Chapter One: Introduction to Statistical Analysis 1.1 Scale of measurement 1.2 Data, data collection and presentation 1.3 Data grouping 1.4 Methods of visualizing data 1.5 Introduction to R software Chapter Two: Descriptive Data Chapter One: Introduction to Statistical Analysis Scale of measurement Data, data collection and presentation Data grouping Methods of visualizing data Introduction to R software Chapter Two: Descriptive Data 2.1. Measure of Central tendency 2.2. Measure of Dispersion 2.3. Shapes of the distribution—symmetric and asymmetric 2.4. Summary statistics of data using R Chapter Three: Basic Probability Concepts 3.1. Experiment and sample space 3.2. Elementary events 3.3 Venn diagram and probability matrices for two sets probability problems. 3.4 Addition rule of probability 3.5 Independent events and dependent events. 3.6 Multiplication rule of probability 3.7 Conditional probabilities 　 Chapter Four: Discrete Probability Distributions 4.1. Expected value and variance of a discrete random variable 4.2. Binomial probability distribution 4.3. Expected value and variance of a binomial distribution 4.4. Solve problems involving binomial distribution using R Chapter Five: Continuous Probability Distribution 5.1. Normal distribution and standardized normal distribution 5.2. Normal curve 5.3. Approximate normal to the binomial distribution 5.4. Use of the normal distribution in business problem solving using R Chapter Six: Sampling and Sampling Distribution 6.1. Probability and non-probability sampling 6.2. Sampling techniques- simple random, systematic, stratified, and cluster samples 6.3. Sampling distribution of the mean 6.4. Central limit theorem and its significance Chapter Seven: Confidence Intervals for Single Population Mean and Proportion 7.1. Point estimates and interval estimates 7.2. Confidence intervals for mean and proportion 7.3. Confidence interval for proportion 7.4 Factors that determine margin of error 　 Chapter Eight: Hypothesis Testing for Single Population Mean and Proportion 8.1. Null and alternative hypotheses 8.2 Type I and Type II Error 8.3. Acceptance and Rejection regions 8.4. Hypothesis testing procedure Chapter Nine: Regression Analysis and Correlation 9.1. Construction of line fit plots 9.2. Types of regression analysis 9.2.1 Uses of regression analysis 9.2.2 Simple linear regression 9.2.3 Assumptions of simple linear regression 9.3. Multiple linear regression 9.3.1 Significance testing of each variable 9.3.2. Interpretation of regression coefficients and other output 9.4 Pearson correlation coefficient 9.4.1 Assumptions of correlation test 9.4.2 Types of correlation 9.4.3 Coefficient of determination 9.4.4 Test for the significance of correlation coefficient (r) Chapter Ten: Poisson Distribution 10.1. Poisson distribution and its properties 10.2. Mean and variance of a Poisson distribution 10.3. Application of Poisson distribution 10.4. Poisson to approximate the Binomial 　 Chapter Eleven: Uniform Distribution 11.1. Uniform distribution and its properties 11.2. Mean and variance of a uniform distribution 11.3. Application of uniform distribution Chapter Twelve: Statistical Process Control 12.1. Types of control chart 12.2 Uses of control chart 12.3 Procedure of control chart 12.4. Variable control charts 12.4.1. X-bar chart 12.4.1.1. Steps for constructing X-bar chart 12.4.2. Range chart 12.4.2.1. Steps for constructing R-chart 12.4.3. S-chart 12.4.4. NP chart 12.4.5. P chart 12.4.6. C chart 12.4.7. U chart 　 Chapter Thirteen: Time Series 13.1. Concept of Time series data 13.1.1 Uses and application of time series analysis 13.2 Univariate time series model 13.2.1 Generating a time-series object in R 13.2.2. Smoothing and seasonal decomposition 13.2.2.3. Exponential Forecasting Models 13.2.2.4. Holt and Holt-Winters exponential smoothing 13.2.2.5 The ets( ) function and automated forecasting 13.2.2.5. ARIMA forecasting models 13.3 Multivariate time series model 13.3.1. ARMA and ARIMA models 13.4 Recap 　 Chapter Fourteen: Multivariate Analysis 14.1. Properties of Multivariate Normal Distribution 14.2. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation 14.2.1. Multivariate Normal Distribution 14.2.2 Maximum Likelihood Estimation of Mean (μ) and Covariance matrix (Σ) 14.3 The Sampling Distribution X ̅ and 14.3.1 Wishart Distribution 14.3.2 Properties of the Wishart Distribution 14.3.3 Large Sample Properties of X ̅ and 14.4 Multivariate Normality 14.4.1 Q-Q Plot for Evaluating Multivariate Normality 14.4.1.1 Steps for Constructing Chi-squared plot 　 　 Chapter Fifteen: Inference About a Mean Vector 15.1 Test of Hypothesis [μ=μ_0] 15.2 Confidence Interval and Simultaneous Comparison of Component Means 15.2.1 Confidence Regions 15.2.2 Simultaneous Confidence Intervals 15.2.3 Bonferroni Method of Multiple Comparisons 15.2.3 Large Sample Inference about a Population Mean Vector 15.2.4 Multivariate Quality Control Charts 15.2.4.1 Univariate Case 15.2.4.1 Multivariate Case Chapter Sixteen: Inference About a Mean Vector 16.1 Paired Comparisons 16.2 Repeated Measurement Comparisons 16.3 Comparisons of Mean Vectors from Two Populations 16.4 Several Multivariate Population Means Comparison. 16.4.1 Univariate Analysis of Variance (ANOVA) 16.4.1.1 Assumptions of ANOVA 16.4.2 Multivariate Analysis of Variance (MANOVA) 16.4.2.1 Assumptions of MANOVA

Business Statistics with Solutions in R covers a wide range of applications of statistics in solving business related problems. It will introduce readers to quantitative tools that are necessary for daily business needs and help them to make evidence-based decisions. The book provides an insight on how to summarize data, analyze it, and draw meaningful inferences that can be used to improve decisions. It will enable readers to develop computational skills and problem-solving competence using the open source language, R. Mustapha Abiodun Akinkunmi uses real life business data for illustrative examples while discussing the basic statistical measures, probability, regression analysis, significance testing, correlation, the Poisson distribution, process control for manufacturing, time series analysis, forecasting techniques, exponential smoothing, univariate and multivariate analysis including ANOVA and MANOVA and more in this valuable reference for policy makers, professionals, academics and individuals interested in the areas of business statistics, applied statistics, statistical computing, finance, management and econometrics.

Mustapha Abiodun Akinkunmi, associate professor of finance and chair of the accounting and finance department at the American University of Nigeria, Yola, Nigeria, is a financial economist and technology strategist with over 25 years of experience in estimation, planning, and forecasting using statistical and econometric methods, with particular expertise in risk, expected utility, discounting, binomial-tree valuation methods, financial econometrics models, Monte Carlo simulations, macroeconomics, and exchange rate modeling. Dr. Akinkunmi has performed extensive software development for quantitative analysis of capital markets, revenue and payment gateway, predictive analytics, data science, and credit risk management. He has worked as a business strategist with AT&T, Salomon Brothers, Goldman Sachs, Phibro Energy, First Boston (Credit Suisse First Boston), World Bank, and Central Bank of Nigeria. He has taught and researched at Manhattan College, Riverdale, NY; Fordham University, New York, NY; University of Lagos, Lagos, Nigeria; State University of New York-FIT, New York, NY; Montclair State University, Montclair, NJ; and American University, Yola, Nigeria. In 1990, he founded Technology Solutions Incorporated (TSI) in New York, which focused on data science and software application development for clients including major financial services institutions. Dr. Akinkunmi is the former Honorable Commissioner for Finance, Lagos State, Nigeria.