0
What else can I help you with?
When can the standard error of the estimate be used to construct a prediction interval about a value y'?
You need the data to be homoscedastic, the errors to be independent. The independent variable(s) should lie within (or very close to) the range of observed values.
What is a prediction and how do we make prediction from data?
Dan Henderson vs. Rashad Evans Prediction
Are IQ scores ordinal or interval?
They are interval.
Is elevation interval data or ratio?
interval
What is a valid prediction range?
The valid prediction range is the range of the "predictor."
How does one do Exponential Smoothing?
There are many ways one might use Exponential Smoothing. Basically, Exponential Smoothing is a simple calculation one uses to collect data that allows one to predict future events.
What advantages as a forecasting tool does exponential smoothing have over moving average?
When implemented digitally, exponential smoothing is easier to implement and more efficient to compute, as it does not require maintaining a history of previous input data values. Furthermore, there are no sudden effects in the output as occurs with a moving average when an outlying data point passes out of the interval over which you are averaging. With exponential smoothing, the effect of the unusual data fades uniformly. (It still has a big impact when it first appears.)
When the confidence interval is wider than a prediction interval?
That, my friend, is not a question.
What are forecasting models?
1) forecasting for stationary series A- Moving average B- Exponential Smoothing 2) For Trends A- Regression B- Double Exponential Smoothing 3) for Seasonal Series A- Seasonal factor B- Seasonal Decomposition C- Winters's methode
When comparing the 95 percent confidence and prediction intervals for a given regression analysis what is the relation between confidence and prediction interval?
Confidence interval considers the entire data series to fix the band width with mean and standard deviation considers the present data where as prediction interval is for independent value and for future values.
What is the forecasting method that takes a fraction of forecast error into account for the next period forecast?
Exponential Smoothing Model
What tables represent an exponential function. Find the average rate of change for the interval from x 7 to x 8.?
what exponential function is the average rate of change for the interval from x = 7 to x = 8.
What has the author G Kallianpur written?
G. Kallianpur has written: 'White noise theory of prediction, filtering, and smoothing' -- subject(s): Gaussian processes, Kalman filtering, Prediction theory
How do linear and exponential functions change over equal intervals?
The linear function changes by an amount which is directly proportional to the size of the interval. The exponential changes by an amount which is proportional to the area underneath the curve. In the latter case, the change is approximately equal to the size of the interval multiplied by the average value of the function over the interval.
What has the author Joseph V Reilly written?
Joseph V. Reilly has written: 'A dynamic inventory model using exponential smoothing'
How do you Calculate Exponential Moving average?
Exponential moving average is a running average of a set of observations, where the weight of each observation is inversely exponentially weighted as a function of how old it is. It is a relatively simple thing to do. Given a set of observations O1, O2, O3, ... ON the running exponential moving average A1, A2, A3, ... AN can be calculated in real time, at each time N, with the expression ... AN = AN-1 (1 - X) + ON X ... where X is a weighting factor that determines that amount of smoothing. For instance, if X were zero, then the smoothing is infinite, and O does not contribute at all to A, and if X were one, then smoothing is zero, and A follows O with no smoothing at all. In a more useful example, if X were 0.2, then the smoothing would be five, and A would follow O with a time constant of five iterations, i.e. after five iterations we would be at about 63% of one step change and after 25 iterations we would be at about 95% of one step change.
How do you derive the exponential smoothing factor?
Lets define exponential smoothing first... Exponential smoothing, or exponential moving average, is a running average of a set of observations, where the weight of each observation is inversely exponentially weighted as a function of how old it is. It is a relatively simple thing to do. Given a set of observations O1, O2, O3, ... ON the running exponential moving average A1, A2, A3, ... AN can be calculated in real time, at each time N, with the expression ... AN = AN-1 (1 - X) + ON X ... where X is a weighting factor that determines that amount of smoothing. For instance, if X were zero, then the smoothing is infinite, and O does not contribute at all to A, and if X were one, then smoothing is zero, and A follows O with no smoothing at all. In a more useful example, if X were 0.2, then the smoothing would be five, and A would follow O with a time constant of five iterations, i.e. after five iterations we would be at about 63% of one step change and after 25 iterations we would be at about 95% of one step change. Some people swap the position of X and (1 - X) in the above equation. Its their choice, but the discussion that follows will have to change accordingly. X is the smoothing factor. It is simply the number of iterations that you want for your time constant. If you were to model this as an electronic circuit, for instance, with a capacitor and a resistor, the exponential curve would be in the form ... e-T/RC ... where RC was your time constant. The same thing applies here. If you evaluated the first equation once per second, with an X value of 0.2, you would have a time constant of 5 seconds. If you, on the other hand, evaluated it 100 times per second, with X being 0.002, you would still have a time constant of 5 seconds, but it would much more closely approximate the second equation, which is a continuous equation, rather than a discrete equation. In summary, then, the smoothing factor, or X, is one over the number of iterations that you want to be your time constant.
