R lm() Function - Linear Regression Model
R Language ExamplesThe R lm() function is used to fit linear regression models and analyze the linear relationship between independent and dependent variables.
Linear regression is one of the most fundamental and important tools in statistical modeling.
The syntax of the lm() function is as follows:
lm(formula, data)
Parameter description:
formulaFormula, in the format y ~ x1 + x2 + ..., where y is the dependent variable and x are the independent variables.
dataData frame, containing the variables in the formula.
Example
# Advertising spend and sales data
ad_spend <- c(10, 15, 12, 18, 20, 14, 22, 16, 25, 19)
sales <- c(50, 65, 55, 72, 80, 60, 88, 68, 95, 75)
# Fit the linear model
model <- lm(sales ~ ad_spend)
# View the model summary
print(summary(model))
# Predict new data
new_spend <- data.frame(ad_spend = c(17, 23))
predictions <- predict(model, new_spend)
print(paste("Predicted sales:", predictions))
ad_spend <- c(10, 15, 12, 18, 20, 14, 22, 16, 25, 19)
sales <- c(50, 65, 55, 72, 80, 60, 88, 68, 95, 75)
# Fit the linear model
model <- lm(sales ~ ad_spend)
# View the model summary
print(summary(model))
# Predict new data
new_spend <- data.frame(ad_spend = c(17, 23))
predictions <- predict(model, new_spend)
print(paste("Predicted sales:", predictions))
Executing the above code produces the following output:
Call:
lm(formula = sales ~ ad_spend)
Residuals:
Min 1Q Median 3Q Max
-4.3780 -1.5831 -0.4329 1.8202 4.9347
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.1622 3.4394 4.990 0.00106 **
ad_spend 3.0623 0.1933 15.839 2.42e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.829 on 8 degrees of freedom
Multiple R-squared: 0.9691, Adjusted R-squared: 0.9652
F-statistic: 250.9 on 1 and 8 DF, p-value: 2.422e-07
[1] "预测销售额: 69.2218150385764 87.5953501640316"
R-squared is 0.9691, indicating that advertising spend can explain about 97% of the variation in sales.
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