Publications by H. K. Tseng
Statistical Learning: Week 5
Binary outcome as a generalization of linear regression model with limited dependent variables Estimating a model of binary outcome using lm() function. library(ISLR) data(Default) names(Default) ## [1] "default" "student" "balance" "income" ## Convert the outcome variable "default" to numeric: Yes = 1, No = 0 # The ifelse logic: ifelse(if a va...
993 sym R (35759 sym/113 pcs) 7 img
Statistical Learning: Week 5
Binary outcome as a generalization of linear regression model with limited dependent variables Estimating a model of binary outcome using lm() function. library(ISLR) data(Default) names(Default) ## [1] "default" "student" "balance" "income" ## Convert the outcome variable "default" to numeric: Yes = 1, No = 0 # The ifelse logic: ifelse(if a va...
993 sym R (35517 sym/113 pcs) 7 img
Statistical Learning: Week 5
Binary outcome as a generalization of linear regression model with limited dependent variables Estimating a model of binary outcome using lm() function library(ISLR) data(Default) names(Default) ## [1] "default" "student" "balance" "income" ## Convert the outcome variable "default" to numeric: Yes = 1, No = 0 # The ifelse logic: ifelse(if a var...
1002 sym R (35605 sym/125 pcs) 7 img
Statistical Learning: Week 4
Non-normality test ## Load Prestige data from the "car" package library(car) ## Loading required package: carData lm.fit <- lm(prestige ~ education, data = Prestige) plot(lm.fit) ## Shapiro test of normality shapiro.test(Prestige$prestige) ## ## Shapiro-Wilk normality test ## ## data: Prestige$prestige ## W = 0.97198, p-value = 0.02875 ...
2343 sym R (31283 sym/91 pcs) 13 img
Statistical Learning: Week 4
Non-normality test ## Load Prestige data from the "car" package library(car) ## Loading required package: carData lm.fit <- lm(prestige ~ education, data = Prestige) plot(lm.fit) ## Shapiro test of normality shapiro.test(Prestige$prestige) ## ## Shapiro-Wilk normality test ## ## data: Prestige$prestige ## W = 0.97198, p-value = 0.02875 ...
2341 sym R (31247 sym/91 pcs) 13 img
Statistical Learning: Week 3
Fitting a linear model A description of variable names and measures can be seen here. # install.packages("ISLR") library(ISLR) ## load the data data(Carseats) ## Check out variable "names" in the data names(Carseats) ## [1] "Sales" "CompPrice" "Income" "Advertising" "Population" ## [6] "Price" "ShelveLoc" "Age" ...
331 sym R (12867 sym/51 pcs) 5 img
Statistical Learning: Week 3
Fitting a linear model A description of variable names and measures can be seen here. # install.packages("ISLR") library(ISLR) ## load the data data(Carseats) ## Check out variable "names" in the data names(Carseats) ## [1] "Sales" "CompPrice" "Income" "Advertising" "Population" ## [6] "Price" "ShelveLoc" "Age" ...
331 sym R (12873 sym/51 pcs) 5 img
Statistical Learning: Week 3
Fitting a linear model A description of variable names and measures can be seen here # install.packages("ISLR") library(ISLR) ## load the data data(Carseats) ## Check out variable "names" in the data names(Carseats) ## [1] "Sales" "CompPrice" "Income" "Advertising" "Population" ## [6] "Price" "ShelveLoc" "Age" ...
330 sym R (12847 sym/51 pcs) 5 img
Statistical Learning: Week 2
Installing R and RStudio R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. RStudio is an integrated development environment for R, a programming language for statistical computing and graphics. You need to download base R in order to run RS...
2540 sym R (23468 sym/140 pcs) 12 img 2 tbl
Statistical Learning: Week 2
Installing R and RStudio R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. RStudio is an integrated development environment for R, a programming language for statistical computing and graphics. You need to download base R in order to run RS...
2515 sym R (13852 sym/117 pcs) 12 img