The point lies 1 unit below the regression line.
Let's say that you fit a simple regression line y = mx + b to a set of (x,y) data points. In a typical research situation the regression line will not touch all of the points; it might not touch any of them. The vertical difference between the y-co-ordinate of one of the data points and the y value of the regression line for the x-co-ordinate of that data point is called a residual.There will be one residual for each data point.To see some labelled diagrams of residuals search images.google.com for residuals.
A line is made up of a large (infinite) number of points. When we say a point is on a line, we mean that it is one of the pints that form the line.
Why wood u say that because that .
All three are measures of distribution. they hep us understand the distribution of a series of data points. or otherwise said, if you had to guess what something was and you had a whole bunch of estimates, what is the best guess. If the data has a couple spikes (a modal distribution) say there were a few ones, a couple twos, a whole bunch of threes, a few fours, a whole bunch of fives, and a few sixes, than the graph would spike at three and five. To generate a best guess from a set of data that is "modal" you use the "mode". If the data is non-modal but leans toward one end or the other. Say a lot of ones, a lot of twos, good number of threes, some fours, some fives, we'd say this data is "skewed". The best guess for a skewed distribution of data is going to be the median which is the mathematical middle point in a rank order list of data points. If the data was "normally distributed" or had a few ones, few more twos, bunch of threes, few less fours, and only a few fives than we'd say the data was normally distributed, or a "bell curve". In the case of normally distributed data the mean is your best measure. all three are averages. all three describe a collection of data. Which of the three best describes the data depends on the data distribution.
What did the point say to the segment
The point lies 1 unit below the regression line.
The point lies one unit below the regression line.
If a data point has a residual of zero, it means that the observed value of the data point matches the value predicted by the regression model. In other words, there is no difference between the actual value and the predicted value for that data point.
Let's say that you fit a simple regression line y = mx + b to a set of (x,y) data points. In a typical research situation the regression line will not touch all of the points; it might not touch any of them. The vertical difference between the y-co-ordinate of one of the data points and the y value of the regression line for the x-co-ordinate of that data point is called a residual.There will be one residual for each data point.To see some labelled diagrams of residuals search images.google.com for residuals.
Let's say that you fit a simple regression line y = mx + b to a set of (x,y) data points. In a typical research situation the regression line will not touch all of the points; it might not touch any of them. The vertical difference between the y-co-ordinate of one of the data points and the y value of the regression line for the x-co-ordinate of that data point is called a residual.There will be one residual for each data point.To see some labelled diagrams of residuals search images.Google.com for residuals.
The language itself doesn't say anything about data (or other) segments.
To be concise in your speaking, to say exactly what you mean, plainly.
It means you cannot alter the data on it.
Depends what you mean with storage technology. Data storage, food storage? I assume you mean data storage. In that case I would say clay tablets are the oldest data storage technology (4th millennium BC). Or did you mean electronic data storage?
It means that there is nothing wrong with the cars computer
To say that there is no point in doing something means you are calling it useless or wasted effort.
Because Power Point is a proper noun referring to a product, you would actually just say "Power Point." Translating it would mean something else entirely. To say a "Power Point presentation," you would say "presentación en Power Point."