Theoretical Paper
- Computer Organization
- Data Structure
- Digital Electronics
- Object Oriented Programming
- Discrete Mathematics
- Graph Theory
- Operating Systems
- Software Engineering
- Computer Graphics
- Database Management System
- Operation Research
- Computer Networking
- Image Processing
- Internet Technologies
- Micro Processor
- E-Commerce & ERP
- Numerical Methods Tutorial
Practical Paper
- C Programming
- C
- Data Structure Using C, C ++
- Programming in R
- Programming with Python
- Machine Learning
- Swift
- Firebase
- Android
- iOS Development
- Django
- PHP
- Arduino
- Internet of Technology
- IOT Projects
- Dart Programming
- Flutter
- Flutter Tutorials
- Kotlin Tutorial
- Laravel Tutorial
- VueJS Tutorial
- Go Lang
- Rust
- Apex
Industrial Training
R - Chi Square Test
Chi-Square test is a statistical method to determine if two categorical variables have a significant correlation between them. Both those variables should be from same population and they should be categorical like − Yes/No, Male/Female, Red/Green etc.
For example, we can build a data set with observations on people's ice-cream buying pattern and try to correlate the gender of a person with the flavor of the ice-cream they prefer. If a correlation is found we can plan for appropriate stock of flavors by knowing the number of gender of people visiting.
Syntax
The function used for performing chi-Square test is chisq.test().
The basic syntax for creating a chi-square test in R is −
chisq.test(data)
Following is the description of the parameters used −
- data is the data in form of a table containing the count value of the variables in the observation.
Example
We will take the Cars93 data in the "MASS" library which represents the sales of different models of car in the year 1993.
library("MASS")
print(str(Cars93))
When we execute the above code, it produces the following result −
'data.frame': 93 obs. of 27 variables: $ Manufacturer : Factor w/ 32 levels "Acura","Audi",..: 1 1 2 2 3 4 4 4 4 5 ... $ Model : Factor w/ 93 levels "100","190E","240",..: 49 56 9 1 6 24 54 74 73 35 ... $ Type : Factor w/ 6 levels "Compact","Large",..: 4 3 1 3 3 3 2 2 3 2 ... $ Min.Price : num 12.9 29.2 25.9 30.8 23.7 14.2 19.9 22.6 26.3 33 ... $ Price : num 15.9 33.9 29.1 37.7 30 15.7 20.8 23.7 26.3 34.7 ... $ Max.Price : num 18.8 38.7 32.3 44.6 36.2 17.3 21.7 24.9 26.3 36.3 ... $ MPG.city : int 25 18 20 19 22 22 19 16 19 16 ... $ MPG.highway : int 31 25 26 26 30 31 28 25 27 25 ... $ AirBags : Factor w/ 3 levels "Driver & Passenger",..: 3 1 2 1 2 2 2 2 2 2 ... $ DriveTrain : Factor w/ 3 levels "4WD","Front",..: 2 2 2 2 3 2 2 3 2 2 ... $ Cylinders : Factor w/ 6 levels "3","4","5","6",..: 2 4 4 4 2 2 4 4 4 5 ... $ EngineSize : num 1.8 3.2 2.8 2.8 3.5 2.2 3.8 5.7 3.8 4.9 ... $ Horsepower : int 140 200 172 172 208 110 170 180 170 200 ... $ RPM : int 6300 5500 5500 5500 5700 5200 4800 4000 4800 4100 ... $ Rev.per.mile : int 2890 2335 2280 2535 2545 2565 1570 1320 1690 1510 ... $ Man.trans.avail : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 1 1 1 1 ... $ Fuel.tank.capacity: num 13.2 18 16.9 21.1 21.1 16.4 18 23 18.8 18 ... $ Passengers : int 5 5 5 6 4 6 6 6 5 6 ... $ Length : int 177 195 180 193 186 189 200 216 198 206 ... $ Wheelbase : int 102 115 102 106 109 105 111 116 108 114 ... $ Width : int 68 71 67 70 69 69 74 78 73 73 ... $ Turn.circle : int 37 38 37 37 39 41 42 45 41 43 ... $ Rear.seat.room : num 26.5 30 28 31 27 28 30.5 30.5 26.5 35 ... $ Luggage.room : int 11 15 14 17 13 16 17 21 14 18 ... $ Weight : int 2705 3560 3375 3405 3640 2880 3470 4105 3495 3620 ... $ Origin : Factor w/ 2 levels "USA","non-USA": 2 2 2 2 2 1 1 1 1 1 ... $ Make : Factor w/ 93 levels "Acura Integra",..: 1 2 4 3 5 6 7 9 8 10 ...
The above result shows the dataset has many Factor variables which can be considered as categorical variables. For our model we will consider the variables "AirBags" and "Type". Here we aim to find out any significant correlation between the types of car sold and the type of Air bags it has. If correlation is observed we can estimate which types of cars can sell better with what types of air bags.
# Load the library.
library("MASS")
# Create a data frame from the main data set.
car.data <- data.frame(Cars93$AirBags, Cars93$Type)
# Create a table with the needed variables.
car.data = table(Cars93$AirBags, Cars93$Type)
print(car.data)
# Perform the Chi-Square test.
print(chisq.test(car.data))
When we execute the above code, it produces the following result −
Compact Large Midsize Small Sporty Van
Driver & Passenger 2 4 7 0 3 0
Driver only 9 7 11 5 8 3
None 5 0 4 16 3 6
Pearson's Chi-squared test
data: car.data
X-squared = 33.001, df = 10, p-value = 0.0002723
Warning message:
In chisq.test(car.data) : Chi-squared approximation may be incorrect
Conclusion
The trend in the above graph helps us predicting the probability of survival at the end of a certain number of days.