1 Programming 1.1 M file 1.1.1 Program Control Statements 1.1.2 M-File Functions 1.2 anonymous functions 2 Computational statistics with Matlab 2.1 Functions on Probability and Statistics 2.1.1 Probability distribution 2.1.2 Descriptive statistics 2.1.3 Statistical plotting 2.1.4 Linear model 2.1.5 Multivariate Statistics 2.2 Monte Carlo with Matlab 2.2.1 Monte Carlo Assessment of Hypothesis Testing 2.2.2 MCMC with matlab 3 Symbolic computation with matlab 3.1 Creating Symbolic Variables and Expressions 3.2 Calculus 4 Optimization 4.1 Unconstrained Minimization Example 4.2 Nonlinear Inequality Constrained Example
10.1 Introduction 10.2 Empirical Studies and Statistical Inference 10.3 Important Features of Big Data 10.4 Big Data Analysis and Statistics 10.5 Machine Learning and Statistics 10.6 Conclusion
l Chapter 1-Key terms Collection, summarization, analysis, and reporting of numerical findings Statistics-Two Usages A. The study of statistics B. Statistics as reported sample measures
Why statistics is important How statistics is used in business The sources of data used in business The types of data used in business The key terms The basics of Microsoft Excel
Objectives The learners should be able to: （1) write an test-article with statistics （2)understand the different functions of different graphs. （3). write simple authentic report making use of statistics （4) convert text to graphs and insert graphs via word processor
1. Mark Berenson, Timothy Krehbiel, David Levine, 2005, \Basic Business Statistics: Concepts and Applications\, Prentice Hall, 2005. 2. Keller& Warrack, Statistics for Management and Economics, 6th Edition, South-
1 Goodness of Fit Statistics We would like some measure of how well our regression model actually fits the data.* We have goodness of fit statistics to test this: i.e. how well the sample regression function (srf) fits the data. The most common goodness of fit statistic is known as R2. One
