Textbook Notes (363,452)
Economics (479)
ECO220Y1 (33)
Chapter 7

# ECO220Y1 Chapter 7 Notes Premium

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School
University of Toronto St. George
Department
Economics
Course
ECO220Y1
Professor
Jennifer Murdock
Semester
Fall

Description
ECO220Y1 Textbook Notes Chapter 7: Introduction to Linear Regression 7.1 The Linear Model  Linear model: an equation of a straight line through the data. o Does not have to touch any point (it is the line that comes closest to all values). o Written in the form of ̂ where and are numbers estimated from the data and ̂ is the predicted value.  The hat on y is used to distinguish the predicted value from the observed, y, value.  Predicted value: the prediction for y found for each x-value in the data. A predicted value, ̂, is found by substituting the x-value in the regression equation. o The predicted values are the values on the fitted line; (x, ̂) lies exactly on the fitted line.  Residual: the difference between the actual data value and the corresponding value predicted by the regression model (or any model). o ̂  Line of best fit: the line for which the sum of the squared residuals is smallest – often called the least squares line. o It has a special property that the variation of data around the model (as seen in the residuals) is the smallest it can be for any straight line model for these data. 7.2 Correlation and the Line  Slope: tells us how the response variable changes for a one-unit step in the predictor variable. o The slope of the least squares line is: o r = correlation; s xnd s aye the s.d.’s of x and y respectively. o The slope gets it’s units from the ratio of the s.d.’s and is expressed as the units of y per unit of x.  Intercept: gives us a starting value in y-units (x = 0). o It usually has no meaning. o By subbing in the average values of y and x, we can find the intercept: ̅ ̅ /re-arrange\ ̅ ̅  Regression line: the particular linear equation that satisfies the least squares criterion, often called the line of best fit.  Same conditions have to be checked for regression like was done for correlation: o Quantitative Variables Condition o Linearity Condition o Outlier Condition  For z-scored data: ̅ ̅ ̅ ̅ ̂  In general, moving any number of s.d.’s in x moves our prediction r times that number of s.d.’s in y. 7.3 Regression to the Mean  Regression to the mean: because the correlation is always less than 1.0 in magnitude, each predicted y tends to be fewer s.d.’s from its mean than its corresponding x from its mean.  Two regression lines can be made: o One where x is the explanatory variable.  Minimize the vertical distances between the points and the line. o One where y is the explanatory variable.  Minimize the horizontal distances between the points and the line. o If correlation = 1, the two lines are identical and all the data points lie exactly on the one line. 7.4 Checking the Model  Linear models make sense only for quantitative data.  Check the scatterplot to determine if the relationship between the two variables is reasonably straight.  Outlying points can dramatically change a regression model. o They may have large residuals that, when squared, have a large impact.  So large they may in fact change the sign of the slope. 7.5 Learning More from the Residuals  Plotting the residuals (y-axis) against t
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