Statistics with SAS

Go to Course: https://www.coursera.org/learn/sas-statistics

Introduction

### Course Review: **Statistics with SAS** #### Overview In the world of data science and analytics, understanding statistics is paramount for making informed decisions based on quantitative data. For users of SAS software, especially those looking to sharpen their statistical analysis skills, the online course **Statistics with SAS** on Coursera is an excellent choice. This introductory course is specifically designed for individuals who are embarking on their statistical journey using SAS/STAT software. The curriculum covers essential concepts, including t-tests, ANOVA, linear regression, and a brief introduction to logistic regression, making it a comprehensive start for any aspiring data analyst. #### Course Structure and Content This course comprises several key modules that systematically guide learners from foundational concepts to more advanced statistical techniques. Here’s a breakdown of the syllabus: 1. **Course Overview and Data Setup** - This module sets the stage for the course. Students learn about the data they'll be working with and how to prepare it for analysis. 2. **Introduction and Review of Concepts** - A crucial module that reviews foundational statistical concepts such as hypothesis testing and p-values. Learners apply one-sample and two-sample t-tests to practice confirming or rejecting hypotheses, grounding them firmly in practical application. 3. **ANOVA and Regression** - Learners delve into graphical tools to identify useful predictors and employ correlation analyses to explore relationships between variables. The fundamental techniques of ANOVA and regression are introduced, allowing students to assess the relationship quality with their response variables. 4. **More Complex Linear Models** - This module expands upon earlier concepts, introducing two-way ANOVA and multiple regression, empowering learners to fit and interpret more intricate models with multiple predictors. 5. **Model Building and Effect Selection** - Essential for those aiming to streamline their analysis, this section explores various tools for selecting the most appropriate models based on research priorities and expertise. 6. **Model Post-Fitting for Inference** - Students learn about verifying model assumptions and diagnosing issues in regression, ensuring they are equipped to handle real-world data challenges effectively. 7. **Model Building for Scoring and Prediction** - Transitioning from inferential statistics to predictive modeling, this module emphasizes honest assessment over p-values, guiding learners in deploying models to predict new data. 8. **Categorical Data Analysis** - Finally, the course wraps up with an exploration of binary response variables. Students learn how to build logistic regression models, enhancing their ability to predict unknown cases. #### Recommendations **Statistics with SAS** is recommended for anyone seeking to understand statistical analysis fundamentals using SAS/STAT software. The course's structure is well-designed, progressively building a learner's knowledge and skills. Here are a few reasons to consider enrolling: - **Hands-On Learning:** The course emphasizes practical application, ensuring that theoretical concepts are paired with real data analysis. - **Expert Instruction:** Developed by accomplished instructors, the course content is high-quality and aligned with industry standards. - **Flexibility:** Being an online course, it offers the flexibility to learn at your own pace, making it ideal for both beginners and those looking to refine their skills. - **Career Advancement:** As organizations increasingly rely on data-driven decision-making, proficiency in statistical analysis using SAS enhances employability and professional advancement. #### Conclusion In conclusion, **Statistics with SAS** on Coursera emerges as a robust gateway into the world of statistical analysis. With its comprehensive syllabus, practical approach, and expert guidance, it equips learners with the necessary tools to navigate and interpret data effectively. If you're eager to enhance your analytical skills and open doors to opportunities in data science, this course is undoubtedly worth considering. Start your journey in statistics today and unlock the potential of your data!

Syllabus

Course Overview and Data Setup

In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.

In this module you learn about the models required to analyze different types of data and the difference between explanatory vs predictive modeling. Then you review fundamental statistical concepts, such as the sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals. After reviewing these concepts, you apply one-sample and two-sample t tests to data to confirm or reject preconceived hypotheses.

In this module you learn to use graphical tools that can help determine which predictors are likely or unlikely to be useful. Then you learn to augment these graphical explorations with correlation analyses that describe linear relationships between potential predictors and our response variable. After you determine potential predictors, tools like ANOVA and regression help you assess the quality of the relationship between the response and predictors.

In this module you expand the one-way ANOVA model to a two-factor analysis of variance and then extend simple linear regression to multiple regression with two predictors. After you understand the concepts of two-way ANOVA and multiple linear regression with two predictors, you'll have the skills to fit and interpret models with many variables.

In this module you explore several tools for model selection. These tools help limit the number of candidate models so that you can choose an appropriate model that's based on your expertise and research priorities.

In this module you learn to verify the assumptions of the model and diagnose problems that you encounter in linear regression. You learn to examine residuals, identify outliers that are numerically distant from the bulk of the data, and identify influential observations that unduly affect the regression model. Finally, you learn to diagnose collinearity to avoid inflated standard errors and parameter instability in the model.

In this module you learn how to transition from inferential statistics to predictive modeling. Instead of using p-values, you learn about assessing models using honest assessment. After you choose the best performing model, you learn about ways to deploy the model to predict new data.

In this module you look for associations between predictors and a binary response using hypothesis tests. Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. Finally, you learn how to use logistic regression to build a model, or classifier, to predict unknown cases.