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Accelerated Failure Time Model
A basic model for the data comprising of survival times, in which the explanatory variables measured on an individual are supposed to act multiplicatively on the time-scale, and so affect the rate at which the individual moves along the time axis.
Accordingly the interruption in model takes place in terms of the speed of progression of the disease.
In the easiest cases of comparing the two groups of patients, for instance, those receiving the treatment A and those who are receiving the treatment B, this model supposes that the survival time of an individual on one treatment is a multiple of survival time on the other treatment; as a result of which the probability that an individual on treatment A survives beyond time t is the probability of that an individual on treatment B survives beyond time _t, where _ is an unidentified positive constant. When the end-point of the interest is the death of the patient, values of _ less than one correspond to the acceleration in the time of death of an individual given to treatment A, and values of _ greater than one shows the reverse of it. The parameter _ is generally known as the acceleration factor
Q. Find the inverse Laplace transform of Y (s) = s-4/s 2 + 4s + 13 +3s+5/s 2 - 2s -3. Q. Use the Laplace transform to solve the initial value problem y''+ y = cos(3t), y(0) =
Sampling Error It is the difference between the value of the actual population parameter and the sample statistic. Samples are used to arrive at conclusions regarding the p
Flow Chart for Confidence Interval We can now prepare a flow chart for estimating a confidence interval for μ, the population parameter. Figure
Quota sampling Under this method enumerators shall select the respondents in place of those not available, as per the quota fixed according to guide lines provided to them.
Explanation of standard deviation and variance Describe the importance of standard deviation and variance, what they calculate and why they are required. Importance of char
who invented the chi square test and why? what is central chi square and non central chi square test? what is distribution free statistics? what are the conditions when the chi squ
case study in heat power engineering
TYPE I AND II Errors If a statistical hypothesis is tested, we may get the following four possible cases: The null hypothesis is true and it is accepted; The
Normal Distribution Meaning: According to ya Lun Chou There perfectly smooth and symmetrical curve, resulting from the expansion of the binomial (p+q) n when n approac
First we look at these charts assuming that we know both the mean and the standard deviation of the process, that is μ and σ . These values represent the acceptable values (bench
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