Read Online (Free) relies on page scans, which are not currently available to screen readers. To access this article, please contact JSTOR User Support . We'll provide a PDF copy for your screen reader.
With a personal account, you can read up to 100 articles each month for free.
Get StartedAlready have an account? Log in
Monthly Plan
- Access everything in the JPASS collection
- Read the full-text of every article
- Download up to 10 article PDFs to save and keep
Yearly Plan
- Access everything in the JPASS collection
- Read the full-text of every article
- Download up to 120 article PDFs to save and keep
Log in through your institution
Purchase a PDF
Purchase this article for $29.00 USD.
Purchase this issue for $129.00 USD. Go to Table of Contents.
How does it work?
- Select a purchase option.
- Check out using a credit card or bank account with PayPal.
- Read your article online and download the PDF from your email or your account.
journal article
Bivariate Regression Analysis is UselessJournal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 12, No. 3 (Nov., 1963)
, pp. 161-179 (19 pages)
Published By: Wiley
//doi.org/10.2307/2985794
//www.jstor.org/stable/2985794
Read and download
Log in through your school or library
Alternate access options
For independent researchers
Read Online
Read 100 articles/month free
Subscribe to JPASS
Unlimited reading + 10 downloads
Purchase article
$29.00 - Download now and later
Journal Information
Applied Statistics of the Journal of the Royal Statistical Society was founded in 1952. It promotes papers that are driven by real life problems and that make a novel contribution to the subject. JSTOR provides a digital archive of the print version of Applied Statistics. The electronic version of Applied Statistics is available at //www.interscience.wiley.com. Authorized users may be able to access the full text articles at this site.
Publisher Information
Wiley is a global provider of content and content-enabled workflow solutions in areas of scientific, technical, medical, and scholarly research; professional development; and education. Our core businesses produce scientific, technical, medical, and scholarly journals, reference works, books, database services, and advertising; professional books, subscription products, certification and training services and online applications; and education content and services including integrated online teaching and learning resources for undergraduate and graduate students and lifelong learners. Founded in 1807, John Wiley & Sons, Inc. has been a valued source of information and understanding for more than 200 years, helping people around the world meet their needs and fulfill their aspirations. Wiley has published the works of more than 450 Nobel laureates in all categories: Literature, Economics, Physiology or Medicine, Physics, Chemistry, and Peace. Wiley has partnerships with many of the world’s leading societies and publishes over 1,500 peer-reviewed journals and 1,500+ new books annually in print and online, as well as databases, major reference works and laboratory protocols in STMS subjects. With a growing open access offering, Wiley is committed to the widest possible dissemination of and access to the content we publish and supports all sustainable models of access. Our online platform, Wiley Online Library (wileyonlinelibrary.com) is one of the world’s most extensive multidisciplinary collections of online resources, covering life, health, social and physical sciences, and humanities.
Rights & Usage
This item is part of a JSTOR Collection.
For terms and use, please refer to our Terms and Conditions
Journal of the Royal Statistical Society. Series C (Applied Statistics) © 1963 Royal Statistical Society
Request Permissions
Linear regression is a basic and commonly used type of predictive analysis. The overall idea of regression is to examine two things: (1) does a set of predictor variables do a good job in predicting an outcome (dependent) variable? (2) Which variables in particular are significant predictors of the outcome variable, and in what way do they–indicated by the magnitude and sign of the beta estimates–impact the outcome variable? These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables. The simplest form of the regression equation with one dependent and one independent variable is defined by the formula y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable.
Naming the Variables. There are many names for a regression’s dependent variable. It may be called an outcome variable, criterion variable, endogenous variable, or regressand. The independent variables can be called exogenous variables, predictor variables, or regressors.
Three major uses for regression analysis are (1) determining the strength of predictors, (2) forecasting an effect, and (3) trend forecasting.
Discover How We Assist to Edit Your Dissertation Chapters
Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.
- Bring dissertation editing expertise to chapters 1-5 in timely manner.
- Track all changes, then work with you to bring about scholarly writing.
- Ongoing support to address committee feedback, reducing revisions.
First, the regression might be used to identify the strength of the effect that the independent variable(s) have on a dependent variable. Typical questions are what is the strength of relationship between dose and effect, sales and marketing spending, or age and income.
Second, it can be used to forecast effects or impact of changes. That is, the regression analysis helps us to understand how much the dependent variable changes with a change in one or more independent variables. A typical question is, “how much additional sales income do I get for each additional $1000 spent on marketing?”
Third, regression analysis predicts trends and future values. The regression analysis can be used to get point estimates. A typical question is, “what will the price of gold be in 6 months?”
Types of Linear Regression
Simple linear regression
1 dependent variable (interval or ratio), 1 independent variable (interval or ratio or dichotomous)
Multiple linear regression
1 dependent variable (interval or ratio) , 2+ independent variables (interval or ratio or dichotomous)
Logistic regression
1 dependent variable
(dichotomous), 2+ independent variable(s) (interval or ratio or dichotomous)
Ordinal regression
1 dependent variable (ordinal), 1+ independent variable(s) (nominal or dichotomous)
Multinomial regression
1 dependent variable (nominal), 1+ independent variable(s) (interval or ratio or dichotomous)
Discriminant analysis
1 dependent variable
(nominal), 1+ independent variable(s) (interval or ratio)
When selecting the model for the analysis, an important consideration is model fitting. Adding independent variables to a linear regression model will always increase the explained variance of the model (typically expressed as R²). However, overfitting can occur by adding too many variables to the model, which reduces model generalizability. Occam’s razor describes the problem extremely well – a simple model is usually preferable to a more complex model. Statistically, if a model includes a large number of variables, some of the variables will be statistically significant due to chance alone.
To Reference this Page: Statistics Solutions. (2013). What is Linear Regression . Retrieved from here.
Related Pages:
Assumptions of a Linear Regression
Statistics Solutions can assist with your quantitative analysis by assisting you to develop your methodology and results chapters. The services that we offer include:
Data Analysis Plan
Edit your research questions and null/alternative hypotheses
Write your data analysis plan; specify specific statistics to address the research questions, the assumptions of the statistics, and justify why they are the appropriate statistics; provide references
Justify your sample size/power analysis, provide references
Explain your data analysis plan to you so you are comfortable and confident
Two hours of additional support with your statistician
Quantitative Results Section (Descriptive Statistics, Bivariate and Multivariate Analyses, Structural Equation Modeling, Path analysis, HLM, Cluster Analysis)
Clean and code dataset
Conduct descriptive statistics (i.e., mean, standard deviation, frequency and percent, as appropriate)
Conduct analyses to examine each of your research questions
Write-up results
Provide APA 6th edition tables and figures
Explain chapter 4 findings
Ongoing support for entire results chapter statistics
Please call 727-442-4290 to request a quote based on the specifics of your research, schedule using the calendar on this page, or email [email protected]