binary is most useful when you want to model the event probability for a categorical response variable with two outcomes. A loan officer wants to know whether the next customer is likely to default—or not default—on a loan. Binary analysis can help assess the risk of extending credit to a particular customer.

Can logistic regression be ineffective? Are there situations when logistic regression becomes ineffective?

It is also helpful to understand when logistic regression might be ineffective*. Here are some hazards to watch out for:

• Independent variables must be valid. Incorrect or incomplete variables will degrade a model’s predictive value.
• Avoid continuous outcomes. Temperatures, time, anything that is open ended will make the model much less precise.
• Do not use inter-related data. If some observations are related to one another, the model will tend to overweight their significance.
• Be wary of overfitting or overstatement. Logistic regression and statistical regression analysis models are precise, but the accuracy is not infallible or without variance.

What are some of the use cases for Logistic Regression?

Use case: Using Binary Logistic Regression to Assess Credit Risk

You are a loan officer at a bank and you want to identify characteristics of people who are likely to default on loans, and use those characteristics to identify good and bad credit risks. You have data on 850 customers. The first 700 are customers who were previously given loans. See how you can use a random sample of these 700 customers to create a logistic regression model and classify the 150 remaining customers as good or bad risks.

“What is linear regression?”:

The statistical concept, linear regression is a linear approach for modeling the relationship between a scalar dependent variable y and one or more or independent variables denoted X.

https://cdn-images-1.medium.com/max/2000/1*oXPGYqgTeIn0Ey3SWgkbsA.jpeg

Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.

Linear regression estimates the coefficients of the linear equation, involving one or more independent variables that best predict the value of the dependent variable.  Linear regression fits a straight line or surface that minimizes the discrepancies between predicted and actual output values.

 Why is linear regression important?

• SPSS regression with default settings results in four tables. The most important table is the Coefficients table.

https://spss-tutorials.com/img/spss-linear-regression-output-coefficients-1.png

• http://www.knowswhy.com/why-is-simple-linear-regression-important/

Linear regression models are relatively simple and give an easily interpreted mathematical formula for generating predictions. Because linear regression is a long-established statistical procedure, the properties of these models are well understood. Linear models are also typically very fast to train. 

How can data be analysed using linear regression?

While performing linear regression, you need to make sure that your data can be analyzed using linear regression. The reason for this is your data must pass through certain assumptions that are required for linear regression analysis. Let’s quickly make a note of how you can check for these assumptions.

1. The variables should be measured at a continuous level. Examples of continuous variables are time, sales, weight and test scores. You can quickly view and change your variable measurements using the statistical software, IBM® SPSS® Statistics.
2. Use a scatterplot to quickly find out if there is a linear relationship between those two variables.
3. The observations should be independent of each other (there should be no dependency).
4. Your data should have no significant outliers. You can request information on outliers using the Explore feature of IBM SPSS Statistics.
5. Check for homoscedasticity – a statistical concept where the variances along the best fitted line remains similar all through that line.
6. The residuals (errors) of the best fitted regression line follows normal distribution.

Are there demos of performing linear regression?

Using IBM® SPSS® Statistics Base

https://www.ibm.com/support/knowledgecenter/en/SSLVMB_24.0.0/spss/tutorials/catreg_carpet_linear.html

Using IBM® SPSS® Spark Machine Learning Library

https://datascience.ibm.com/docs/content/analyze-data/spss-viz-linear.html

Using IBM® Cognos® Statistics

https://www.ibm.com/support/knowledgecenter/en/SSRL5J_1.0.1/com.ibm.swg.ba.cognos.ug_cr_rptstd.10.1.1.doc/t_id_rs_stats_linxmpl.html