Statistics Informed Decisions Using Data

This book covers all the titles related to statistics and their usage in real life for the senior level. The topics that are covered within this book are data collection, organizing and summarizing data, probability and probability distribution, estimating the value of a parameter and its hypothesis testing, inference on two samples and categorical data, and correlation regression. The first chapter deals with data collection, which includes an introduction to the practice of statistics, observational studies versus designed experiments, simple random sampling, other effective sampling methods, the bias in sampling, the design of experiments.The second chapter focuses on organizing and summarizing data. The third chapter deals with probability and probability distributions with includes probability rules, the addition rule and complements, independence and the multiplication rule, conditional probability and the general multiplication rule, counting techniques, Bayes' rule, discrete random variables, binomial, geometric and Poisson probability distribution, their properties, the normal approximation to the binomial probability distribution, etc.The fourth chapter deals with estimating the value of the parameter and its hypothesis testing which includes estimating a population proportion, mean, standard deviation, the language of hypothesis testing, hypothesis test for a population proportion, mean, population standard deviation, probability of a type II error and the power of the test.The fifth chapter deals with inference on two samples and categorical data which includes inference about two population proportion, two means: dependent and independent samples, two population standard deviations, the goodness of fit test, tests for independence and the homogeneity of proportions, inference about two population proportions: dependent samples.The sixth chapter deals with correlation regression which includes scattering diagrams and correlation, least square regression, diagnostics on the least square regression line, non-linear regression, testing ad significance of the least-squares regression model, confidence and prediction intervals, introduction to multiple regression, interaction and dummy variables, polynomial regression, building a regression model.