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Statistical Methods for Engineers Assignment Help

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Online Statistical Methods for Engineers Assignment help tutors help with topics like Distributions, Interval estimation, Tests of hypotheses, Linear regression, Model selection, Elementary experimental design, Analysis of variance, Statistical methods for environmental sciences and engineering, Statistics: means, medians, modes and all that, Getting started with matlab, Probability density functions.

Some of the homework help topics include:

  • Descriptive statistics
  • Basic probability
  • Random variables

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Help for complex topics like:

  • Least-squares fits: defining the problem, Matlab and linear algebra, Least-squares fits: finding solutions, Least-squares fits: estimating uncertainties, Weighted least-squares fits, Fitting complex quantities, Decorrelation and degrees of freedom.
  • Sinusoidal variability, Spectral analysis., Fourier transforms., Fast fourier transform, Spectra, Uncertainties and spectra., Coherence, Rotary spectra, Empirical orthogonal functions, Eofs differ from spectra, Uncertainty and eofs, Design & analysis of factorial experiments for 2 factors - model i
  • model ii, and model iii applications;, Fully crossed and nested analyses - common transformations, Design and analysis of factorial experiments with 3 or more factors - model i, model ii, and model iii applications
  • Fully crossed and nested analyses - aet determination - quasi- and pseudo-f ratios, Simple regression and correlation, Measures of relationship, Methods for correlation and association
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Computing means, variances from pdfs.Generally topics like Estimating errors, Error propagation, Errors on the mean ,Significance of differences, Correlation, Non-linear regression analysis, Multiple regression analysis.

  • Descriptive Statistics, General Introduction, Statistics studies data, population, and samples, Descriptive Statistics vs Inferential Statistics, Descriptive Statistics, Pictorial and tabular methods, Stemplot, dotplot, histogram, boxplot, Numerical measures, Measures of Location: Mean and Median, Measures of Variability: Range, Variance, and IQR, Inferential Statistics, Draw conclusions about a certain population parameter
  • Confidence Intervals, Hypothesis Testing, Probability, Random Experiments, Sample Space and Events., Random Experiment (R.E.), Sample Space, Events, Examples, Introduction to Probability, Axiom, Properties, Examples, Counting Techniques, Combinations, Permutations, Conditional Probability, Definition, Related rules and theorem, Independence, Discrete Random Variable and Probability Distribution, Discrete Random Variables, Chapter Overview, Random Variable (r.v.), Definition, Discrete and continuous r.v., Probability distribution for discrete r.v.
  • Mass function, Cumulative distribution function (CDF), Some discrete probability distribution, Binomial, Geometric and Hypergeometric, Poisson, Uniform, Expectation and variance, Expectation, Variance, Poisson Process, Continuous Random Variables and Probability Distributions, more, Continuous Random Variables, Chapter Overview, Continuous random variables, Probability density function, Definition and interpretation, Cumulative distribution function, Definition and interpretation, Expectation, variance and percentile for continuous rv, Some continuous distributions, Uniform and exponential, continuous distributions − Gamma, Chi-squared, Weibull, Lognormal, Beta etc.
  • Joint Probability Distributions and Random Samples, Jointly distributed rv, Joint mass function, marginal mass function for discrete rv, Joint density function, marginal density function for continuous rv, Independent random variables, Expectation, covariance and correlation between two rvs, Expectation, Covariance, Correlation, Interpretations, Statistics and their distributions.
  • Distribution of the sample mean, Distribution of a linear combination, Point Estimation, Point estimate and point estimator, Define point estimate and estimator, Unbiased estimators, Minimum variance estimators, Finding the standard error of an estimator, Deriving directly, Bootstrapping, Methods of point estimation, The method of moments, The method of maximum likelihood.
  • Estimating functions of parameters, Confidence Intervals, Basics Confidence Intervals (C.I.), Large-Sample Confidence Intervals for Population Mean and Proportion, C.I. for mean µ, C.I. for proportion, One-sided intervals, Intervals Based on a Normal Population Distribution for mean, t distribution, One sample t C.I., Confidence Intervals for the Variance and Standard Deviation of a Normal Population, Hypothesis Testing, Hypothesis Testing: The Basics, Chapter Overview, Introduction to hypothesis testing, Null hypothesis and alternative hypothesis.
  • Decision procedures, Type I and type II errors, Tests about population mean, Normal with known σ, Normal with unknown σ, Large sample from unknown distribution, Test about population proportion, Sample size n is small, Sample size n is large, Comparing Two Means, One-Way ANOVA, slides, Simple Linear Regression, Plotting Histograms, Numerical Summaries, Relative Frequency, Binomial Distribution, Normal Distribution, Sampling Distributions, t-Distribution and Confidence Interval, Hypothesis Testing, Chi-Square Test
  • Descriptive statistics
  • Probability
  • Interval estimation
  • Tests of hypotheses
  • Nonparametric methods
  • Linear regression
  • Analysis of variance
  • Elementary experimental design

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