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Statistics Assignment Help | Statistics homework help


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Statistics

It is a branch of mathematics which deals with collection of data, organization, analyzing, interpretation and presentation of data. It refers to numbers that are used to describe relationships. Statisticians use these method to get a correct result such as:

1) Producing reliable data
2) Analyzing the data appropriately
3) Design reasonable result In weather forecasting, there are some computers models which build on statistical concepts.

These computer models compares prior weather with the current weather and predict future weather. Statistics is a crucial process. It helps in making decision on the given data, it help in prediction. It is used by the researcher to calculate results to save their money, time and data. It is the key of how traders, businessmen invest and make money. It play a big role in the medical field. It is used in quality testing, retail marketing, trading, on consumer properties and much more.

Statistics homework help

Get the instant help for Statistics homework . Schedule an expert for the sessions & any other assistance, Our Statistics homework help tutors offer 24*7 homework assistance with Statistics coursework

The mathematics that is used in collecting ,organizing,presenting,summarizing and anlyzing the data is called statistics.it starts with the collection of data and its interpretation in numerical terms.the following four steps are involved:

  • data collection
  • data presentation
  • data analysis
  • conclusion and result

The statistics has two main branches that are inferential and descriptive.the branch of statistics that deals with data collection and its presentation in form of charts diagrams, graphs and tables is called descriptive statistics. the techniques which are used in analysis of the data and drawing the conclusions through testing and sampling is called inferential statistics.

Statistics performs various important functions such as data collection ,data estimation and testing.

Data collection and data analysis .

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Few topics Covered by Online Statistics experts :
  • Programming using the R language for the Statistical Computing. Data objects, for loops, if statements, using packages. General Statistics:LGraphs,descriptive statistics,confidence intervals, hypothesis tests
  • tests of association. Statistics for Business :Surveys, sampling, descriptive statistics, confidence intervals, contingency tables, control charts,regression, exponential smoothing, forecasting. Statistical Methods:Techniques in statistical inference; confidence intervals, hypothesis tests, analysis of variance, chi-square tests.
  • Sampling Techniques:Sample designs: simple random, stratified, systematic, cluster, unequal probability, two-phase; methods of estimation and sample size determination. Biostatistics.:Biostatistical methods
  • confidence intervals, hypothesis tests, simple correlation and regression, one-way analysis of variance. quantitative reasoning, statistical methods in SPSS including ANOVA, regression, logistic regression One-way analysis of variance,factorial designs, blocked designs, multiple comparisons of means, multiple regression.
  • Statistics for Engineers and Scientists:Calculus-based probability and statistics: distribution theory, estimation, hypothesis testing, applications to engineering and sciences. Elementary Probabilistic-Stochastic Modeling
  • Probabilistic and stochastic models of real phenomena; distributions, expectations, correlations, averages  simple Markov chains and random walks, Multiple Regression Analysis Assignment Help Polynomial
  • multiple regression models; analysis of residuals  selection of variables; nonlinear regression.Statistical Data Analysis:Estimation and inference based upon Gaussian linear regression models; residual analysis variable selection; non-linear regression.
  • Single-factor analysis of variance models; multi-factor analysis of variance models; randomized block design; Latin squares; split-plot design. Design of Experiments:Analysis of variance, covariance; randomization; completely randomized
  •  randomized block, latin-square,split-plot, factorial and other designs. Data Analysis Tools:Data analysis principles and practice, statistical packages and computing; ANOVA, regression and categorical data methods.
    Statistical Computing:Computationally intensive statistical methods: optimization for statistical problems; simulation & Monte Carlo methods; resampling methods; smoothing Probability and Mathematical Statistics
  • Probability, random variables, distribution functions, and expectations; joint and conditional distributions and expectations  transformations Theories and applications of estimation, testing, and confidence intervals, sampling distributions normal, gamma, beta X-squared, t, and F.
  • Bayesian Data Analysis:Applied Bayesian data analysis, Bayesian inference and interpretation of results, computing methods including MCMC,model selection and evaluation. Applied Multivariate Analysis
  • Principles for multivariate estimation and testing; multivariate analysis of variance, discriminant analysis; principal components, factor analysis. Probability Theory:Probability, random variables, distributions
  •  expectations, generating functions, limit theorems, convergence, random processes. Stochastic Processes: Characterization of stochastic processes. Markov chains in discrete and continuous time, branching processes, renewal theory, Brownian motion
    Martingales and applications, random walks, fluctuation theory, diffusion processes, point processes, queueing theory, Analysis of Time Series:Trend and seasonality  stationary processes, Hilbert space techniques
  •  spectral distribution function, fitting ARIMA models, linear prediction.Spectral analysis; the periodogram  spectral estimation techniques; multivariate time series; linear systems, optimal control; Kalman filtering Homework Help

Complex Topics for Statistics
  • Mathematical Statistics:Sampling distributions, estimates, testing, confidence intervals, exact asymptotic theories of maximum likelihood and distribution-free methods. Applied Multivariate Analysis:Multivariate analysis of variance
  •  principal components; factor analysis; discriminant analysis; cluster analysis. Nonparametric Statistics:Distribution and uses of order statistics; nonparametric inferential techniques Optimization and integration in statistics
  • Monte Carlo methods; simulation; bootstrapping Theory of Sampling Techniques Theory of estimation;optimization techniques for minimum variance or costs. Design and Linear Modeling:linear models; experimental design
  • fixed, random, and mixed models.Mixed factorials; response surface methodology Taguchi methods; variance components, Categorical Data Analysis and GLIM binary and polytomous data, log linear models quasilikelihood, survival data models.Probability Theory Measure theoretic probability
few topics covered by Statistics
  •  central limit, extreme value, asymptotic theory.Applied Probability and Stochastic Processes:General theory of processes; Markov processes in discrete  continuous time; review of martingales, random walks; renewal and regenerative processes
  • Brownian motion, diffusion, stochastic differential equations  weak convergence, central limit theorems. Applications in engineering, natural sciences.Time Series and Stationary Processes:Spectral theory of multivariate stationary processes  estimation, testing for spectral, linear, AR-MA representations; best linear predictors, filters.Advanced Theory of Statistics:Minimal sufficiency, maximal invariance
  • Neyman-Pearson theory; Fisher, Kullback-Leibler information; asymptotic properties of maximum-likelihood methods.Decision-theory model; Bayes, E-Bayes, complete  and admissible classes; applications to sequential analysis and design of experiments
  • Advanced Statistical Methods:Generalized additive models  recursive partitioning regression and classification; graphical models and belief networks; spatial statistics.Advanced Theory of Design:Information theory  factorial designs and optimal designs, orthogonal and balanced arrays,designs with discrete/continuous factors.
  • Approximation Theory and Methods:Edgeworth expansions, saddlepoint methods; applications of weak convergence ,Correlation and simple regression,probability theory,multiplicative probability,probability of unions,law of total probability
  • Bayes' rule,gambler's fallacy. Tree techniques,random variables,discrete random variables,continuous random variables,method of moments,hypothesis testing. Varieties of research design,experimental comparisons,correlation and causation,descriptive statistics,reliability and validity graphical representation of data,sampling,coefficients of correlation,chi-squared test.
  • histograms,average, the standard deviation, the normal curve, correlation ,statistical reasoning
  •  numerical data
  • probability theory
  •  Discrete probability distributions
  • hypergeometric, binomial, geometric and Poisson distribution
  •  Continuous probability distributions
  •  uniform on an interval
  •  Normal distribution
  •  t-distribution
  •  Exponential distribution
  •  x² distribution.
  •  Confidence interval and hypothesis testing
  • variance
  • F-distribution
  •  Correlation
  •  Simple linear regression

 

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