- Welcome to the Handbook!
- Welcome to Handbook of Regression Method
Welcome to Handbook of Regression Method. Here is the welcome message from Dr. Young ...
- Some Notes about the Handbook
- Supplementary Documents
Here is some inevitable typo found by users
- Introduction
- Introduction to Chapter 2
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 02.08.01 || Toy Data
- 02.08.02 || Steam Output Data
- 02.08.03 || Computer Repair Data
- Chapter 2 || Section 8
Regression Results
Data Plots
R Code
Regression Results
Data Plots
R Code
- Introduction
- Introduction to Chapter 3
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 03.06.01 || Steam Output Data
- 03.06.02 || Computer Repair Data
- 03.06.03 || Pulp Property Data
- Chapter 3 || Section 6
Data Plots
R Code
Data Plots
R Code
- Introduction
- Introduction to Chapter 4
- 04.07.01 || Steam Output Data
- 04.07.02 || 1993 Car Sale Data
- 04.07.03 || Black Cherry Tree Data
- Chapter 4 || Section 7
Regression Results
Data Plots
R Code
Regression Results
R Code
Regression Results
R Code
- Introduction
- Introduction to Chapter 5
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 5.3.1 || Steam Output Data
- 5.3.2 || Computer Repair Data
- 5.3.3 || Fiber Strength Data
- Chapter 5 || Section 3
Regression Result for Steam Output Data
Code for Steam Output Data
Regression Result for Computer Repair Data
Code for Computer Repair Data
Ndaro et al. (2007) conducted a study about the strength of a particular type of fiber based on the amount of pressure applied. The experiment consisted of five replicates at each of six different water pressure levels for a total sample size of n = 30.
The measured variables are
• Y : tensile strength of fiber (measured in N/5 cm)
• X: amount of water pressure applied (measured in bars); the unique levels are 60, 80, 100, 120, 150, and 200
Method
Plots for Fiber Strength Data
Code for Fiber Strength Data
- Introduction
- Introduction to Chapter 6
A multiple linear regression model is a linear model that describes how a response variable relates to two or more predictor variables (or transformations of those predictor variables).
For example, suppose that a researcher is studying factors that might affect systolic blood pressures for women aged 45 to 65 years old. The response variable is systolic blood pressure (Y). Suppose that two predictor variables of interest are age (X1) and body mass index (X2). The general structure of a multiple linear regression model for this situation would be
- 6.7.1 || Thermal Energy Data
- 6.7.3 || Tortoise Eggs Data
- Chapter 6 || Section 7
Regression Result for Pulp Property Data
Data Plot 1
Code for Pulp Property Data
This dataset of size n = 18 contains measurements from a study on the number of eggs in female gopher tortoises in southern Florida (Ashton et al., 2007). The number of eggs in each tortoise - called the clutch size - was obtained using X-rays. The variables are:
• Y : number of eggs (clutch size)
• X: carapace length (in millimeters)
Method
Data Plot 1
Code for Plots
- Introduction
- Introduction to Chapter 7
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 07.04.01 || Thermal Energy Data
- 07.04.02 || Tortoise Eggs Data
- Chapter 7 || Section 4
Regression Results
Data Plots
R Code
Regression Results
Data Plots
R Code
- Introduction
- Introduction to Chapter 8
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 08.06.01 || Thermal Energy Data
- 08.06.02 || Simulated Partial Leverage Data
- Chapter 8 || Section 6
Regression Results
R Code
- Introduction
- Introduction to Chapter 9
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 09.06.01 || Auditory Discrimination Data
- 09.06.02 || Steam Output Data
- 09.06.03 || Tea Data
- Chapter 9 || Section 6
Regression Results
Data Plots
R Code
Data Plots
R Code
Regression Results
R Code
- Introduction
- Introduction to Chapter 10
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 10.05.01 || Punting Data
- 10.05.02 || Expenditures Data
- Chapter 10 || Section 5
Regression Results
R Code
Regression Results
R Code
- Introduction
- Introduction to Chapter 11
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 11.06.01 || Blood Alcohol Concentration Data
- 11.06.02 || U.S. Economy Data
- 11.06.03 || Arsenate Assay Data
- Chapter 11 || Section 6
Data Plots
R Code
Regression Result
Code for U.S. Economu Data
Regression Results
Data Plots
R Code
- Introduction
- Introduction to Chapter 12
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 12.06.01 || Computer-Assisted Learning Data
- 12.06.02 || Arsenate Assay Data
- Chapter 12 || Section 6
Regression Results
R Code
Regression Results
Data Plots
R Code
- Introduction
- Introduction to Chapter 13
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 13.05.01 || Google Stock Data
- 13.05.02 || Cardiovascular Data
- 13.05.03 || Natural Gas Prices Data
- 13.05.04 || Air Passengers Data
- 13.05.05 || Mouse Liver Data
- Chapter 13 || Section 5
Data Plots
R Code
Regression Results
R Code
Regression Results
R Code
Regression Results
R Code
Regression Results
Data Plots
R Code
- Introduction
- Introduction to Chapter 14
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 14.06.01 || Punting Data
- Chapter 14 || Section 6
Regression Results
Data Plots
R Code
- Introduction
- Introduction to Chapter 15
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 15.05.01 || Sleep Study Data
- 15.05.02 || Cracker Promotion Data
- 15.05.03 || Odor Data
- 15.05.04 || Yarn Fiber Data
- Chapter 15 || Section 5
Regression Results
Data Plots
R Code
Regression Results
Data Plots
R Code
R Code
- Introduction
- Introduction to Chapter 16
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 16.05.01 || Prostate Cancer Data
- 16.05.02 || Automobile Features Data
- Chapter 16 || Section 5
Regression Results
Data Plots
R Code
Regression Results
Data Plots
R Code
- Introduction
- Introduction to Chapter 17
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 17.05.01 || Gamma-Ray Burst Data
- 17.05.02 || Quasars Data
- 17.05.03 || LIDAR Data
- Chapter 17 || Section 5
Regression Results
Data Plots
R Code
Data Plots
R Code
- Introduction
- Introduction to Chapter 18
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 18.07.01 || Extramarital Affairs Data
- 18.07.02 || Motorette Data
- 18.07.03 || Bone Marrow Transplant Data
- 18.07.04 || Durable Goods Data
- Chapter 18 || Section 7
Regression Results
R Code
Regression Results
Data Plots
R Code
- Introduction
- Introduction to Chapter 19
An analysis of variance (ANOVA) table for regression displays quantities that measure how much of the variability in the response variable is explained and how much is not explained by the regression relationship with the predictor variable. The table also gives the construction and value of the mean squared error (MSE) and a significance test of whether the variables are related in the sampled population.
- 19.04.01 || Puromycin Data
- 19.04.02 || Light Data
- 19.04.03 || James Bond Data
- Chapter 19 || Section 4
Data Plots
R Code
Regression Results
Data Plots
R Code
Regression Results
Data Plots
R Code
- 20.05.01 || Subarachnoid Hemorrhage Data
- 20.05.02 || Sportsfishing Survey Data
- 20.05.03 || Cheese-Tasting Experiment Data
- 20.05.04 || Biochemistry Publications Data
- 20.05.05 || Hospital Stays Data
- Chapter 20 || Section 5