We may earn an affiliate commission when you visit our partners.
EDUCBA
This course provides a practical and applied introduction to logistic regression and supervised learning using IBM SPSS Statistics. Designed for learners seeking to build analytical skills in predictive modeling, the course emphasizes both conceptual understanding and tool-based execution.
Through step-by-step instruction, learners will identify key components of logistic regression, configure data within SPSS, and construct predictive models using real-world case studies. They will analyze model outputs, evaluate predictor significance, and interpret statistical results to make informed decisions.
Read more
This course provides a practical and applied introduction to logistic regression and supervised learning using IBM SPSS Statistics. Designed for learners seeking to build analytical skills in predictive modeling, the course emphasizes both conceptual understanding and tool-based execution.
Through step-by-step instruction, learners will identify key components of logistic regression, configure data within SPSS, and construct predictive models using real-world case studies. They will analyze model outputs, evaluate predictor significance, and interpret statistical results to make informed decisions.
Read more
This course provides a practical and applied introduction to logistic regression and supervised learning using IBM SPSS Statistics. Designed for learners seeking to build analytical skills in predictive modeling, the course emphasizes both conceptual understanding and tool-based execution.
Through step-by-step instruction, learners will identify key components of logistic regression, configure data within SPSS, and construct predictive models using real-world case studies. They will analyze model outputs, evaluate predictor significance, and interpret statistical results to make informed decisions.
The course integrates Excel-based logistic modeling and reinforces learning through guided examples such as heart pulse analysis and smoking behavior classification. By the end, learners will be able to confidently apply logistic regression methods to structured datasets, assess model performance using statistical evidence, and communicate findings through SPSS-generated outputs.
Register for this course and see more details by visiting:
OpenCourser.com/course/hcpb5t/spss
Two deals to help you save
Save money when you learn. Here are two deals and offers that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until July 14
Coursera Plus Annual
Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.
Up to
40%
off
Get offer
Valid until August 30
Google AI App Builder
Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind
Take
100%
off
Get offer
What's inside
Syllabus
Logistic Regression & Supervised Learning using SPSS
This module introduces learners to the foundational principles of logistic regression and equips them with hands-on skills in SPSS for managing variables, configuring the data environment, and interpreting statistical outputs. Learners will explore both theoretical concepts and practical applications, including variable setup, SPSS navigation, model output interpretation, and foundational logistic modeling techniques. By the end of this module, learners will be capable of preparing, analyzing, and interpreting logistic regression models using SPSS and supporting tools like MS Excel.
Read more
Syllabus
Logistic Regression & Supervised Learning using SPSS
This module introduces learners to the foundational principles of logistic regression and equips them with hands-on skills in SPSS for managing variables, configuring the data environment, and interpreting statistical outputs. Learners will explore both theoretical concepts and practical applications, including variable setup, SPSS navigation, model output interpretation, and foundational logistic modeling techniques. By the end of this module, learners will be capable of preparing, analyzing, and interpreting logistic regression models using SPSS and supporting tools like MS Excel.
Read more
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Activities
Coming soon
We're preparing activities for
SPSS: Apply & Interpret Logistic Regression Models.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete SPSS: Apply & Interpret Logistic Regression Models will develop knowledge and skills
that may be useful to these careers:
Predictive Modeler
Predictive Modeler
A Predictive Modeler develops and deploys statistical or machine learning models to forecast future events or behaviors, providing insights that drive strategic decisions. The SPSS: Apply & Interpret Logistic Regression Models course is designed precisely for individuals aiming to become a Predictive Modeler. It offers a practical and applied introduction to logistic regression and supervised learning, emphasizing both conceptual understanding and tool-based execution with IBM SPSS Statistics. Learners will gain step-by-step instruction on constructing predictive models using real-world case studies, analyzing model outputs, and evaluating predictor significance to assess model performance and make informed decisions, which are core competencies for this role.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to biological, medical, and public health research, playing a critical role in designing studies, analyzing data, and interpreting findings. This career typically requires an advanced degree. The SPSS: Apply & Interpret Logistic Regression Models course is profoundly relevant for an aspiring Biostatistician. It provides a strong foundation in logistic regression, a frequently used model for analyzing binary outcomes in health research. Learners gain hands-on proficiency with SPSS for data management, model construction with real-world examples like smoking behavior classification, and intricate interpretation of statistical outputs to assess model performance and communicate findings.
Statistician
Statistician
A Statistician applies mathematical and statistical methods to collect, analyze, and interpret data, solving problems across various fields from science to business. This career path typically requires an advanced degree. For an aspiring Statistician, the SPSS: Apply & Interpret Logistic Regression Models course offers invaluable practical experience. It provides foundational principles of logistic regression and supervised learning, alongside hands-on SPSS skills to manage variables, configure data, and interpret complex statistical outputs. Learners will construct predictive models, evaluate predictor significance, and assess model performance using statistical evidence, directly aligning with the core responsibilities of a Statistician in preparing, analyzing, and interpreting advanced statistical models.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the patterns, causes, and effects of health and disease conditions in defined populations, relying heavily on statistical methods to understand public health challenges. This profession typically requires an advanced degree. For an aspiring Epidemiologist, the SPSS: Apply & Interpret Logistic Regression Models course is exceptionally valuable. Logistic regression is a cornerstone for analyzing risk factors and disease outcomes, particularly when dealing with binary health events. Learners will gain hands-on proficiency with SPSS, configuring data, constructing predictive models, and interpreting statistical results from real-world case studies like smoking behavior classification, which directly mirrors typical epidemiological analyses and research.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst applies advanced mathematical and statistical methods to financial and business problems, developing models to assess risk, price assets, or forecast market trends. This career path often requires an advanced degree. For an aspiring Quantitative Analyst, the SPSS: Apply & Interpret Logistic Regression Models course provides practical mastery in a core statistical modeling technique. Learners will gain profound understanding of logistic regression and supervised learning, coupled with hands-on SPSS skills to manage data, construct complex predictive models, and interpret intricate statistical outputs. This ability to evaluate model performance and prediction accuracy is essential for making data-driven decisions in quantitative finance and other analytical domains.
Credit Risk Analyst
Credit Risk Analyst
A Credit Risk Analyst assesses the creditworthiness of individuals and businesses, using statistical models to predict the likelihood of default on loans or other financial obligations. For this role, the SPSS: Apply & Interpret Logistic Regression Models course is exceptionally pertinent. Logistic regression is a cornerstone technique for modeling binary outcomes such as default or non default. The course equips learners with practical skills in configuring data within SPSS, constructing predictive models, and analyzing model outputs to evaluate predictor significance, which is crucial for identifying key risk factors. Mastering the interpretation of statistical results enables informed decision-making in credit allocation and risk management.
Market Research Analyst
Market Research Analyst
A Market Research Analyst studies market conditions to assess potential sales of products and services, using statistical methods to understand consumer behavior and market trends. This course offers highly relevant skills for a Market Research Analyst. By learning to apply and interpret logistic regression models using IBM SPSS Statistics, learners can analyze data from surveys, experiments, and customer interactions to predict outcomes like purchase likelihood or brand preference. The ability to construct predictive models, evaluate variable significance, and interpret statistical results from real-world case studies prepares you to uncover actionable insights for strategic marketing and business development.
Data Analyst
Data Analyst
A Data Analyst examines large datasets to identify trends, draw conclusions, and help organizations make informed decisions. This role involves cleaning, processing, and performing statistical analyses on data. The SPSS: Apply & Interpret Logistic Regression Models course provides a robust foundation for an aspiring Data Analyst, equipping learners with practical skills in constructing and interpreting predictive models. By mastering logistic regression within SPSS and understanding supervised learning, you gain the ability to analyze complex patterns, evaluate variable significance, and communicate actionable insights from structured datasets. This course directly enhances your capability to perform advanced analytical tasks, making you proficient in using a powerful statistical tool for real-world data challenges.
Customer Insights Analyst
Customer Insights Analyst
A Customer Insights Analyst uncovers deep understanding of customer behavior, preferences, and needs to help businesses improve products, services, and marketing strategies. This course offers highly valuable skills for a Customer Insights Analyst. Learning to apply and interpret logistic regression models using IBM SPSS Statistics enables you to analyze customer data to predict crucial behaviors, such as churn risk, purchase intent, or subscription renewal. The practical experience in configuring data, constructing predictive models, evaluating variable significance, and interpreting statistical results empowers you to translate complex data patterns into clear, actionable strategies for optimizing customer experience and engagement.
Fraud Analyst
Fraud Analyst
A Fraud Analyst investigates suspicious activities and patterns using data analysis and specialized tools to identify, prevent, and mitigate fraudulent transactions or behaviors. The SPSS: Apply & Interpret Logistic Regression Models course is highly relevant for a Fraud Analyst. Logistic regression is a powerful statistical technique for classifying binary outcomes, such as fraudulent versus non fraudulent activity. The course provides practical skills in constructing predictive models within SPSS, analyzing model outputs, and evaluating the significance of variables that indicate fraud. This direct application of supervised learning and model interpretation helps in developing robust detection systems and making informed decisions to combat financial crime.
Decision Scientist
Decision Scientist
A Decision Scientist combines elements of data science, behavioral economics, and operations research to build models and frameworks that guide strategic decision-making in organizations. This role often requires an advanced degree. For a Decision Scientist, the SPSS: Apply & Interpret Logistic Regression Models course provides essential skills in predictive modeling. By focusing on logistic regression and supervised learning, learners will develop the ability to construct powerful predictive models, analyze their outputs, and interpret statistical results to inform critical choices. The course's emphasis on evaluating model performance and communicating findings effectively through SPSS outputs is crucial for translating complex analytical insights into actionable business strategies.
Healthcare Data Analyst
Healthcare Data Analyst
A Healthcare Data Analyst collects, analyzes, and interprets data from various sources within the healthcare sector to improve patient care, operational efficiency, and public health outcomes. This course offers valuable analytical capabilities for a Healthcare Data Analyst. By focusing on logistic regression and supervised learning with IBM SPSS Statistics, learners develop the skills to analyze clinical trial data, patient outcomes, or disease risk factors. The ability to construct predictive models, evaluate the significance of variables impacting health, and interpret statistical results, as demonstrated through real-world examples like heart pulse analysis, is directly applicable to making data-driven decisions in healthcare.
Research Analyst
Research Analyst
A Research Analyst collects, processes, and interprets data to answer specific research questions, often working in academic institutions, non profits, or market research firms. The SPSS: Apply & Interpret Logistic Regression Models course provides excellent preparation for a Research Analyst career. It focuses on the foundational principles of logistic regression and equips learners with hands-on skills in SPSS for managing variables, configuring data, and interpreting statistical outputs. The ability to construct predictive models using real-world case studies, analyze model outputs, and evaluate predictor significance allows you to make informed decisions and effectively communicate research findings based on robust statistical evidence.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst focuses on transforming data into actionable insights for business operations and strategy, often creating dashboards and reports, but also performing predictive analytics. The SPSS: Apply & Interpret Logistic Regression Models course may be helpful for a Business Intelligence Analyst. While core BI often emphasizes reporting, the course's focus on predictive modeling using logistic regression and supervised learning can significantly enhance advanced analytical capabilities. Learners will gain familiarity with SPSS for configuring data, constructing models, and interpreting statistical results for informed decisions, which can be applied to forecast business trends, customer behavior, or operational efficiency metrics, moving beyond descriptive analytics.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses advanced analytical methods to help organizations make more effective decisions and operate more efficiently, often involving optimization, simulation, and predictive modeling. This role often involves an advanced degree. The SPSS: Apply & Interpret Logistic Regression Models course may be useful for an Operations Research Analyst. It introduces predictive modeling with logistic regression and supervised learning using IBM SPSS Statistics. The ability to construct predictive models, analyze outputs, and interpret statistical results for informed decisions can be applied to forecasting operational outcomes, such as equipment failure or supply chain disruptions, contributing to more robust decision-making and operational improvements.
For more career information including salaries, visit:
OpenCourser.com/course/hcpb5t/spss
Reading list
We've selected 23 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
SPSS: Apply & Interpret Logistic Regression Models.
This is widely considered the definitive guide for learning statistics with SPSS, offering an engaging and comprehensive look at the software's capabilities. It provides an excellent foundation for logistic regression, explaining the underlying assumptions and diagnostics in a way that is accessible to beginners. The book is highly valuable as both a primary textbook and a long-term reference for interpreting complex outputs.
Highly practical, 'how-to' guide that mirrors the course's emphasis on tool-based execution. It provides clear, step-by-step instructions for running logistic regression and, more importantly, tells the reader exactly how to report the results. It is an essential reference for learners who need to move quickly from data entry to final output interpretation.
Updated for the latest version of SPSS, this book provides the most current screenshots and menu paths relevant to the course. It covers logistic regression within a broader context of multivariate analysis, helping students understand where this model fits in the predictive modeling landscape. It is particularly useful for students who want to ensure their software navigation matches current industry standards.
Excels at breaking down complex statistical procedures into manageable steps, making it a perfect supplement for the course's guided examples. It focuses heavily on the interpretation of SPSS output tables, which core learning objective of the course. It is best used as a companion during the hands-on lab portions of the syllabus.
Knapp’s book is specifically designed to follow an introductory course, making it a perfect 'next step' for learners completing this SPSS module. It focuses on the application of models to real-world datasets, mirroring the course's case-study approach. The walkthroughs for interpreting SPSS output tables are exceptionally clear and detailed.
Since the course syllabus specifically mentions integrating Excel-based logistic modeling, this book unique and perfect fit. It bridges the gap between spreadsheet calculations and professional statistical software, reinforcing the course's dual-tool approach. It is particularly helpful for learners who are transitioning from basic data management in Excel to predictive modeling in SPSS.
Is specifically designed to help students understand the 'why' and 'how' of complex statistics using SPSS. It provides detailed explanations of logistic regression outputs, including the classification table and Hosmer-Lemeshow test. It is an excellent textbook for those who need a more structured learning path than a short online course can provide.
Written by recognized SPSS experts, this book focuses on the predictive analytics and visualization aspects of the software. It aligns well with the course's goal of communicating findings through SPSS-generated outputs and charts. It provides advanced tips for configuring the data environment that go beyond basic introductory steps.
Save
Designed for self-study, this book uses a unique lecture-style format that is very helpful for learners taking an online course. It covers the logistic equation and variable interaction in great detail, which supplements the course's focus on model construction. It is an excellent resource for those who need to review the logic of the model at their own pace.
This is the authoritative academic text on logistic regression, providing the deep theoretical background that a practical course might skip. It is indispensable for learners who want to understand the mathematics behind the 'heart pulse' and 'smoking behavior' models mentioned in the syllabus. While more technical, it serves as the ultimate reference for model validation and goodness-of-fit testing.
This textbook is widely used in academic institutions for its clear pedagogical approach to multivariate statistics. It contains dedicated chapters on logistic regression that focus on assumptions and the interpretation of odds ratios. It provides the necessary prerequisite knowledge for students who may find the software-specific instructions too fast-paced.
Save
This recent publication offers a very modern approach to regression, focusing on the interpretation of results for decision-making. It aligns with the course's goal of making 'informed decisions' based on statistical evidence. It useful reference for learners who want to see how logistic regression is applied in contemporary data science projects.
Provides a modern 'supervised learning' context for logistic regression, which key theme in the course description. While it uses Python, the conceptual chapters on classification and model evaluation (like ROC curves) are the gold standard for predictive modeling theory. It adds significant depth to the course by explaining the broader machine learning framework.
This foundational textbook for industry professionals involved in predictive modeling and market research. It provides a non-mathematical approach to understanding the 'big picture' of logistic regression and supervised learning. It is particularly helpful for learning how to communicate statistical findings to a non-technical audience.
Is highly regarded for its focus on the practical application of multivariate methods in social science research. It contains a robust section on logistic regression that emphasizes the importance of checking model assumptions. It provides a more rigorous academic framework for the 'Applied Analysis' module of the course.
Part of the Sage Quantitative Applications in the Social Sciences series, this book concise yet rigorous treatment of the subject. It is highly recommended for learners who want a deep dive into variable significance and the construction of logistic equations. It serves as an excellent bridge between introductory SPSS usage and advanced statistical research.
A classic and highly respected text in the field of behavioral sciences, this book offers a high-level look at logistic regression. It valuable reference for troubleshooting complex data issues like multicollinearity or outliers in SPSS. It adds significant breadth to the course by showing how logistic regression relates to other multivariate techniques.
This handbook concise reference tool for researchers who need to perform specific analyses quickly. It provides direct examples of logistic regression using SPSS syntax and menus, reinforcing the course's practical goals. It is particularly useful for learners who want to see a variety of different case studies beyond those provided in the course.
Agresti leading authority on categorical data, and this book provides the comprehensive statistical theory required for professional-level modeling. It useful reference tool for understanding the link functions and distributions that define logistic regression. It is more valuable as a background reference than a daily SPSS guide.
Focuses on the 'applied' nature of statistics in a business and data science context, making it very relevant to professional development. It provides clear explanations of why logistic regression is used for classification problems, such as the smoking behavior case study. It helps learners understand the 'why' behind the statistical evidence they generate in SPSS.
Save
This very short, focused primer on the logic and interpretation of logistic regression. It is ideal for a quick review of the concepts of odds and logit, which are central to the course's syllabus. It great resource for students who want a conceptual overview without being overwhelmed by software screenshots.
Is excellent for providing the 'background knowledge' required to design a study before even opening SPSS. It places logistic regression within the context of research design, helping learners understand how to choose predictors. It useful academic textbook for those taking the course for educational or research purposes.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hcpb5t/spss
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser