simple linear regression equation statistics

First, let’s take a look at these seven assumptions: Even when your data fails certain assumptions, there is often a solution to overcome this. This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out linear regression when everything goes well! However, don’t worry.

In practice, checking for these seven assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task.īefore we introduce you to these seven assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated (i.e., not met). You need to do this because it is only appropriate to use linear regression if your data 'passes' seven assumptions that are required for linear regression to give you a valid result. When you choose to analyse your data using linear regression, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using linear regression. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for linear regression to give you a valid result. This 'quick start' guide shows you how to carry out linear regression using SPSS Statistics, as well as interpret and report the results from this test. If you have two or more independent variables, rather than just one, you need to use multiple regression. For example, you could use linear regression to understand whether exam performance can be predicted based on revision time whether cigarette consumption can be predicted based on smoking duration and so forth. The variable we are using to predict the other variable's value is called the independent variable (or sometimes, the predictor variable). The variable we want to predict is called the dependent variable (or sometimes, the outcome variable).

It is used when we want to predict the value of a variable based on the value of another variable. Linear regression is the next step up after correlation. Linear Regression Analysis using SPSS Statistics Introduction

Simple linear regression equation statistics