Basic Estimation Techniques
... • Must determine if there is sufficient statistical evidence to indicate that Y is truly related to X (i.e., b 0) • Even if b = 0, it is possible that the sample will produce an estimate b̂ that is different from zero • Test for statistical significance using t-tests or p-values ...
... • Must determine if there is sufficient statistical evidence to indicate that Y is truly related to X (i.e., b 0) • Even if b = 0, it is possible that the sample will produce an estimate b̂ that is different from zero • Test for statistical significance using t-tests or p-values ...
Econometrics Lecture Notes
... 1. A loss of efficiency. If disturbances are correlated, then previous values of the disturbance have some information to convey about the current disturbance. If this information is ignored, it is clear that the sample data are not being used with maximum efficiency. 2. If there is a positive autoc ...
... 1. A loss of efficiency. If disturbances are correlated, then previous values of the disturbance have some information to convey about the current disturbance. If this information is ignored, it is clear that the sample data are not being used with maximum efficiency. 2. If there is a positive autoc ...
Worksheet of Nov. 22
... A natural goal is to try to use the distance of a destination to predict the airfare for flying there, and the simplest model for this prediction is to assume that a straight line summarizes the relationship between distance and airfare. 1. Place a straightedge over the scatterplot above so that the ...
... A natural goal is to try to use the distance of a destination to predict the airfare for flying there, and the simplest model for this prediction is to assume that a straight line summarizes the relationship between distance and airfare. 1. Place a straightedge over the scatterplot above so that the ...
critical t
... • Ten regions with data on promotional expenditures (X) and sales (Y), selling price (P), and disposable income (M) • If look only at Y and X: Result: Y = 120.755 + .434 X • One use of a regression is to make predictions. • If a region had promotional expenditures of 185, the prediction is Y = 201.0 ...
... • Ten regions with data on promotional expenditures (X) and sales (Y), selling price (P), and disposable income (M) • If look only at Y and X: Result: Y = 120.755 + .434 X • One use of a regression is to make predictions. • If a region had promotional expenditures of 185, the prediction is Y = 201.0 ...
data analysis - DCU School of Computing
... Simply; - the 2 distribution is the sum of k squares of independent random variables, i.e. defined in a k-dimensional space. Constraints: e.g. forcing sum of observed and expected observations in a row or column to be equal, or e.g. estimating a parameter of parent distribution from sample values, ...
... Simply; - the 2 distribution is the sum of k squares of independent random variables, i.e. defined in a k-dimensional space. Constraints: e.g. forcing sum of observed and expected observations in a row or column to be equal, or e.g. estimating a parameter of parent distribution from sample values, ...
Slide 1
... Variation explained e.g. r = 0.661 r2 = = 0.44 less than half of the variation (44%) in the dependent variable due to independent variable ...
... Variation explained e.g. r = 0.661 r2 = = 0.44 less than half of the variation (44%) in the dependent variable due to independent variable ...
Chapter 14, part C
... A. Total Sum of Squares (SST) If you had to estimate repair cost but had no knowledge of the car’s age, what would be your best guess? Probably the mean repair cost. If we subtract each yi from the mean, we calculate the error involved in using the mean to estimate cost. I hope our regression equat ...
... A. Total Sum of Squares (SST) If you had to estimate repair cost but had no knowledge of the car’s age, what would be your best guess? Probably the mean repair cost. If we subtract each yi from the mean, we calculate the error involved in using the mean to estimate cost. I hope our regression equat ...
Coefficient of determination
In statistics, the coefficient of determination, denoted R2 or r2 and pronounced R squared, is a number that indicates how well data fit a statistical model – sometimes simply a line or a curve. An R2 of 1 indicates that the regression line perfectly fits the data, while an R2 of 0 indicates that the line does not fit the data at all. This latter can be because the data is utterly non-linear, or because it is random.It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model (pp. 187, 287).There are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. In this case, if an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the outcomes and their predicted values. If additional explanators are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination ranges from 0 to 1.Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions that are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept. Additionally, negative values of R2 may occur when fitting non-linear functions to data. In cases where negative values arise, the mean of the data provides a better fit to the outcomes than do the fitted function values, according to this particular criterion.