Statistical Reasoning Student Notes
... Learning Outcome: Learn to use z-scores to compare data, make predictions, and solve problems. What are z-scores? A common statistical way of standardizing data on one scale so a comparison can take place is using a z-score. The z-score is like a common yard stick for all types of data. Each z-score ...
... Learning Outcome: Learn to use z-scores to compare data, make predictions, and solve problems. What are z-scores? A common statistical way of standardizing data on one scale so a comparison can take place is using a z-score. The z-score is like a common yard stick for all types of data. Each z-score ...
Slide 1
... is the ratio of the likelihood of the reduced model to that of the full model If is near 1, 1 contributes little If is <1, 1 has an effect G2 = -2 ln() Log-likelihood 2 or G statistic G2 = - 2 (log-likelihood reduced – log-likelihood full) G2 follows 2 with 1 df ...
... is the ratio of the likelihood of the reduced model to that of the full model If is near 1, 1 contributes little If is <1, 1 has an effect G2 = -2 ln() Log-likelihood 2 or G statistic G2 = - 2 (log-likelihood reduced – log-likelihood full) G2 follows 2 with 1 df ...
STP226, Summer 99 Review notes for Test #1
... What is an outlier and influential observation? How to check if observation is influential. Know about residual plots and how they may indicate problems with the regression. Know what is the meaning of the slope of the regression line and what are its units. Is r and r2 changing with units change i ...
... What is an outlier and influential observation? How to check if observation is influential. Know about residual plots and how they may indicate problems with the regression. Know what is the meaning of the slope of the regression line and what are its units. Is r and r2 changing with units change i ...
chapter 3
... Range = Max. value – Min. value - the difference between the largest and smallest observations. Two data sets may have the same mean but different ranges. The Sample Standard Deviation – measures variation - tells how far, on average, the observations are from the mean - like the mean, it is not a r ...
... Range = Max. value – Min. value - the difference between the largest and smallest observations. Two data sets may have the same mean but different ranges. The Sample Standard Deviation – measures variation - tells how far, on average, the observations are from the mean - like the mean, it is not a r ...
Time series
A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).