And also important skills in data analysis is "Data Handling".. 2 Class 2: Basic Characters of Data a Using this data, learn basic calculation command in R.. a Distribute Excel File incl
Trang 1COURSE SYLLABUS Course Title: Quantitative and Qualitative Analysis
1 Course code VJU6010
2 Numbers of credits: 2 credits
3 Prerequisite courses:
4 Teaching language: English (Japanese if so requested)
5 Lecturers
5.1 Lecturer 1
Shinichi TAKEDA (Mr.), Associate Professor/ Faculty of International Studies, Takushoku University (Japan), M Eng
Lecturer 2
(Full name; Position/Job title, Academic degree; Institutions)
6 Course objective:
The purpose of “Analysis” are elucidation of Causality some factor and reproduction
of construction of “Model” expressed by equation Students in other Lectures or research activities, will build a variety of models, such as prediction and speculation
In this lecture it is intended to learn the basics that will be introduced from the basics of statistics techniques It intended that the process proceeds to Probability Statistics from Descriptive Statistics
I think all of the students no need to learn a detailed statistics It is important, input section is accurate data and including the conversion of the data "Data Handling"
In judgment for output, "Test" is the most important concept Basic of quantitative analysis is “t-test” and basis of qualitative analysis “chai squared test”
At last, learn basis of all of the models “Regression Analysis” Regression analysis is expresses relationship between the results and the factors simplicity, which has been used in many fields Also discuss for further Action many Note, Model improvement, analysis of Time-series data, and more etc
7 Expected learning outcomes
7.1 Knowledge competence
The students are expected to construct their own hypotheses, process the data, and write the analytical reports in the end There is a variety of statistical
Trang 2analysis software; this Class uses Excel & R I hope all students not only while you are in university but also to analyzed in the future
Past time existing statistics tools are always expensive However, R is a free software, is becoming a standard in the world Techniques that can be described in this class is limited However, I believe you can build a variety of models using R
7.2 Skills
I have been especially emphasized, Data Handling and discussion of analysis results Of course it is accompanied by operation of practical data and Taming
of Basic Multivariate Analysis on R.
And also important skills in data analysis is "Data Handling" It is possible to obtain a large amount of data from the database and measurement equipment From these data, to extract what you need, is a technique to modify the format of the purpose is "Data Handling" Acquisition of the skills is also important
7.3 Ethics
We are surrounded by a wealth of data And “Analysis” can also be arbitrarily performed We must objectively analyze for data I want to teach it to emphasize, the duty of scientists
Of course, it is NG as illegally obtained software, data
8 Assessment methods/Grading system
8.1 Attendance and class participation
The minimum attendance ratio is 2/3 of total classes, yet attendance alone would not be counted for marks This class is stacked important point, which is repeatedly Must learn the review and exercise
8.2 Mid-term and final exams
Attendance and short papers : 40%
Final exam : 60%
9 Learning materials
9.1 Required textbook and other material
Resume of lecture text prepared by me will be provided at each class
9.2 Reference materials
<<If the reference of the statistics elementary R, all OK And “R Help” also
Trang 3useful, not always necessary Reference book The following is an example; >> Julian J Faraway, “Linear Models with R”, Chapman & Hall/CRC, ISBN 978-1584884255, 2004
9.3 Computer & Software
In this Course, Every students need own notebook computer We will build a system through the lesson And also need Microsoft Excel (Office) Mainly use free statistics application R is installed in the class
10 Course description (Briefly explaining in approximately 120 words)
First understand basic characteristics of the data Data is divided into
“Quantitative”, “Qualitative”, “Macro” and “Micro” Calculated to Basic Statistical Values For qualitative data, simple aggregation, to create a cross-tabulation Chart also important analysis tools In order to draw some special chart, you must use programing the macro data is database Next section describes how to use them
In inferential statistics is required understanding of probability and probability distribution Basic probability are N & t distribution often use to sequence tests of Hypotheses
Then through the Correlation, Very important analysis is Regression; basis of all of the models Multiple Regression needs selection of independent Variables, Dummy Variables Work out in a variety of data Try and error Training Introducing for other Research Method technics at final Lecture
11 Course contents
# Depending on the basic skills term of class students, to adjust speed of progression and content all contents
(1) Class 1: Excel Skill check and Install R
Free statistics application "R” install it on your PC It unifies all of members work environment Also check the basic operation of Excel Excel is the knowledge required in all subjects If student skill of Excel is low, they need special training
(2) Class 2: Basic Characters of Data
a) Using this data, learn basic calculation command in R
b) Data is divided into “Quantitative” and “Qualitative” by the
Trang 4measurement unit Also it can be divided into “Macro” and “Micro” data as an aggregate unit To understand the respective qualities c) Most popular file format type “Text File” and its special type “csv File” Explanation for the important knowledge to the exchange of data
(3) Class 3: Obtaining data
Obtain of data is three ways
a) Direct Input; Necessary to prevent typing errors If you use Excel, and use the data settings and Excel-VBA (uses a rudimentary programming techniques)
b) From File; Use linking Excel and R, or output data file from measuring instruments We must learn the conversion of data and data format c) From Database; How to use about Database system Mainly World Bank’s “World Development Indicator (WDI)”
To learn about the a), b) methods C) is how to use about Database system Mainly World Bank’s “World Development Indicator (WDI)”
(4) Class 4: Manage concepts “Object” on R
a) Distribute Excel File include Quantitative data of micro “ex1; Score of 1 class” and Qualitative data of micro “ex2; Distortion of the dice”
b) Learn management of concepts “object”, “scalar”, ”vector” and
“matrix”
c) To be able to manipulate the elements of object
d) What is “R Script”?
# Work of these "data handling" is very important If you can fully remember the analysis method cannot these tasks, you will not be able to perform data analysis
(5) Class 5: Chart
In this section, Confirmation of the basic chart "Bar chart", "Line chart",
"Circle chart", "Scatter plot" You will learn the "label containing scatter plot" using Excel-VBA And you learn a chart plotting by R R of the graphics have a common function, if you can understand, drawing of repeatedly makes it quiet easier
(6) Class 6: Descriptive Statistics
Trang 5a) For Quantitative data, Calculated to “Basic statistics value” using “ex1; Score of 1 class” And make “Frequency Table” and “Histogram” using distributed R script
b) For Qualitative data, simple aggregation, to create a cross-tabulation using “ex2; Distortion of the dice” using distributed R script
c) Distribute Excel File include Quantitative data of micro “ex3; Score of
100 class” using distributed R script d) Use c) data, 100 classes’ “Basic statistics value” calculate and
“Histogram” drawing continuously output to text and bmp file using modified R script a)
(7) Class 7: Interval Estimation
To learn sequence “Test” of hypotheses, needs to learn basic probability statistics distribution “Normal Distribution” & “t distribution” “Interval Estimation” is a method to estimate of the section which is expected to include the mean μ, from "A lower bound” to "B upper bound" using N & t distribution
a) Calculation of the probability of using the normal distribution
b) The difference of the standard normal distribution and t distribution c) Calculation of the interval estimation
# Class 7 using Excel for easy to understand
(8) Class 8: Test of Hypotheses,
“Test of Hypotheses” is “Reject the hypothesis (and keep alternative hypothesis)” or “not reject null hypothesis” statistical procedures to decide
on the basis of observations data
a) Distribute Excel File include Quantitative data of macro “ex4; 10 samples Weight of Industrial Martial ”
b) Rejection region is "should reject the null hypothesis of statistical probability of a set amount"
c) “Population Average Test” with t- Distribution using d) data
d) Chai square test using “ex2; Distortion of the dice”
# d) – f) using distributed R script
(9) Class 9: Correlation, Correlation test
a) Distribute Excel File include Quantitative data of macro “ex5; GDP
Trang 6capital vs CO2 Emission”
b) Through the Correlation, Correlation test using a) data
c) Exercises: Reading of data from csv files, Extraction of the vector and variable
(10) Class 10: Regression Analysis (1)
Regression Analysis is very important because basis of all kind of “MODEL” a) Simple Linear Regression with OLS method Learn to “Least-squares estimators”, “Error & Residuals” and study for regression result using distributed R script
b) Change to variables “Log” Macro analysis in subject “Development Economics”, Major technique to change “Log” for independent variable
is used Why to consider, must think advantages and disadvantages of logarithmic
(11) Class 11: Regression Analysis (2)
Multiple Regression
a) Distribute Excel File include Quantitative data of macro “ex6; GDP capital vs multi Infrastructure level indicator”
b) Multiple Regression Learn to “Selection of Independent (Explanatory) Variables” under “Stepwise method” using modified R script class 8 c) c) Major Problem in multiple regression analysis ' multi-co-linearity - Usually call ”multico”
d) VIF (Variance inflation Factor ） and Tolerance are indicator of multi-co-linearity “Residual vs Fitted Plot”, “normal Q-Q plot” are diagnostics for Residuals
(12) Class 12: Regression Analysis (3)
Improvement Multiple Regression model
a) Dummy variable Use Dummy variables, Y (Explained variables) Include
‘External shocks’ or ’Timing and location of the structural changes’ value of certain case
b) Exercises using the ex6 data In number of particular country data increase or decrease effect on the estimation
c) AIC (Akaike's Information Criterion).Amount of information that was conceived in order to balance the factors and the model fit Therefore,
Trang 7independent variables to the minimum necessary to suppress or reduce the number of which is actually difficult to determine The AIC provides a solution to this problem
(13) Class 13: Regression Analysis (4)
Type of regression equation There are various types of expressions of the regression analysis It can be easily changed in R To introduce it More importantly, the results is how to change Passed Class to reference, repeated trial and error
a) Monomial (linear) regression analysis
b) Polynomial regression
c) Regression “Line” or “Curve”
(14) Class 14: Time series Data
In addition, it is determined whether or not there is a time-series correlation in the data The data that has autocorrelation in the "Time” or
"Spatial" is confirmed, it is necessary to apply the measures
a) Confirmation by “DW-ratio; Durbin Watson ratio” is one of explore indicator
b) Putting the "Trend dummy" and "Cycle dummy" are simple solution reduction correlation effect from data
c) Data used “ex7; Relationship parking cars and week change factor or events”
(15) Regression Analysis (4)
Exercise for Multiple Regression between Income level and any kind of indicator comparison of East Asian countries
a) Using WDI database, it the student intentionality selection the data to get csv files
b) Using this data, we perform a regression analysis The results are summarized in the report
c) Introducing for other Research Method technics