when does it make sense to choose a linear function to model a set of data
A system of linear equations determines a line on the xy-plane. The solution to a linear set must satisfy all equations. The solution set is the intersection of x and y, and is either a line, a single point, or the empty set.
You describe the shape, not of the data set, but of its density function.You describe the shape, not of the data set, but of its density function.You describe the shape, not of the data set, but of its density function.You describe the shape, not of the data set, but of its density function.
The answer will depend on the set of data!
It is simply a data set.
The minimum data value in a data set is simply the lowest value of the set (easily found by arranging the set from lowest-highest values in an excel sheet or by hand).
If a linear model accurately reflects the measured data, then the linear model makes it easy to predict what outcomes will occur given any input within the range for which the model is valid. I chose the word valid, because many physical occurences may only be linear within a certain range. Consider applying force to stretch a spring. Within a certain distance, the spring will move a linear distance proportional to the force applied. Outside that range, the relationship is no longer linear, so we restrict our model to the range where it does work.
A correlation coefficient close to 0 makes a linear regression model unreasonable. Because If the correlation between the two variable is close to zero, we can not expect one variable explaining the variation in other variable.
It is one of the key measures of a data set: it shows the value around which the observations are spread out.
yes
hello i discovered answer: Assessing the scope of a model, that is, determining what situations the model is applicable to, can be less straightforward. If the model was constructed based on a set of data, one must determine for which systems or situations the known data is a "typical" set of data.
Depends on your definition of "linear" For someone taking basic math - algebra, trigonometry, etc - yes. Linear means "on the same line." For a statistician/econometrician? No. "Linear" has nothing to do with lines. A "linear" model means that the terms of the model are additive. The "general linear model" has a probability density as a solution set, not a line...
Knowing the formula is helpful. Also, having a data-set to analyze makes the job much easier.
If, by "mode", you mean that value of a set of data which features the most often, the answer is YES.
When a function or given data set differes from a liniar curve fit. the difference between the data and a linear curve fit is your linearity error
What you are asking is not precisely clear, but in general missing data is filled in by a process of interpolation. eg. Linear interpolation is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points.
In computer science, data modeling is the process of creating a data model by applying a data model theory to create a data model instance. A data model theory is a formal data model description.For the source and more detailed information concerning your request, click on the related links section (Answers.com) indicated below.
It suggests that there is very little evidence of a linear relationship between the variables.