TP2: Statistical analysis using R
... a) To obtain a « numerical synthesis » of a dataframe, R has the command summary() : > summary(a) Question: do you see some specific features in these data? ...
... a) To obtain a « numerical synthesis » of a dataframe, R has the command summary() : > summary(a) Question: do you see some specific features in these data? ...
Name
... Notice that the y intercept is __________ and the slope is __________ so we have a straight line! After this ONE time we will just remember that when we are using an exponential model the result is you take the log of just the _____ value, but not the _____ value. *For a power model you take the log ...
... Notice that the y intercept is __________ and the slope is __________ so we have a straight line! After this ONE time we will just remember that when we are using an exponential model the result is you take the log of just the _____ value, but not the _____ value. *For a power model you take the log ...
Chap 14_ slides 1-28
... If the change in the mean y value associated with a 1-unit increase in one independent variable depends on the value of a second independent variable, there is interaction between these two variables. When the variables are denoted by x1 and x2, such interaction can be modeled by including x1x2, the ...
... If the change in the mean y value associated with a 1-unit increase in one independent variable depends on the value of a second independent variable, there is interaction between these two variables. When the variables are denoted by x1 and x2, such interaction can be modeled by including x1x2, the ...
PowerPoint 簡報
... GROWTH is the exponential counterpart to the linear regression function TREND described in Method of Least Squares. For R1 = the array containing the y values of the observed data and R2 = the array containing the x values of the observed data, GROWTH(R1, R2, x) = EXP(a) * EXP(b)^x where EXP(a) and ...
... GROWTH is the exponential counterpart to the linear regression function TREND described in Method of Least Squares. For R1 = the array containing the y values of the observed data and R2 = the array containing the x values of the observed data, GROWTH(R1, R2, x) = EXP(a) * EXP(b)^x where EXP(a) and ...
Indicative Solutions Institute of Actuaries of India October 2009 Examination CT3: Probability and Mathematical Statistics
... IAI CT3 1009 From the scatter plot we see that the line of regression may be a best fit. Only a point seems to be a ...
... IAI CT3 1009 From the scatter plot we see that the line of regression may be a best fit. Only a point seems to be a ...
BCA III POINTS (14173)
... 5) Value of correlation coefficient is between -1 or +1. 6) The value of r2 lie between 0 & 1 7) Two regression line intersect each other mean x and mean y 8) There are two types of classification qualitative and quantitative 9) Mean is an ideal average 10) the relative measure of dispersion based o ...
... 5) Value of correlation coefficient is between -1 or +1. 6) The value of r2 lie between 0 & 1 7) Two regression line intersect each other mean x and mean y 8) There are two types of classification qualitative and quantitative 9) Mean is an ideal average 10) the relative measure of dispersion based o ...
Coefficient of determination
In statistics, the coefficient of determination, denoted R2 or r2 and pronounced R squared, is a number that indicates how well data fit a statistical model – sometimes simply a line or a curve. An R2 of 1 indicates that the regression line perfectly fits the data, while an R2 of 0 indicates that the line does not fit the data at all. This latter can be because the data is utterly non-linear, or because it is random.It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model (pp. 187, 287).There are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. In this case, if an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the outcomes and their predicted values. If additional explanators are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination ranges from 0 to 1.Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions that are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept. Additionally, negative values of R2 may occur when fitting non-linear functions to data. In cases where negative values arise, the mean of the data provides a better fit to the outcomes than do the fitted function values, according to this particular criterion.