What does S mean in a computer output?
|
There are four basic types of output: audio output, graphics output, text output, and video output. Below are examples of each type of these outputs. Show Audio output
Graphics output
Text output
Video output
Tip See our output device page for a list of output devices used with a computer. In addition to computers, output can be produced from any electronic device. For example, a water heater may receive input from a temperature sensor. The output would be a signal that turns on a pilot light or gas burner to heat the water to the desired temperature. Related information
2. When referring to HTML, the Control unit, Hard copy, Hardware terms, Input, Input/Output device, IPOS, Output device, Output screen, Output stream, Tactile output The closer RSquare is to 1, the more variation that is explained by the model. In our example, 84.8584% of the variation in our response, Removal, is explained by the variable OD. Note that the value of RSquare can be influenced by a number of factors, so here are a few cautions:
So, although RSquare is a useful measure, and in general a higher RSquare value is better, there is no cutoff value to use for RSquare that indicates we have a good model. RSquare, and the similar measure RSquare Adjusted, are best used to compare different models on the same data. We describe RSquare Adjusted in the Multiple Linear Regression lesson. R-squared gets all of the attention when it comes to determining how well a linear model fits the data. However, I've stated previously that R-squared is overrated. Is there a different goodness-of-fit statistic that can be more helpful? You bet! Today, I’ll highlight a sorely underappreciated regression statistic: S, or the standard error of the regression. S provides important information that R-squared does not. What is the Standard Error of the Regression (S)? In the regression output for Minitab statistical software, you can find S in the Summary of Model section, right next to R-squared. Both statistics provide an overall measure of how well the model fits the data. S is known both as the standard error of the regression and as the standard error of the estimate. S represents the average distance that the observed values fall from the regression line. Conveniently, it tells you how wrong the regression model is on average using the units of the response variable. Smaller values are better because it indicates that the observations are closer to the fitted line. The fitted line plot shown above is from my post where I use BMI to predict body fat percentage. S is 3.53399, which tells us that the average distance of the data points from the fitted line is about 3.5% body fat. Unlike R-squared, you can use the standard error of the regression to assess the precision of the predictions. Approximately 95% of the observations should fall within plus/minus 2*standard error of the regression from the regression line, which is also a quick approximation of a 95% prediction interval. For the BMI example, about 95% of the observations should fall within plus/minus 7% of the fitted line, which is a close match for the prediction interval. Why I Like the Standard Error of the Regression (S)In many cases, I prefer the standard error of the regression over R-squared. I love the practical, intuitiveness of using the natural units of the response variable. And, if I need precise predictions, I can quickly check S to assess the precision. Conversely, the unit-less R-squared doesn’t provide an intuitive feel for how close the predicted values are to the observed values. Further, as I detailed here, R-squared is relevant mainly when you need precise predictions. However, you can’t use R-squared to assess the precision, which ultimately leaves it unhelpful. To illustrate this, let’s go back to the BMI example. The regression model produces an R-squared of 76.1% and S is 3.53399% body fat. Suppose our requirement is that the predictions must be within +/- 5% of the actual value. Is the R-squared high enough to achieve this level of precision? There’s no way of knowing. However, S must be <= 2.5 to produce a sufficiently narrow 95% prediction interval. At a glance, we can see that our model needs to be more precise. Thanks S! What is S in a computer output for statistics?S is known both as the standard error of the regression and as the standard error of the estimate. S represents the average distance that the observed values fall from the regression line. Conveniently, it tells you how wrong the regression model is on average using the units of the response variable.
How to interpret S and R 2?The standard error of the regression provides the absolute measure of the typical distance that the data points fall from the regression line. S is in the units of the dependent variable. R-squared provides the relative measure of the percentage of the dependent variable variance that the model explains.
What are the units of SER?A ser is an obsolete unit of dry volume in India. In 1871 it was defined as being exactly 1 litre. After metrication in the mid-20th century, the unit became obsolete. It was the unit in pre-modern India which was so close to the metric values of volume approx equal to a litre.
What is sy in statistics?sy is the sample standard deviation for y values. r is the regression coefficient. The line of regression is: ŷ = b0 + b1x.
|
