Econometrics with R for IS and Thesis [MFU MBA in LSCM]
You are lucky!
Econometrics seem hard and tough. But with R, econometrics analysis is easy and FREE!
There are 7 more reasons why we should use R for statistics and econometrics analysis.
For the special course on Econometric analysis with R for MBA dissertation at Mae Fah Luang University, please do this 3 things as follow.
1. Download R
First please download the software from the official website www.r-project.com
- If you use Window PC, please use this link.
- If you use Apple Mac, please use this link.
- If you use Linux, please use this link.
2. Download R-Studio
The default R interface is not that good. There is a more optimised and user-friendly IDE for R called “R Studio”. R Studio is also free and available for Window, Linux and Mac users. It is my best IDE for R in various aspects. For students, R Studio is also a good one.
So please also download R Studio here (select RStudio desktop version)
3. Consult my online R-Manual and R related post here
Please have a look and play with the ASER manual before the class, see you!
- Slides used in the lecture can be downloaded here here
- Codes used in the classed are as follow.
# codes used in the course "econometrics with R" at Mae Fah Luang University on 30th March 2013 # use R as a calculator 2+3 # start an analysis data <- mtcars # call variable names names(data) # draw a histogram hist(data$mpg, xlab="miles per gallon", main = "Histogram of MPG", col="blue") hist(data$hp, xlab="miles per gallon", main = "Histogram of hp", col="red") # transform hp by sqr / exp hist(sqrt(data$hp)) # create new variable by sqrting hp data$sqrt_hp <- sqrt(data$hp) # call 2nd obs. data[2,] data[2:5,] data[2:5,2] barplot(data$hp) plot(data[2,]) # descriptive analysis library("psych") describe(data) # Mean testing # pair sample t-test t.test(data$mpg, data$cyl) t.test(data$drat, data$wt) # independent variable t-test t.test(data$mpg ~ data$am) # regression model 1 model1 <- lm(mpg ~ gear, data = data) summary(model1) anova(model1) # regression model 2 model2 <- lm(mpg ~ gear + hp, data = data) summary(model2) plot(model2) # test if model2 is better than model1 statistically anova(model1, model2) <pre>