§5-1 Basic principle of polarograpy §5-2 Equation of diffusion current A basis of polarographically quantitative analysis §5-3 Halfwave potential —polarographic qualitative analysis §5-4 Interference currents and their removing methods §5-5 Characteristics and disadvantages of polarographic analysis §5-6 Polarographic Catalytic Wave §5-7 Single-sweep polarography(单扫描极谱法） (Oscillographic polarography，示波极谱法) § 5-8 Squar-wave polarography §5-9 Pulse polarography §5-10 Stripping voltammetry §5-11 单指示电极安培滴定（Amperometric titration with single indicating electrode, polarographic titration) §5-12 双指示电极安培滴定（永停滴定） （Dead-stop end point titration） §5- 13 双指示电极电位滴定 （potentiometric titration with double indicating electrodes)

6.1 Non-asymptotic Analysis: Concentration inequality 6.2 Non-asymptotic Analysis: extreme singular values

• MSC.EASY5 Overview • Getting Started • Help and Documentation • Model Building & Simulation • Operating Point and Steady State Analysis • Linear Analyses • Data Tables and the Matrix Editor • Analysis Tools & Methodology • Simulation and Integration • Interactive Simulation • Miscellaneous Topics • MSC.EASY5 Architecture • Writing Code in MSC.EASY5 • Summary

Univariate versus Multivariate Analysis Univariate analysis Solely interested in an isolated characteristic of a set of objects, irrespective of other variable characteristics possessed by the objects Examples the study of the achievement test scores of students X the salaries of workers

2.1 Analysis 2.2 Asymptotic Order of Growth(渐进分析) 2.3 Three Techniques for Designing Algorithms 1. Greedy Algorithms 2. Divide and Conquer 3. Dynamical Programming

Earnings Persistence Recasting and Adjusting Earnings persistence is a key to effective equity analysis and valuation Analyzing earnings persistence is a main analysis objective

 3.1 Learning Problems  3.2 The least square method (LSM)  3.3 Linear regression analysis  1 Simple Linear Regression  2 Multiple Regression  3 Understanding the Regression Output  4 Coefficient of Determination R2  5 Validating the Regression Model

 2.1 Overview of data types  2.2 Review of Data pre-processing tools and platforms  2.3 Clean, storage and management of raw data  2.4 Collections of data analysis and data mining

 1.1 What’s big data?  1.2 Overview of data analysis  1.3 Overview of data mining  1.4 Make requirement for different professional applications

Short-Term Forecasting Objective and Users Main objective is analysis of liquidity Sufficient cash to effectively operate Ability to service short-term debt Liquidity is of interest to both internal and external users Managers(cash for operations) Creditors (loan safeguards) Investors(return implications) Auditors( going concern analysis) Numerous others