Simple Linear Regression is a modeling technique. It is based on correlation and can be used to explore the relationship between one continuous dependent variable and one continuous independent variable or predictor. In this way, we can find out whether an independent variable can make significant unique contribution to the prediction of the dependent variable. In addition, Simple Linear Regression can be used to explore the predictive ability of the model. Simple Linear Regression will provide us with information about the model as a whole and the relative contribution of the independent variable that make up the model. In other words, Simple Linear Regression tells us how much of the variance in our dependent variable can be explained by our independent variable. It also gives us an indication of the relative contribution of our independent variable. To put it in a nutshell, Simple Linear Regression is used
What we need: One continuous dependent variable and one continuous independent variable (we can also use dichotomous independent variables, e.g. males=l, females=2.) Assumptions: Before performing this analysis, we should check several assumptions such as Normality, linearity, homoscedasticity, independence of observations, and Outliers. Steps in Regression AnalysisSimple Linear Regression consists of two parts: Descriptive & Inferential Descriptive: 1. The Correlation Coefficient (r): Based on r value, we describe the strength and direction of the linear relationship between two continuous variables 2. The Coefficient of Determination (R Square): Based on R square, we can say that how much of the total variance in the dependent variable is uniquely explained by an independent variable. 3. Prediction Equation or Regression Model: Regression Model involves statistics and parameters, namely: Intercept and slope of the regression line. 2. Inferential we should Test two objectives: 1. To explore predictive ability of the model (To determine whether the model really fits the data. 2. To determine the significance of the relationship between IV and DV ( To test whether an independent variable is making significant unique contribution to the prediction of the dependent variable.) Linear Regression using Excel |
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