📉Intro to Business Statistics Unit 13 – Linear Regression and Correlation
Linear regression is a powerful statistical tool used to analyze relationships between variables. It helps predict outcomes based on one or more factors, making it valuable in fields like finance, marketing, and healthcare. Understanding its key concepts and applications is crucial for data-driven decision-making.
This study guide covers the fundamentals of linear regression, including its mathematical foundations and types. It also explores important assumptions, real-world applications, and common pitfalls to avoid when using this technique. Mastering these concepts will enhance your ability to interpret data and make informed predictions.
Study Guides for Unit 13
What's Linear Regression?
Linear regression analyzes the linear relationship between a dependent variable and one or more independent variables
Helps predict the value of the dependent variable based on the values of the independent variables
Represented by the equation y=β0+β1x+ϵ, where y is the dependent variable, x is the independent variable, β0 is the y-intercept, β1 is the slope, and ϵ is the error term
The goal is to find the line of best fit that minimizes the sum of squared residuals (differences between observed and predicted values)
Can be used for both simple linear regression (one independent variable) and multiple linear regression (two or more independent variables)
Allows for hypothesis testing to determine the statistical significance of the relationship between variables
Provides a measure of the strength of the relationship through the coefficient of determination (R2)
Key Concepts and Terms
Dependent variable (response variable) is the variable being predicted or explained by the independent variable(s)
Independent variable (predictor variable) is the variable used to predict or explain the dependent variable
Slope (β1) represents the change in the dependent variable for a one-unit change in the independent variable
Y-intercept (β0) is the value of the dependent variable when the independent variable is zero
Residuals are the differences between the observed values and the predicted values from the regression line
Coefficient of determination (R2) measures the proportion of variance in the dependent variable explained by the independent variable(s)
Ranges from 0 to 1, with higher values indicating a stronger relationship
P-value is used to determine the statistical significance of the relationship between variables
A p-value less than the chosen significance level (e.g., 0.05) indicates a statistically significant relationship
Confidence interval is a range of values that is likely to contain the true value of a population parameter with a certain level of confidence (e.g., 95%)
The Math Behind It
The least squares method is used to estimate the parameters (β0 and β1) of the linear regression model
The goal is to minimize the sum of squared residuals (SSR): SSR=∑i=1n(yi−y^i)2, where yi is the observed value and y^i is the predicted value
The normal equations are used to solve for the parameters:
∑i=1nyi=nβ0+β1∑i=1nxi
∑i=1nxiyi=β0∑i=1nxi+β1∑i=1nxi2
The slope (β1) is calculated as: β1=∑i=1n(xi−xˉ)2∑i=1n(xi−xˉ)(yi−yˉ)
The y-intercept (β0) is calculated as: β0=yˉ−β1xˉ
Hypothesis testing is performed using the t-test for the significance of the slope and the F-test for the overall significance of the regression model
Confidence intervals for the parameters are calculated using the standard errors and the t-distribution with n−2 degrees of freedom
Types of Linear Regression
Simple linear regression involves one independent variable and one dependent variable
Example: predicting sales revenue (dependent variable) based on advertising expenditure (independent variable)
Multiple linear regression involves two or more independent variables and one dependent variable
Example: predicting house prices (dependent variable) based on square footage, number of bedrooms, and location (independent variables)
Polynomial regression is a type of linear regression that includes polynomial terms of the independent variable(s)
Example: predicting the relationship between age (independent variable) and income (dependent variable) using a quadratic term (age2)
Stepwise regression is a method for selecting the most relevant independent variables in a multiple linear regression model
Forward selection starts with no variables and adds the most significant variable at each step
Backward elimination starts with all variables and removes the least significant variable at each step
Ridge regression and lasso regression are regularization techniques used to address multicollinearity and improve the stability of the estimates
Ridge regression adds a penalty term to the sum of squared residuals to shrink the coefficients towards zero
Lasso regression adds a penalty term that can set some coefficients exactly to zero, effectively performing variable selection
Correlation vs. Regression
Correlation measures the strength and direction of the linear relationship between two variables
Pearson's correlation coefficient (r) ranges from -1 to 1, with -1 indicating a perfect negative correlation, 1 indicating a perfect positive correlation, and 0 indicating no correlation
Does not imply causation and does not provide a predictive model
Regression analyzes the relationship between a dependent variable and one or more independent variables
Provides a predictive model that can be used to estimate the value of the dependent variable based on the values of the independent variables
Can be used to infer causality, but only if certain assumptions are met (e.g., no confounding variables, no reverse causality)
Correlation is a necessary but not sufficient condition for regression
A strong correlation between variables is required for a meaningful regression model
However, a strong correlation does not guarantee that a regression model will be accurate or useful
Regression can be used to quantify the relationship between variables, while correlation only provides a measure of the strength and direction of the relationship
Assumptions and Limitations
Linearity assumes that the relationship between the dependent variable and the independent variable(s) is linear
Violations can lead to biased and inefficient estimates
Can be checked using residual plots and tests for linearity (e.g., RESET test)
Independence assumes that the observations are independent of each other
Violations can occur with time series data or clustered data
Can be checked using the Durbin-Watson test for autocorrelation
Homoscedasticity assumes that the variance of the residuals is constant across all levels of the independent variable(s)
Violations (heteroscedasticity) can lead to inefficient estimates and invalid inference
Can be checked using residual plots and tests for heteroscedasticity (e.g., Breusch-Pagan test)
Normality assumes that the residuals are normally distributed
Violations can affect the validity of hypothesis tests and confidence intervals
Can be checked using normal probability plots and tests for normality (e.g., Shapiro-Wilk test)
No multicollinearity assumes that the independent variables are not highly correlated with each other
Violations can lead to unstable and unreliable estimates
Can be checked using the variance inflation factor (VIF) and correlation matrix
Outliers and influential observations can have a significant impact on the regression results
Can be identified using residual plots, leverage values, and Cook's distance
May need to be removed or addressed using robust regression techniques
Real-World Applications
Finance: predicting stock prices based on economic indicators and company performance metrics
Marketing: analyzing the impact of advertising expenditure on sales revenue and market share
Healthcare: identifying risk factors for diseases and predicting patient outcomes based on clinical variables
Real estate: estimating property values based on location, size, and amenities
Economics: studying the relationship between economic growth and factors such as investment, education, and trade
Social sciences: investigating the determinants of income inequality, crime rates, and political preferences
Environmental studies: modeling the impact of climate change on biodiversity and ecosystem services
Sports analytics: predicting player performance based on past statistics and physical attributes
Common Pitfalls and How to Avoid Them
Overfitting occurs when a model is too complex and fits the noise in the data rather than the underlying pattern
Can be avoided by using cross-validation, regularization techniques, and model selection criteria (e.g., adjusted R2, AIC, BIC)
Underfitting occurs when a model is too simple and fails to capture the true relationship between variables
Can be avoided by including relevant variables, considering non-linear relationships, and using more flexible models (e.g., polynomial regression, splines)
Extrapolation involves making predictions outside the range of the observed data
Can lead to unreliable and inaccurate predictions
Should be avoided or done with caution, acknowledging the increased uncertainty
Confounding variables are variables that are related to both the dependent and independent variables, causing a spurious relationship
Can be addressed by including the confounding variables in the model or using techniques such as instrumental variables and propensity score matching
Misinterpretation of coefficients can occur when the units or scales of the variables are not carefully considered
Standardizing or centering the variables can make the coefficients more interpretable
The interpretation should always be in the context of the specific units and scales used
Ignoring practical significance focuses solely on statistical significance and overlooks the practical implications of the results
The magnitude and direction of the coefficients should be considered in addition to their statistical significance
The results should be interpreted in the context of the specific application and the decision-making process