![]() Only use the model to make predictions within the range of data used to estimate the regression model.įor example, suppose we fit a regression model using the predictor variable “weight” and the weight of individuals in the sample we used to estimate the model ranged between 120 pounds and 180 pounds. Keep in mind the following when using a regression model to make predictions:ġ. We would interpret this interval to mean that we’re 95% confident that the true height of this individual is between 64.8 inches and 68.8 inches. So, to capture this uncertainty we can create a confidence interval – a range of values that is likely to contain a population parameter with a certain level of confidence.įor example, instead of predicting that a new individual will be 66.8 inches tall, we may create the following confidence interval:ĩ5% Confidence Interval = When using a regression model to make predictions on new observations, the value predicted by the regression model is known as a point estimate.Īlthough the point estimate represents our best guess for the value of the new observation, it’s unlikely to exactly match the value of the new observation. Using the model, we would predict that this individual would have a yearly income of $85,166.77: He can then use the model to predict the yearly income of a new individual based on their total years of schooling and weekly hours worked.įor example, suppose a new individual has 16 years of total schooling and works an average of 40 hours per week. ![]() Income = 1,342.29 + 3,324.33*(years of schooling) + 765.88*(weekly hours worked)Īfter checking that the assumptions of the linear regression model are met, the economist concludes that the model fits the data well. He then fits a multiple linear regression model using “total years of schooling” and “weekly hours worked” as the predictor variable and “yearly income” as the response variable. Suppose an economist collects data for total years of schooling, weekly hours worked, and yearly income on 30 individuals. Height = 32.7830 + 0.2001*(170) = 66.8 inches Example 2: Make Predictions with a Multiple Linear Regression Model Using the model, we would predict that this patient would have a height of 66.8 inches: He can then use the model to predict the height of new patients based on their weight.įor example, suppose a new patient weighs 170 pounds. The fitted regression equation is as follows:Īfter checking that the assumptions of the linear regression model are met, the doctor concludes that the model fits the data well. She then fits a simple linear regression model using “weight” as the predictor variable and “height” as the response variable. Suppose a doctor collects data for height (in inches) and weight (in pounds) on 50 patients. Example 1: Make Predictions with a Simple Linear Regression Model The following examples show how to use regression models to make predictions.
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