Linear Regression Model: Goodness of Fit and Testing of Hypothesis
Panchanan Das
Additional contact information
Panchanan Das: University of Calcutta, Department of Economics
Chapter 3 in Econometrics in Theory and Practice, 2026, pp 65-94 from Springer
Abstract:
Abstract In a linear regression model, estimation of parameter is done on the basis of a sample randomly taken from the population. As sample is a part of the population, an OLS estimate of a parameter would not be exactly equal to the parameter. So, inference is to be made regarding how a SRF is representative of the PRF. An OLS estimate is a random variable following a probability distribution. Inference about parameters can be done by exploiting the randomness behaviour of statistics. Statistical inference is a process by which one can make inference about unknown population on the basis of the estimates from known sample. In classical econometrics, the principal way of doing this is performing hypothesis tests and constructing confidence intervals. This chapter deals with this problem.
Date: 2026
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-981-95-7226-7_3
Ordering information: This item can be ordered from
http://www.springer.com/9789819572267
DOI: 10.1007/978-981-95-7226-7_3
Access Statistics for this chapter
More chapters in Springer Texts in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().