Scatter plots - non-linear relationship, Advanced Statistics

The scatter plots of SRES1, RESI1 versus totexp demonstrates that there is non-linear relationship that exists as most of the points are below and above zero. The scatter plots show that there many vertical structural breaks clearly visible. There are many outliers that can be seen and are obvious when analyzing these scatter plots. There is no leverage or influential points that are determining the slope of the line of best fit. There is no heteroscedasticity in these scatter graphs which means there is constant variance.

786_Scatter Plots.png

Posted Date: 3/4/2013 5:31:52 AM | Location : United States







Related Discussions:- Scatter plots - non-linear relationship, Assignment Help, Ask Question on Scatter plots - non-linear relationship, Get Answer, Expert's Help, Scatter plots - non-linear relationship Discussions

Write discussion on Scatter plots - non-linear relationship
Your posts are moderated
Related Questions
ain why the simulated result doesn''t have to be exact as the theoretical calculation

An approach to investigations designed to recognize a particular medical condition in the large population, usually by means of a blood test, which might result in the considerable

Bioassay : It is an abbreviation of biological assay, which in its classical form includes an experiment conducted on biological material to determine relative potency of test and

The type of longitudinal study in which the subjects receive different treatments on the various occasions. Random allocation is required to determine the order in which the treatm

Product-limit estimator is a method for estimating the survival functions for the set of survival times, some of which might be censored observations. The logic behind the procedu

The tabulation of a sample of observations in terms of numbers falling below particular values. The empirical equivalent of the growing probability distribution. An example of such

Designs which permits two or more questions to be addressed in the investigation. The easiest factorial design is one in which each of the two treatments or interventions are p

The Null Hypothesis - H0:  There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1:  There is heteroscedasticity i.e. β 1 0 Reject H0 if Q = ESS/2  >

The probability distribution which is a linear function of the number of component probability distributions. This type of distributions is used to model the populations thought to

Bimodal distribution : The probability distribution, or we can simply say the frequency distribution, with two modes. Figure 15 shows the example of each of them