#Linear regression equation example software#

Linear regression analysis is based on a few assumptions about the data. The researcher would then plot the data points and fit a regression line to know how much the respondents’ earnings influence their wellbeing. The researcher takes quantitative values of the dependent variable (happiness) and independent variable (income) by surveying people in a particular geographical location.įor instance, the data contains income figures and happiness levels (ranked on a scale from 1 to 10) from 500 people from the Indian state of Maharashtra. A social researcher interested in knowing how individuals’ income affects their happiness levels performs a simple regression analysis to see if a linear relationship occurs. Second, it gives you the value of the dependent variable at a certain value of the independent variable.Ĭonsider this linear regression example. First, it tells you the strength of the relationship between the dependent and independent factors of the historical data. Simple linear regression analysis is concerned with two things. If you have more than one independent variable, you will use multiple linear regression instead. Linear Regression BasicsĪ simple linear regression model uses a straight line to estimate the relationship between two quantitative variables. The larger the error term, the less certain would be your regression line. And the line merely gives you an estimate based on the data available. It takes into account the reality that independent predictors may not always be perfect predictors of dependent variables. Typically, the regression line formula also includes an error term (Y = 200 + 3 X + error term).

You would sell 203 units if it rains 1 inch, 206 units if it rains 2 inches, 209 inches if it rains 3 inches, and so on.

Assuming that the variables stay the same as we advance, every additional inch of rain would result in an average sales of three more units. It tells you that you sold 200 units when it didn’t rain at all (i.e., when X=0). In addition, you can also output a formula explaining the slope of the line.Ĭonsider this formula for the above example: Y = 200 + 3X.

#Linear regression equation example software#

Nowadays, Excel and statistics software like SPSS, R, or STATA can help you draw a line that best fits the data at hand. You could get some degree of certainty if you drew a line through the middle of all data points on the chart. But it would be tricky to estimate how much you would typically sell when it rained a certain amount, say 3 or 4 inches. Presume the sales to be higher on the days it rained more. If you take another glance at the data, you might notice a pattern. Each dot on the chart would show how much it rained during a particular month and the corresponding sales numbers. The y-axis represents the number of sales (dependent variable), and the x-axis depicts the total rainfall. Let’s say that you plotted this information on a chart. Suppose you have data on the monthly sales and average monthly rainfall for the past three years. But in this blog, we will mainly focus on the linear regression model where both variables are quantitative. It may be binary in the case of logistic regression. Regression models are used when the dependent variable is quantitative.

Independent: Factors that you suspect to have an impact on the dependent variable.

Dependent: Factor that you are attempting to predict or understand.

It also determines how certain we are about the factors involved. It is a mathematical analysis that sorts out which variables have an impact and matter the most. To Explore all our certification courses on AI & ML, kindly visit our page below.Ī regression model describes the relationship between dataset variables by fitting a line to the data observed. Master of Science in Machine Learning & AI from LJMUĮxecutive Post Graduate Programme in Machine Learning & AI from IIITBĪdvanced Certificate Programme in Machine Learning & NLP from IIITBĪdvanced Certificate Programme in Machine Learning & Deep Learning from IIITBĮxecutive Post Graduate Program in Data Science & Machine Learning from University of Maryland