This Multiple Linear Regression Calculator helps you analyze the relationship between a dependent variable and multiple independent variables. It provides comprehensive analysis including model summary statistics, coefficient estimates, confidence intervals, and diagnostic tests. The calculator also generates diagnostic plots to check regression assumptions. To learn about the data format required and test this calculator, .
No variables available. Please enter data in the table above.
Multiple Linear Regression models the relationship between a dependent variable and two or more independent variables, assuming a linear relationship. It extends simple linear regression to account for multiple predictors.
Where:
Sum of Squares:
Where is the predicted value and is the mean
R-squared:
Adjusted R-squared:
Housing prices model:
| House | Price (K) | Sqft | Age | Bedrooms |
|---|---|---|---|---|
| 1 | 300 | 1500 | 15 | 3 |
| 2 | 250 | 1200 | 20 | 2 |
| 3 | 400 | 2000 | 10 | 4 |
| 4 | 550 | 2400 | 5 | 4 |
| 5 | 317 | 1600 | 12 | 3 |
| 6 | 389 | 1800 | 8 | 3 |
Design matrix X:
Coefficients calculation:
Fitted equation:
Key diagnostic measures:
library(tidyverse)
library(broom)
data <- tibble(
price = c(300, 250, 400, 550, 317, 389),
sqft = c(1500, 1200, 2000, 2400, 1600, 1800),
age = c(15, 20, 10, 5, 12, 8),
bedrooms = c(3, 2, 4, 4, 3, 3)
)
model <- lm(price ~ sqft + age + bedrooms, data = data)
tidy(model)
glance(model)import pandas as pd
import numpy as np
from statsmodels.formula.api import ols
import statsmodels.api as sm
df = pd.DataFrame({
'price': [300, 250, 400, 550, 317, 389],
'sqft': [1500, 1200, 2000, 2400, 1600, 1800],
'age': [15, 20, 10, 5, 12, 8],
'bedrooms': [3, 2, 4, 4, 3, 3]
})
model = ols('price ~ sqft + age + bedrooms', data=df).fit()
print(model.summary())
print("Coefficients:")
print(model.params)
print("R-squared:", model.rsquared)Consider these alternatives:
This Multiple Linear Regression Calculator helps you analyze the relationship between a dependent variable and multiple independent variables. It provides comprehensive analysis including model summary statistics, coefficient estimates, confidence intervals, and diagnostic tests. The calculator also generates diagnostic plots to check regression assumptions. To learn about the data format required and test this calculator, .
No variables available. Please enter data in the table above.
Multiple Linear Regression models the relationship between a dependent variable and two or more independent variables, assuming a linear relationship. It extends simple linear regression to account for multiple predictors.
Where:
Sum of Squares:
Where is the predicted value and is the mean
R-squared:
Adjusted R-squared:
Housing prices model:
| House | Price (K) | Sqft | Age | Bedrooms |
|---|---|---|---|---|
| 1 | 300 | 1500 | 15 | 3 |
| 2 | 250 | 1200 | 20 | 2 |
| 3 | 400 | 2000 | 10 | 4 |
| 4 | 550 | 2400 | 5 | 4 |
| 5 | 317 | 1600 | 12 | 3 |
| 6 | 389 | 1800 | 8 | 3 |
Design matrix X:
Coefficients calculation:
Fitted equation:
Key diagnostic measures:
library(tidyverse)
library(broom)
data <- tibble(
price = c(300, 250, 400, 550, 317, 389),
sqft = c(1500, 1200, 2000, 2400, 1600, 1800),
age = c(15, 20, 10, 5, 12, 8),
bedrooms = c(3, 2, 4, 4, 3, 3)
)
model <- lm(price ~ sqft + age + bedrooms, data = data)
tidy(model)
glance(model)import pandas as pd
import numpy as np
from statsmodels.formula.api import ols
import statsmodels.api as sm
df = pd.DataFrame({
'price': [300, 250, 400, 550, 317, 389],
'sqft': [1500, 1200, 2000, 2400, 1600, 1800],
'age': [15, 20, 10, 5, 12, 8],
'bedrooms': [3, 2, 4, 4, 3, 3]
})
model = ols('price ~ sqft + age + bedrooms', data=df).fit()
print(model.summary())
print("Coefficients:")
print(model.params)
print("R-squared:", model.rsquared)Consider these alternatives: