{"id":235,"date":"2021-03-14T23:10:34","date_gmt":"2021-03-14T23:10:34","guid":{"rendered":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/ziyang-yang\/?p=235"},"modified":"2021-04-30T12:31:58","modified_gmt":"2021-04-30T12:31:58","slug":"statistics-in-social-science-2-explaining-linear-regression","status":"publish","type":"post","link":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/ziyang-yang\/2021\/03\/14\/statistics-in-social-science-2-explaining-linear-regression\/","title":{"rendered":"Statistics in Social science (2): Explaining Linear regression"},"content":{"rendered":"\n
This blog will give you a real example how to explain linear regression<\/span><\/p>\n\n\n\n People in social science uses linear regression frequently. Scientists often use it to measure the relationship between a dependent variable and independent variables. And most real-world situations could be modelled and therefore explained. Basically, if we have data, and if you want to:<\/p>\n\n\n\n You could use linear regression. Linear regression often expressed as the Equation below. The dependent variable is the variable we want to explain, and independent variables are factors associated with dependent variables. The coefficient and constant will be estimated by computers, and we will explain them later. When there is only one independent variable, it is called a simple linear regression model. Multiple independent variables indicate multiple linear regression.<\/p>\n\n\n\n When I was trying to find resources about linear regression, most tutorials only focused on building the model in software while fewer mentions explanation. Moreover, there is no tutorial talking about the variable choice before modelling. Here, we assume our readers are confident with building models with different software, and we only focus on the process and explanation of model construction. <\/p>\n\n\n\n Directly building a model with all variables is not sensible, especially when the number of variables is large. And you don’t want to explain something by irrelevant factors like explain a pupil’s grade by the number of animals in the zoo. So, we only want relevant factors in our model. <\/p>\n\n\n\n Typically, there are two rules to find relevant factors:<\/p>\n\n\n\n If a variable fits either rule, it could be selected to build the model. <\/p>\n\n\n\n For example, we will measure what factors will influence peoples’ feeling about the life satisfaction ladder (figures from 1 to 7 with 1 represents dissatisfaction totally and 7 represents satisfaction totally). From the literature review, researches show age and gender has an impact on life satisfaction. The relationship plot and correlation coefficient also support this argument. Therefore, we added both age and gender in our model to explain life satisfaction.<\/p>\n\n\n\n Fitting model and checking model is a technical and complex process, we don’t show the whole process here.<\/p>\n\n\n\n Recall our example about life satisfaction, we have the mathematical expression like:<\/p>\n\n\n\n \\(Lifesatisfaction = 7.101 + -0.099 \\times Age +0.111 \\times Gender (Female)\\)<\/p>\n\n\n\n How to explain it?<\/p>\n\n\n\n Now, you are able to select the variables for models and explain the basic linear models.<\/p>\n\n\n\n For more technical blogs on model construction:<\/p>\n\n\n\n How to build models in R: https:\/\/www.r-bloggers.com\/2020\/05\/step-by-step-guide-on-how-to-build-linear-regression-in-r-with-code\/<\/a><\/p>\n\n\n\n How to build models in SPSS: https:\/\/www.r-bloggers.com\/2020\/05\/step-by-step-guide-on-how-to-build-linear-regression-in-r-with-code\/<\/a><\/p>\n\n\n\n For more readings about the linear regression:<\/p>\n\n\n\n
<\/figure><\/div>\n\n\n\nWhy we need linear regression?<\/h1>\n\n\n\n
<\/figure><\/div>\n\n\n\nWhat factors have to be included in the model?<\/h1>\n\n\n\n
How to explain the final model?<\/h1>\n\n\n\n