However, you find that much more data has been collected at high rates of meat consumption than at low rates of meat consumption, with the result that there is much more variation in the estimate of cancer rates at the low range than at the high range. Example: Data that doesn’t meet the assumptionsYou think there is a linear relationship between cured meat consumption and the incidence of colorectal cancer in the U.S. If your data do not meet the assumptions of homoscedasticity or normality, you may be able to use a nonparametric test instead, such as the Spearman rank test. The relationship between the independent and dependent variable is linear: the line of best fit through the data points is a straight line (rather than a curve or some sort of grouping factor).Linear regression makes one additional assumption: Normality: The data follows a normal distribution.Independence of observations: the observations in the dataset were collected using statistically valid sampling methods, and there are no hidden relationships among observations.Homogeneity of variance (homoscedasticity): the size of the error in our prediction doesn’t change significantly across the values of the independent variable.Simple linear regression is a parametric test, meaning that it makes certain assumptions about the data. Frequently asked questions about simple linear regression.Can you predict values outside the range of your data?.How to perform a simple linear regression.Assumptions of simple linear regression.If you have more than one independent variable, use multiple linear regression instead. Your independent variable (income) and dependent variable (happiness) are both quantitative, so you can do a regression analysis to see if there is a linear relationship between them. You survey 500 people whose incomes range from 15k to 75k and ask them to rank their happiness on a scale from 1 to 10. Simple linear regression exampleYou are a social researcher interested in the relationship between income and happiness. Regression allows you to estimate how a dependent variable changes as the independent variable(s) change.
Linear regression models use a straight line, while logistic and nonlinear regression models use a curved line. Regression models describe the relationship between variables by fitting a line to the observed data.
Simple Linear Regression | An Easy Introduction & Examples
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