Question

Residuals: how can you find the predicted value of random values? I DONT GET IT!!!!!!!!!!!

Answer 1

To find the predicted value of random values using residuals, you can follow these steps:
1. Start with a regression model:
Residuals are typically used in the context of regression analysis, which involves predicting a response variable based on one or more predictor variables. First, you need to establish a regression model that relates the predictor variables to the response variable
2.Fit the regression model: Use statistical software or tools to fit the regression model to your data. This process estimates the coefficients of the model, which represent the relationships between the predictor variables and the response variable.
3. Calculate the predicted values: Once the model is fitted, you can calculate the predicted values for the response variable. These predicted values represent the expected values of the response variable based on the given
4. Calculate the residuals: After obtaining the predicted values, subtract them from the actual observed values of the response variable. The resulting differences are the residuals. Each residual represents the deviation between the observed value and the predicted value for a specific data point.
5. Use the residuals to find the predicted value: If you have random values and want to find their predicted values, you can add the residuals to the mean or median of the response variable. This assumes that the residuals follow a symmetric distribution with a mean or median of zero.

Answer 2

How do you find the predicted value in R?

The predict() function in R is used to predict the values based on the input data.

predict(object, newdata, interval)

df <- datasets::cars.

# Creates a linear model my_linear_model <- lm(dist~speed, data = df) # Prints the model results my_linear_model.

Explanation:

no