【R培训】Solve Statistical and Econometrical problems Using R

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R语言现场培训_全英文授课
October@Beijing

Lecturer Introduction：

Gino, a Chinese in his thirty's, who majored in mathematics for a bachelor's degree andstatistics for a master's degree from a prestigious university in an earliertime, has a rich experience in R programming and teaching.

Gino solved tonsof modelling problems when he worked in the economical sector of a Top 500 Global corporations.

By doing so, heaccumulated abundant skills in modelling by kinds of software, such as SAS, R,Matlab and Stata. Among these, he chose R as the most powerful one.

During 2013, Ginogave several classes about how to use R to model in the field of statistics andEconometrics, launching a R study and training trend in this Forum, benefitingthousands of people in using R to solve the problems in study or research, andgot a highly credit from his students.

Although not anative speaker of English, Gino speaks and writes English fairly well. He can give excellent courses on statistics and econometrics using R.  

Course Introduction：

The course covers all the fundamentals in statistics and econometrics and starts from the introduction of R basic function in data management and statistics, by giving some typical examples to demonstrate key issues of R.

You will masterpractical skills to deal with the annoying data management problem before doinga seconds-running problem.

Graphing inmodelling becomes more and more important, the course won't let you down if youwant to draw elegant pictures to show your results clearly. There's no moresoftware can draw beautiful images than R.

Of course, loopsand function are the core of R, which are fully explained in this course withample instances.

Later on, thiscourse steps into the field of Probability Theory and Statistics. You will findthe magic effect that all the aspects, such as Estimation of Parameters,Testing Hypotheses, Linear Least Squares, could be done in R within secondsconveniently. Linear models could be realized in R swiftly.

The course stretches the contents to two fundamental turfs, Multivariate Analysis and Time Series,which are widely used in all kinds of industries.

The course chooses Meta-analysis as a final part because it is getting significant concernsnowadays.

Course Features:

1. Intentionally avoids the complex mathematical formulas but the most important ones and layemphasis on the idea, method of certain statistical method or concept;

2. Demonstratingabstract thoughts by using concrete examples that could be happened in ourdaily study and research frequently;

3. Never skips thecodes hazing students to understand and clearly explains every codes and resultsof every model. That means, statistical meanings of every results could befully understood by the students;

4. Not alwayspreaching at students while passing out problems for group discussion andlearning by doing;

5. Patientlyanswering questions from the students in the middle of class and encouragesprompt questions while listening.

Target Candidates:

1. Those who wantto step into the career of data analysis using a powerful tool with acomparative weak foundation of statistics and computer skills;

2. Studentsoverseas who want to grasp a statistical software to advance the research intheir study, especially those quickly makes use of these methods in the paperpublishing;

3. All the peoplewho wish a systematical training both on statistics and R.

Outline:

A short introduction to R	How to install R and Rstudio
	How to install the packages and make use of  them
	How to get help when meeting difficulties
	Some examples Using R
	The do's and don'ts when Using R
Importing kinds of data into R	Concatenating Data with the c Function
	Combining variables and data Using cbind(),  rbind(),vector(),data.frame(),list().
	Creating matrix, array and calculating them
	Importing data from other sources
Summarizing and managing the subsets of  data	Using str function, attach function and $  sign to   access variables from a data frame
	Sorting the data according to certain  conditions
	Merging two data sets
	Factoring categorical variables
	Exporting data
	"apply" function family
	Using table function to find distributions
Graphing skills with R	Exploring the plot function from basic to  proficiency
	Symbols, Colours, Sizes and legend
	How to add a smoothing line
	The Pie Chart, Bar Chart and Strip Chart
	The Boxplot
	Histogram and QQ plot (How to test normal  distribution)
	Adding the Mean to a Cleveland Dotplot
	The Coplot
Conditions, Loops and Functions	Using "if" for condition choices
	Some examples Using "for" syntax
	Constructing the Loop
	Zeros and NAs
	Using loop and function to calculate index
Basic Probability Theory Using R	Random Variables in R (Discrete and  Continuous)
	Joint distributions and conditional  distributions
	Covariance and correlation (matrix)
	Histograms, Density Curves, and  Stem-and-Leaf Plots
Estimation of Parameters Using R	The Method of Moments
	The Method of Maximum Likelihood
	Using t.test() to Estimate Parameters
	Estimation for abnormal distributions
	The Bayesian Approach to Parameter Estimation
Testing Hypotheses Using R	Significance Level and the Concept of  P-value
	The Null Hypothesis
	Likelihood Ratio Tests
	Tests for Normality
	Comparing Two Samples (Independent or  paired)
The Analysis of Categorical Data with R	Fisher's Exact Test
	The Chi-Square Test of Homogeneity and  Independence
	The Independent Test of contingency table
	Odds Ratios
Linear Least Squares (Regression) with R	Simple Linear Regression
	Statistical Properties of Least Squares  Estimates
	Multiple Linear Regression examples
	Partially Linear Regression
	Linear Regression with Time Series Data
	Linear Regression with Panel Data
	The 2SLS Method by R (examples)
	Regression Diagnostics
Other Important Regression Models by R	Generalized Linear Models
	Logistic Regression Model (Example and  Analysis)
	nls() function for Non-Linear Models
	Regression Models for Count Data
	Tobit and Censored Dependent Variables
	Case with Quantile Regression
Multivariate Analysis with R	Discriminant Analysis (Theory and Examples)
	Cluster Analysis (Theory and Examples)
	Principal Component Analysis (Theory and  Examples)
	Factor Analysis (Theory and Examples)
Monte Carlo Method with R	Introduction to Monte Carlo Method
	Random shot point Method
	Mean Value Method
	Precision of the Two Method
Time Series with R	Differences and Lags
	Creating ARIMA Models and Diagnostics
	Predicted Values
	Durbin-Watson Test for Autocorrelation
	Stationarity,Unit Roots, and Cointegration
	Error Correction Model
Introduction to Meta-Analysis with R	Fixed-Effects and Random-Effects in  Meta-Analysis
	Meta-Analysis with Binary Data
	Meta-Analysis for Continuous Data
	Heterogeneity in Meta-Analysis
	Meta-Regression

The Registration Process:
1, Mail your name and University / Company to vip@pinggu.org;
2, Pay online after the confirming call from us (RMB 12000 / RMB 8000 for full-time college student);
3, Prepare the course after receiving the lecture note one week before the course.

Contact Information：
QQ：1143703950
Mail：vip@pinggu.org
Tel：010-68478566

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