Nguồn ngoài

Chemometrics Using R

The goal of this textbook is to provide an introduction to chemometrics suitable for the undergraduate chemistry curriculum at the junior or senior level. Although you may not yet know what we mean by the term chemometrics, you almost certainly make routine use of chemometric techniques in your classes and labs: reporting an average result for several trials of an experiment or creating a calibration curve.

Analytical Chemistry Chemistry

Tác giả: David Harvey
Nhà xuất bản: DePauw University
Nguồn: LibreTexts
Giấy phép: CC BY-NC-SA
Ngôn ngữ: EN
Danh mục: Khoa học cơ bản

Đọc trực tuyến Tải PDF (nguồn mở)

Mục lục· 77 mục

01: R and RStudio
1.01: Installing and Accessing R and RStudio
1.02: The Basics of Working With R
1.03: Exercises
02: Types of Data
2.01: Ways to Describe Data
2.02: Using R to Organize and Manipulate Data
2.03: Exercises
03: Visualizing Data
3.01: Types of Visualizations
3.02: Using R to Visualize Data
3.03: Creating Plots From Scratch in R Using Base Graphics
3.04: Exercises
04: Summarizing Data
4.01: Ways to Summarize Data
4.02: Using R to Summarize Data
4.03: Exercises
05: The Distribution of Data
5.01: Terminology
5.02: Theoretical Models for the Distribution of Data
5.03: The Central Limit Theorem
5.04: Modeling Distributions Using R
5.05: Exercises
06: Uncertainty of Data
6.01: Properties of a Normal Distribution
6.02: Confidence Intervals
6.03: Using R to Model Properties of a Normal Distribution
6.04: Using R to Find Confidence Intervals
6.05: Exercises
07: Testing the Significance of Data
7.01: Significance Testing
7.02: Significance Tests for Normal Distributions
7.03: Analysis of Variance
7.04: Non-Parametric Significance Tests
7.05: Using R for Significance Testing and Analysis of Variance
7.06: Exercises
08: Modeling Data
8.01: Linear Regression of a Straight-Line Calibration Curve
8.02: Weighted Linear Regression with Errors in y
8.03: Weighted Linear Regression With Errors in Both x and y
8.04: Curvilinear and Multivariate Regression
8.05: Using R for a Linear Regression Analysis
8.06: Exercises
09: Gathering Data
9.01: Response Surfaces
9.02: Searching Algorithms
9.03: One-Factor-at-a-Time Optimizations
9.04: Simplex Optimization
9.05: Mathematical Models of Response Surfaces
9.06:Using R to Model a Response Surface
9.07:Exercises
10: Cleaning Up Data
10.1: Signals and Noise
10.2: Signal Averaging
10.3: Background Removal
10.4: Using R to Clean Up Data
10.5: Exercises
11: Finding Structure in Data
11.01: What Do We Mean By Structure and Order
11.02: Cluster Analysis
11.03: Principal Component Analysis
11.04: Multivariate Regression
11.05: Using R for a Cluster Analysis
11.06: Using R for a Principal Component Analysis
11.07: Using R For A Multivariate Regression
11.08: Exercises
12: Appendices
12.01: Single-Sided Normal Distribution
12.02: Critical Values for t-Test
12.03: Critical Values for F-Test
12.04: Critical Values for Dixon's Q-Test
12.05: Critical Values for Grubb's Test
12.06 Critical Values for the Wilcoxson Signed Rank Test
12.07: Critical Values for the Wilcoxson Ranked Sum Test
13: Resources
13.1: Chemometric Resources
13.2: R Resources