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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/sas-statistics
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课程名称：使用SAS进行统计分析 课程概述： 本入门课程面向使用SAS软件进行统计分析的用户，重点介绍SAS/STAT软件的使用。课程内容主要涵盖t检验、方差分析(ANOVA)和线性回归，并简要介绍了逻辑回归。 课程大纲： 1. **课程概述和数据准备**：学习课程内容和分析的数据，并设置所需的数据以进行实践活动。 2. **概念介绍与复习**：学习分析不同类型数据所需的模型，以及解释性建模与预测建模的区别。复习基本的统计概念，如均值的抽样分布、假设检验、p值和置信区间，随后应用单样本和双样本t检验对数据进行假设验证。 3. **方差分析与回归**：学习使用图形工具确定哪些预测变量可能有用，并通过相关分析描述潜在预测变量与响应变量之间的线性关系。使用ANOVA和回归分析评估响应与预测变量之间关系的质量。 4. **更复杂的线性模型**：将单因素ANOVA模型扩展到双因素方差分析，并将简单线性回归扩展到包含两个预测变量的多重回归。掌握两因素ANOVA和多重线性回归的概念，以适应多个变量的模型拟合与解读。 5. **模型构建与效应选择**：探索几种模型选择工具，帮助限制候选模型的数量，以便根据专业知识和研究优先级选择合适的模型。 6. **模型拟合后的推断**：学习验证模型假设，诊断线性回归过程中遇到的问题，检查残差、识别异常值和影响观察值，以及诊断多重共线性以避免模型中的标准误差膨胀和参数不稳定。 7. **模型构建用于评分与预测**：学习如何从推断统计过渡到预测建模，使用诚实评估取代p值，并在选择最佳模型后，了解如何部署模型以预测新数据。 8. **分类数据分析**：使用假设检验寻找预测变量与二元响应之间的关联，构建逻辑回归模型，表征响应与预测变量之间的关系，并学习如何使用逻辑回归建立预测未知情况的模型或分类器。 此课程旨在帮助学员掌握统计分析的基本方法，并通过SAS软件进行实践，提升数据分析能力。
Name:Course Overview and Data Setup
Description: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.
Name:Introduction and Review of Concepts
Description: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.
Name:ANOVA and Regression
Description: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.
Name:More Complex Linear Models
Description: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.
Name:Model Building and Effect Selection
Description: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.
Name:Model Post-Fitting for Inference
Description: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.
Name:Model Building for Scoring and Prediction
Description: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.
Name:Categorical Data Analysis
Description: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.
This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.
