R-squared<\/a>\u00a0measures how well your data fits a regression line<\/a>. More specifically, it’s how much variation in the response variable<\/a> your linear model explains.\u00a0it is expressed as a percentage\u00a0 (0 to 100%). The percentage is problem specific, so you can’t compare R-squared across different situations; You can<\/em> use it to compare different models for one specific set of data.<\/span><\/p>\n R-squared is also influenced by the number of observations<\/strong>: 0.80 R-squared on 100 observations doesn’t mean the same thing as 0.80 R-squared on 1,000 observations. One way around this is to compute R-squared on multiple sub-samples with 100 observations, then compute its median. That way, you can compare an R-squared on (say) 1,000 observations, with one on 100 observations<\/span><\/p>\n For more statistical concepts explained in one picture, follow this link<\/a>.\u00a0<\/p>\n<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/span><\/p>\n