-τ(ln (Vo-Vc/Vo)=t Mgk is that all
partial correlation is the relation between two variable after controlling for other variables and multiple correlation is correlation between dependent and group of independent variables.
If the figures in the table are exact and without measurement error then take any two of the points (x1, y1) and (x2, y2) and use these to form the linear relation y - y1 = ((y2 - y1)/(x2 - x1))(x - x1) If, however, you suspect that the values in the table do not exactly follow a linear relationship then use linear regression for which formulae are provided in wikipedia.
The domain of the inverse of a relation is the range of the relation. Similarly, the range of the inverse of a relation is the domain of the relation.
difference between relation sehema and relation instance in dbms
Slope-intercept form is one way of expressing a linear function (a fancy name for a straight line) as an equation.The slope-intercept form is modeled in the following way:y=mx+bwhere m represents the slope of the line and brepresents the y-intercept.Slope represents rate of change (how much y values change in relation to x) and on a graph determines how "sloped" a line is. Values of m close to 0 mean the line is more horizontal, or less sloped. As m approaches negative infinity or positive infinity, the line becomes more vertical, or more sloped.The y-intercept is simply the coordinate where the line crosses the y-axis on a Cartesian Plane. The point is (0, b).The slope-intercept method is popular because of its convenience. Using it, a graph of the line can be constructed very easily by hand without having to rearrange the equation. That said, in practical applications where computer software or graphing calculators are used, the form of a linear equation is less important. Slope-intercept form is often used by teachers and school textbooks.
in general regression model the dependent variable is continuous and independent variable is discrete type. in genral regression model the variables are linearly related. in logistic regression model the response varaible must be categorical type. the relation ship between the response and explonatory variables is non-linear.
The sample regression function is a statistical approximation to the population regression function.
In linear correlation analysis, we identify the strength and direction of a linear relation between two random variables. Correlation does not imply causation. Regression analysis takes the analysis one step further, to fit an equation to the data. One or more variables are considered independent variables (x1, x2, ... xn). responsible for the dependent or "response" variable or y variable.
Yes.
Two variables, X and Y, are in inverse relation if X*Y = a constant.
Because mathematical formulas can only describe dynamic changes if some elements (variables) vary in relation to other variables.
You may get more ideas from wikipedia under regression analysis. You can do a regression analysis with as little as 2 x,y points- but is it meaningful? Requirements for valid or meaningful relationships can be subjective. However, in my opinion, if meaningful relationships are to be created using regression analysis, the following are important: a) The independent variable should have values that are independent (no relation exists between them). b) There should be a good rational or experimental basis for identifying the independent variables and the resultant dependent variable. c) Sufficient data should be collected in a controlled environment to identify the relationship. d) The validity of the relationship should easy to identify both visually and by numbers (see "goodness of fit" tests).
Certain variables in thermodynamics are hard to measure experimentally such as entropy. Maxwell relations provide a way to exchange variables
Independent variables are those that you change in an experiment. Dependent variables are the ones that you measure in an experiment. Dependent variables are influenced by the independent variables that you change, so they are dependent upon the independent variable. Generally, experiments should have only one independent variable.
You can use correlation analysis to quantify the strength and direction of the relationship between two variables. This can help determine if there is a linear relationship, and whether changes in one variable can predict changes in the other. Additionally, regression analysis can be used to model and predict the value of one variable based on the value of another variable.
time or length could be two different variables depending in what the investigation was based on
The easiest way is to draw a scatter plot.We are assuming here that the variables are supposed to have a relation, for ex height and weight of people.