Linear Modeling Survival Analysis Assignment and Homework Help

Linear Modeling Survival Analysis college work can tend to be difficult if students don’t master the core concepts. This course is subdivided into two key parts which are survival analysis and generalized linear models. Linear Modeling Survival Analysis is a course that aims to ensure students are familiar with practical application and methodology of some standard techniques which are used in data analysis and modeling. Therefore, it is important for scholars to attain high grades in order for them to excel in this course. At we have established a team of linear Modeling Survival Analysis tutors who are mandated to see students excel. Our services revolve around linear Modeling Survival Analysis homework help and linear Modeling Survival Analysis coursework help.

Linear Modeling Survival Analysis homework may be difficult to students who do not have good grasp on mathematical methods to the level (MA100) and probability to the level of probability distribution theory of inference (ST202) and knowledge of linear regression. Linear Modeling Survival Analysis homework assistance experts will take it upon themselves to offer online Linear Modeling Survival Analysis assignment help covering all the areas mentioned above.

Online Linear Modeling SurviHelp

It is very critical for linear Modeling Survival Analysis students to master the course content; this will give them an edge when dealing with linear Modeling Survival Analysis assignment problems. The course will dwell on theories and application of the generalized linear models for the analysis of categorical, continuous, and survival data. The key areas covered by online linear homework tutors include linear regression, logistic regression for binary data, analysis of variance (ANOVA) models for unordered and ordered (nominal) responses, contingency tables and log-linear models for count data. Most importantly, linear Modeling Survival Analysis homework help will also cover the usage of Stata software which is used by both linear Modeling Survival Analysis students and tutors. Highlighted below is a comprehensive list of topics to be covered by students undertaking this course:

  • Introduction
    • Background review
    • Linear models in matrix notation
    • Model assessment
  • The exponential family distributions
    • Definitions and examples
    • Mean and variance
    • Variation function and scale parameter
  • Generalized linear models (GLM)
    • Linear predictor
    • Link function
    • Canonical link
    • Maximum likelihood estimation
    • Iterative reweighted least squares
    • Fisher scoring algorithms
    • Significance of parameter estimates
    • Deviance
    • Pearson and deviance residuals
    • Pearson chi-square test and likelihood of ratio test  fitting by the use of R
  • Normal linear regression models
    • Least squares
    • Variance analysis
    • Orthogonality of parameters
    • Factor interactions between factor
  • Binary and binomial data analysis
    • Distribution and models
    • Logistic regression models
    • Odds ratio
    • One and two-way logistic regression analysis
  • Poisson count data analysis
    • Poison regression models with offset
    • Two-dimensional contingency tables
    • Log-linear models
  • Survival data
    • The survivor
    • Hazard cumulative hazard functions
    • Kaplan-Meier estimate of survivor function
  • Fitting exponential and Weibull distribution to survival data
    • Hazard plots
    • Cumulative hazard plots
  • Proportional hazards cox and(ph) regression
    • Assumption and interpretation
    • Model fitting and diagnostics
    • Hazard ratios and confidence intervals