Linear Regression
Topic Review on "Title": 
 Model: an equation used to characterize the relationship between variables and predict future outcomes.
 Models: we can specifically model the relationship of variables with a line and its equation.
 Models use parameters are numbers, or numbers that are derived from the data.
 Recall that the Normal model was specified with mean µ and standard deviation σ.
 A linear model is the equation of a straight line through the data.
 Similar to the slopeintercept form of a line, y = mx + b.
 Predicted Value: is the estimate made from a model, known as (yhat).
 Residual: the difference between the observed value and the predicted value of an observation, otherwise known as error.
 Residual =
 Line of Best Fit: The line for which the sum of the squared residuals is minimized, known as the least squares regression line.
 Residual Plot: plots residuals on the vertical axis and the explanatory variable on the horizontal axis.
 Influential Observations: Are those observations that markedly change the position of the regression line.
 R2, coefficient of determination: the proportion of variability of y accounted for by the least squares regression on x.
 Extrapolation: the use of a regression line for prediction outside the domain of values of the explanatory variable we used to obtain the line.
 Least Squares Regression Equation,
 Slope,
 Yintercept,
 Least Squares Regression Line: Also called the best line of fit.
 This line minimizes residuals between the line and the observed values.
 The yintercept is the value of y when x = 0
 The slope says that a change of one standard deviation of x corresponds to a change of r standard deviations in y along the regression line
 In other words, the y units per every x unit

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"Title" Tutorial Summary : 
This tutorial shows the definitions of linear regression. Basically, a model is an equation used to characterize the relationship between variables and predict future outcomes. A linear model is the equation of a straight line through the data, similar to the slopeintercept form of a line, y = mx + b.
Therefore by completing this tutorial, you will be able to find a linear regression model that best fit the data (minimized the residuals) and thus able to predict future response values or outcomes.

Tutorial Features: 
Specific Tutorial Features:
 Residuals can be used to work out and illustrate the linear regression example problems, step by step.
 Examples showing how to build a model and find the line of fit are provided.
Series Features:
 Concept map showing interconnections of new concepts in this tutorial and those previously introduced.
 Definition slides introduce terms as they are needed.
 Visual representation of concepts
 Animated examples—worked out step by step
 A concise summary is given at the conclusion of the tutorial.

"Title" Topic List: 
 Model
 Predicted Value
 Residual
 Residual Plot
 Influential Observations
 Verify the fit of a regression line
 Identify, calculate and interpret r2, the coefficient of determination

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