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The procedures for extracting the pattern in a series of observations when this is obscured by the noise. Basically any such technique or method separates the original series into a smooth sequence and the residual sequence (usually called the 'rough'). For instance, a smoother can separate seasonal Fluctuations from the briefer events such as identifiable peaks and random noise. A simple example of such a process is the moving average; a more complex one is locally weighted regression.
Law of likelihood : Within framework of the statistical model, a particular set of data supports one statistical hypothesis or assumption better than another if the likelihood of t
It is used generally for the matrix which specifies a statistical model for a set of observations. For instance, in a one-way design with the three observations in one group, tw
Homoscedasticity - Reasons for Screening Data Homoscedasticity is the assumption that the variability in scores for a continuous variable is roughly the same at all values of
Hi , Im currently taking the course Financial Econometrics of Master of Finance at RMIT. I find it really difficult to understand the course''s material and now im having the majo
Half-normal plot is a plot for diagnosing the model inadequacy or revealing the presence of outliers, in which the absolute values of, for instance, the residuals from the multipl
sales per day for a product are as follows: x= 10, 11, 12, 13 (p)= 0.2, 0.4, 0.3, 0.1 obtain mean and variance of daily sale. if the profit is described by the following equation p
VIF is the abbreviation of variance inflation factor which is a measure of the amount of multicollinearity that exists in a set of multiple regression variables. *The VIF value
Bartlett's test for variances : A test for equality of the variances of the number (k)of the populations. The test statistic can be given as follows where s square is an
In a mathematics examination the average grade was 82 and the standard deviation was 5. all students with grade from 88 to 94 received grade of B. if the grade are approximately no
Quantile regression is an extension of the classical least squares from estimation of the conditional mean models to the estimation of the variety of models for many conditional q
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