Format |
Pages |
Price |
||

20 | $58.00 | ADD TO CART | ||

Hardcopy (shipping and handling) |
20 | $58.00 | ADD TO CART | |

Standard + Redline PDF Bundle | 40 | $69.00 | ADD TO CART |

Historical Version(s) - view previous versions of standard

More E11.10 Standards Related Products Standard References

**Abstract**

This practice covers regression analysis of a set of data to define the statistical relationship between two numerical variables for use in predicting one variable from the other. This practice is restricted in scope to consider only a single numerical response variable and a single numerical predictor variable. The objective is to obtain a regression model for use in predicting the value of the response variable Y for given values of the predictor variable X.

*This abstract is a brief summary of the referenced standard. It is informational only and not an official part of the standard; the full text of the standard itself must be referred to for its use and application. ASTM does not give any warranty express or implied or make any representation that the contents of this abstract are accurate, complete or up to date. *

**Significance and Use**

4.1 Regression analysis is a procedure that uses data to study the statistical relationships between two or more variables (1, 2).3 This practice is restricted in scope to consider only a single numerical response variable and a single numerical predictor variable. The objective is to obtain a regression model for use in predicting the value of the response variable Y for given values of the predictor variable X.

4.2 A regression model consists of: (1) a regression function that relates the mean values of the response variable distribution to fixed values of the predictor variable, and (2) a statistical distribution that describes the variability in the response variable values at a fixed value of the predictor variable.

4.2.1 The regression analysis utilizes either experimental or observational data to estimate the parameters defining a regression model and their precision. Diagnostic procedures are utilized to assess the resulting model fit and can suggest other models for improved prediction performance.

4.3 The information in this practice is arranged as follows.

4.3.1 Section 5 gives a general outline of the steps in the regression analysis procedure. The subsequent sections cover procedures for estimation of specific regression models.

4.3.2 Section 6 assumes a straight line relationship between the two variables. This is also known as the simple linear regression model or a first order model. This model should be used as a starting point for understanding the XY relationship and ultimately defining the best fitting model to the data.

4.3.3 Section 7 considers a proportional relationship between the variables, where the ratio of one variable to the other is constant. The intercept is constrained to be zero. This model is useful for single point calibration, where a reference material is run periodically as a standard during routine testing to correct for drift in instrument performance over a given range of test results.

4.3.4 Section 8 discusses a regression function that considers curvature in the XY relationship, the second order polynomial model.

4.3.5 Annex A1 provides supplemental information of a more mathematical nature in regression.

4.3.6 Appendix X1 lists calculations for the curvature model estimates and exhibits a worksheet for these calculations.

**1. Scope**

1.1 This practice covers regression analysis of a set of data to define the statistical relationship between two numerical variables for use in predicting one variable from the other.

1.2 The regression analysis provides graphical and calculational procedures for selecting the best statistical model that describes the relationship and for evaluation of the fit of the data to the selected model.

1.3 The resulting regression model can be useful for developing process knowledge through description of the variable relationship, in making predictions of future values, in relating the precision of a test method to the value of the characteristic being measured, and in developing control methods for the process generating values of the variables.

1.4 The system of units for this practice is not specified. Dimensional quantities in the practice are presented only as illustrations of calculation methods. The examples are not binding on products or test methods treated.

1.5 *This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.*

1.6 *This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.*

**2. Referenced Documents** *(purchase separately)* The documents listed below are referenced within the subject standard but are not provided as part of the standard.

**ASTM Standards**

E178 Practice for Dealing With Outlying Observations

E456 Terminology Relating to Quality and Statistics

E2586 Practice for Calculating and Using Basic Statistics

**ICS Code**

ICS Number Code 03.120.30 (Application of statistical methods)

**UNSPSC Code**

UNSPSC Code

Link Here | |||

Link to Active (This link will always route to the current Active version of the standard.) | |||

**DOI:** 10.1520/E3080-19

**Citation Format**

ASTM E3080-19, Standard Practice for Regression Analysis with a Single Predictor Variable, ASTM International, West Conshohocken, PA, 2019, www.astm.org

Back to Top