Experimental Design and Analysis Assignment Homework Help

Experimental Design a research design that eliminates all factors that influence outcome except for the cause being studied. All other factors are controlled by randomization, investigator-controlled manipulation of the independent variable, and control of the study situation by the investigator, including the use of control groups. We at StatisticsOnlineAssignmentHelp have a team of highly qualified and well experienced Experts/Tutors who have helped a number of students in Experimental Design assignments, homework’s and projects. We cater 24x7 hour customer service round the clock with 100% assistance and satisfaction. We provide the homework and assignments solution with no plagiarism and with reference styles Harvard, APA, AMA, MLA and IEEE. Statisticsonlineassignmenthelp assures to provide you with well-structured and well-formatted solutions and our deliveries have always been on time whether it’s a day’s deadline or long.


The team has helped a number of students in Experimental Design pursuing education through regular and online universities, institutes or online Tutoring in the following topics:

  • Analysis of experiments via Statisticsonlineassignmenthelp/R/SAS
  • Applications in Aeronautical, electrical, mechanical, chemical, industrial & civil      Balance, replication, randomization,   blocking and its interaction
  • Basic ideas of experimental design
  • BIBD: definition, applications, analysis and efficiency, construction
  • canonical analysis and ridge analysis of fitted surface
  • Checking Assumptions: Diagnostics and Remedial Measures
  • Complete Block Designs
  • Completely Randomized Designs
  • Concept of analysis of variance (ANOVA) and multiple comparisons
  • Confounding and partial confounding in 2n designs
  • Confounding/Blocking Designs
  • connectedness concepts and classifications orthogonality with examples
  • Construction of MOLs based on Galois fields
  • Cross-over Designs, Factorial Designs
  • Design of experimental controls
  • Designs eliminating heterogeneity in one direction: Block designs and its tests for treatment contrasts, comparison tests pairwise
  • Designs Row-column and their applications
  • discussion of basic designs from the point of view of blocking
  • D-optimal design measure.
  • Experimental Design: experimentation, control, randomization, replication.
  • Experimental Principles, Basic Statistics, Data Summary
  • experiments with factors at 3 levels
  • Fixed versus random effects
  • Fractional Factorial Designs
  • Kronecker calculus for factorials
  • Latin Square and Graeco-Latin Square Designs
  • Latin Squares and Graeco-Latin Squares
  • Main effect plans for 2-level factorials
  • Methods and logic in the analysis of gene function
  • Multiple linear regression and quadratic regression with one explanatory variable
  • New product design & development
  • Notion of mixed effects models
  • One-way layout, two-way layout, and latin square as special cases
  • Optimal regression designs
  • Optimality criteria, A-, D-, E-optimality
  • Orthogonal arrays, construction, Hadamard matrices. Rao's bound.
  • Orthogonal designs eliminating heterogeneity in two or more directions: use of Latin square designs
  • Orthogonality and Orthogonal contrasts
  • PBIB designs with emphasis on group divisible designs
  • Plot Designs, Comparing Regression Lines
  • Principles and concepts of experimental design
  • Principles and procedures of experimental designs
  • Principles, blocks and plots, uniformity trials, use of completely randomized designs
  • Proc NPAR1WAY, Proc Mixed, GLIMMIX,
  • Process development and manufacturing process improvement
  • Randomized Complete Block (RCB) Design, Latin Square (LS), Factorial Experiments
  • Reduced development lead time, enhanced process performance, and improved product quality
  • Relative efficiency of designs based on average variance
  • Repeated Measures Designs
  • Research Design Principles, Completely Randomized Designs
  • Response surface methodology and optimal designs
  • Review of experimental designs in a regression setting
  • Review of non-orthogonal block designs under fixed effects models
  • Robust designs and Taguchi methods
  • Sampling in Statistical Inference: sampling distributions, bias, variability.
  • Sampling: simple, stratified, and multistage random sampling.
  • Split-plot and repeated-measures designs
  • Split-plot designs, analysis and their use
  • Treatment Comparisons–Contrasts and Multiple Comparisons
  • Treatment structuredevelopment of analysis based on linear models
  • Two-level fractional factorial designs under statistics
  • Two-sample inference and basic statistical concepts
  • Universal optimality of BBD and generalized Youden Square Designs
  • Use of concomitant variables in related analysis and orthogonal designs
  • Useful designs using confounding in 33, 32 experiments