Chi-square inspectionis a statistical method to determine whether there is a significant correlation between two categorical variables. These two variables should come from the same population and they should be similar yes/no, male/female, red/green, etc.
For example, we could build a dataset that observes people’s ice cream buying patterns and try to correlate a person’s gender with the taste of their favorite ice cream. If relevance is found, we can plan the appropriate taste stock by knowing the number of genders of the person visited.
grammar
The function used to perform the chi-square test is()。
The basic syntax for creating chi-square tests in R language is
(data)
The following is a description of the parameters used
data is data in the form of a table containing the count values of the variables in the observation.
example
We will get Cars93 data in the "MASS" library, which represents sales of different models of cars in 1993.
library("MASS")
print(str(Cars93))
When we execute the above code, it produces the following result
'': 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 ... $ : 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 ... $ : num 18.8 38.7 32.3 44.6 36.2 17.3 21.7 24.9 26.3 36.3 ... $ : int 25 18 20 19 22 22 19 16 19 16 ... $ : 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 ... $ : int 2890 2335 2280 2535 2545 2565 1570 1320 1690 1510 ... $ : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 1 1 1 1 ... $ : 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 ... $ : int 37 38 37 37 39 41 42 45 41 43 ... $ : num 26.5 30 28 31 27 28 30.5 30.5 26.5 35 ... $ : 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 results show that there are many factor variables in the data set and can be considered categorical variables. For our model, we will consider the variables "AirBags" and "Type". Here, our goal is to find out any significant correlation between the type of car sold and the type of airbag. If correlation is observed, we can estimate which type of car can better sell what type of airbag.
# Load the library.
library("MASS")
# Create a data frame from the main data set.
<- (Cars93$AirBags, Cars93$Type)
# Create a table with the needed variables.
= table(Cars93$AirBags, Cars93$Type)
print()
# Perform the Chi-Square test.
print(())
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:
X-squared = 33.001, df = 10, p-value = 0.0002723
Warning message:
In () : Chi-squared approximation may be incorrect
in conclusion
The result shows that the p value is less than 0.05, which indicates the string correlation.
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