Multiple Linear Regression is a statistical technique used to model the relationship between multiple variables and a single dependent variable. It is used when we want to predict the value of a dependent variable based on the values of one or more independent variables.
In simple terms, it is used to find the best-fit line that represents the relationship between the independent variables and the dependent variable. The best-fit line is the line that minimizes the sum of the squared errors between the predicted values and the actual values.
The multiple linear regression model assumes that the relationship between the dependent variable and the independent variables is linear. It also assumes that the errors between the predicted values and the actual values are normally distributed.
The multiple linear regression model is widely used in various fields such as finance, economics, and marketing to predict the outcome of a dependent variable based on the values of one or more independent variables. It is also used in machine learning to predict the outcome of a dependent variable based on the values of one or more independent variables.
In summary, multiple linear regression is a powerful statistical technique used to model the relationship between multiple variables and a single dependent variable. It is widely used in various fields to predict the outcome of a dependent variable based on the values of one or more independent variables.
Dummy variables are a type of statistical technique used in data analysis and machine learning. They are binary variables, which take on the values of 0 or 1, and are used to represent categorical variables in a statistical model.
In a regression analysis, for example, a dummy variable is used to represent whether a particular predictor variable is present or absent in a sample. For example, if we are studying the effect of two different treatments on a response variable, we might use a dummy variable to represent whether the treatment was applied or not.
In a logistic regression model, for example, a dummy variable is used to represent the presence or absence of a particular predictor variable in a sample. For example, if we are studying the effect of two different treatments on a response variable, we might use a dummy variable to represent whether the treatment was applied or not.
In summary, dummy variables are used to represent categorical variables in a statistical model, and they are binary variables that take on the values of 0 or 1. They are commonly used in regression and logistic regression models to represent predictor variables that take on a limited number of values.
| Profit | R&D Spend | Admin | Marketing | State |
|---|---|---|---|---|
| 192, 261.83 | 165, 349.20 | 136, 897.80 | 471, 784.10 | New York |
| 191, 792.06 | 162, 597.70 | 151, 377.59 | 443, 898.53 | California |
| 191, 050.39 | 153, 441.51 | 101, 145.55 | 407, 934.54 | California |
| 182, 901.99 | 144, 372.41 | 118, 671.85 | 383, 199.62 | New York |
| 166, 187.94 | 142, 107.34 | 91, 391.77 | 366, 168.42 | California |
P > SL, go to STEP 4, otherwise go to FIN (Finish)NOTE: The “*” represents important
y - xn Select the one with the lowest P-valueP < SL, go to STEP 3, otherwise go to FINP < SLENTER to enter)P < SLSTAY to stay)2N-1 total combinations # Import necessary libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# Load your dataset (replace 'your_dataset.csv' with your actual file)
# Example: df = pd.read_csv('your_dataset.csv')
# Ensure that your dataset includes multiple independent variables (features) and the target variable (dependent variable).
# For demonstration, let's generate a synthetic dataset:
np.random.seed(42)
X = 2 * np.random.rand(100, 3) # 3 features
y = 4 + 3 * X[:, 0] + 2 * X[:, 1] + np.random.randn(100)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create a Multiple Linear Regression model
model = LinearRegression()
# Train the model on the training data
model.fit(X_train, y_train)
# Make predictions on the test data
y_pred = model.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
print(f"Root Mean Squared Error: {rmse}")
# Print the coefficients and intercept
print("Coefficients:", model.coef_)
print("Intercept:", model.intercept_)
# Note: Make sure to replace the column names and dataset with your own data.
# You can also perform feature scaling, feature engineering, or other preprocessing steps based on your dataset.
| «Previous | Next» |