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Descriptive statistics is used to analyze and represent the data that have been previously collected. It includes frequency counts, ranges (high and low scores or values), means, modes, median scores, and standard deviations. Two important concepts to understand descriptive statistics are: Variables and Distribution. It is a statistic or a measure that describes the data such as the average salary of employees. It is a describing data with tables and graphs which is quantitative or categorical variables. In terms of numerical descriptions, it has two terms center and variability. The center gives some example measures of center of the data and variability gives some example measures of variability of the data. Now it is given the bivariate descriptions, the dependency measure is considered under correlation.
The main focus of this course is statistical analysis, which involves both descriptive statistics and inferential statistics. The major concern of descriptive statistics is to present information in a convenient, usable, and understandable form. Descriptive statistics is used to describe a set of data in terms of its frequency of occurrence, its central tendency, and its dispersion. Descriptive statistics collects, summarizes and describes data. The Inferential statistics deals with drawing conclusions and/or making decisions concerning a population based only on sample data.
The major courses in Descriptive analytics include descriptive statistics, data visualization, random variables, probability theories, probability distributions, parameter estimation, regression methods, Hypothesis Testing I, t-test, confidence interval, Hypothesis testing II, ANOVA, Hypothesis testing III, Bootstrapping, cross validation and permutation tests etc.
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Some of the homework help topics include :
- Statistics , methods, therminology, tables , plots, stages of statistical investigation, frequency tables, statistical data, characteristics of level, variability, skewness, kurtosis, probability calculations , elementary terminology of probability, distribution theory, repeated trials with replacement, repeated trials without replacement, random variables, probability distribution, distribution function, probability density function, probability mass function, property of random variables, distributions of continuous, discrete random variables, Fundamentals of inferential statistics , Population, random sample, Point estimation, construction , properties
- Confidence intervals, Statistical hypothesis tests, Statistical hypothesis steps, Statistical hypothesis errors, Parametric tests, nonparametric tests, Descriptive statistics, basic concepts, Classifications of variables, classification of data, Handling sums, characteristics of location, Types of averages, arithmetic, harmonic, geometric, Modus, median, Quantiles, percentiles, characteristics of variability, Range, Interquartile range, Variance, standard deviation, Coefficient of variation
- Usage of quartiles, deciles, Statistical functions of a calculator, first continuous test, probability calculus, Continuous test descriptive statistics, Classical of probability, statistical of probability, Basic rules of the probability calculus, Discrete random variable, Binomial distribution , Probability function, distribution function, Mean, variance, Continuous random variable, Density function, Normal distribution, Processing data from surveys, Point estimation, interval estimation, Types of surveys
Help for complex topics like :
- Sample characteristics, Point estimation of the population mean, interval estimation of the population mean, Point estimation of the population variance, Point estimation of the population proportion, interval estimation of the population proportion, second continuous test, Interval estimation, probability calculus, estimation, Confidence interval for the population total, Confidence interval for the total number of units, Sample size determination, Hypothesis testing, Formulation of hypotheses, Type I errors, type II errors, significance level, rejection region, One sample tests of the mean, One sample tests of the population proportion, Comparison of two population means, Comparison of two population proportions
- Frequency distributions, Charts, Distribution centre, Dispersion of a distribution, Linear Transformation, Frequency approach, Axiomatic approach , symmetric Probabilities, Conditional Probabilities, Probability Distributions, discrete case , Bernoulli, Binomial, Uniform, Poisson, Geometric , Hypergeometric distribution theories, Density Functions, Random Variable Functions, Mathematic Expectation , Random Variables Coupling , Joint, Marginal , Conditional distributions , Covariance, Correlation, Linear Combination of two random variables, Displaying data, measures of central tendency, Wheres Waldo Histograms, Measures of variability , Mean, medians , modes, Correlation, Regression, normal distribution
- Basic concepts of probability , normal distribution, Sampling distributions , hypothesis testing, Food preference survey, probability, Nerd Survey , Central Limit Theorem, Displaying data, Measures of central tendency, Measures of variability, Correlation, Regression, The normal distribution, probability, Sampling distributions, One sample & two related samples, Frequency distributions and Charts, Distribution centre, Dispersion of a distribution, Linear Transformation
- Measures of central tendency,Measures of variability,Correlation,Regression,normal distribution,probability,Sampling distributions ,hypothesis testing,One sample,Hypothesis tests to means,Measures and Measurement,Statistics,Presenting Basic Information,Summarising and Simplifying Data,Distributions,Measures of Dispersio,Correlation,Graphical Presentation, samples, variables ,type of sampling,probability and non-probability samples ,data or variables,variable of interest ,type of variable ,qualitative and quantitative data in statistics,Statistical methods for variation of data,statistics measures,Numerical measures of relative standing,Relationships between variables,Correlations regression,simple linear regression