Linear regression is used to model and quantify the relationship between a dependent variable and one or more independent variables. It’s employed when:
Modeling Relationships: Assumes a linear relationship between variables. Predictive Modeling: Predicts values based on historical data.
Understanding Variable Influence: Identifies and quantifies the impact of predictors on the response.
Hypothesis testing :
1. The hypothesis test for linear regression determines whether variables are related.
2. Alternative hypothesis (\(H_1\)): A link exists; Null hypothesis (\(H_0\)): No link (\(\beta = 0\)).
3. Locate the regression model’s coefficients using the data.
4. Examine the coefficient significance of a statistic (such as the t-statistic).
5. Make a decision based on p-values: If \(H_0\) indicates a substantial association, reject it; if not, continue it.
In the coming CDC2018 data set we will be using linear regression to predict relation between diabetes and inactivity and obesity.