library(readr)
ab=read_csv("C:/Users/USER/Downloads/Bank_Data.csv")
## Rows: 300 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Customer_ID, Branch_Type, Loan_Status
## dbl (1): Processing_Time_Days
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
ab
## # A tibble: 300 × 4
## Customer_ID Branch_Type Loan_Status Processing_Time_Days
## <chr> <chr> <chr> <dbl>
## 1 CU001 Urban Not_Processed 7.2
## 2 CU002 Urban Processed 3.6
## 3 CU003 Rural Processed 3.9
## 4 CU004 Rural Not_Processed 6.5
## 5 CU005 Rural Processed 5.5
## 6 CU006 Rural Processed 2.6
## 7 CU007 Urban Not_Processed 6.5
## 8 CU008 Urban Processed 4
## 9 CU009 Urban Not_Processed 7.5
## 10 CU010 Urban Not_Processed 6.9
## # ℹ 290 more rows
library(stats)
#1
processed=ab$Processing_Time_Days[ab$Loan_Status=="Processed"]
processed
## [1] 3.6 3.9 5.5 2.6 4.0 5.7 6.2 3.5 4.7 5.4 2.9 3.9 4.4 5.6 5.0 5.9 5.9 5.0
## [19] 5.8 4.8 4.5 5.2 3.5 4.0 3.8 3.2 4.8 5.0 5.4 4.6 4.7 5.7 4.7 4.4 2.8 3.6
## [37] 3.0 4.5 2.9 4.2 4.2 4.6 4.3 3.7 4.0 4.9 5.6 5.3 4.2 4.4 5.3 3.6 4.0 4.6
## [55] 4.5 5.4 3.7 5.5 4.0 4.0 4.5 5.2 3.5 4.3 2.8 5.4 7.9 5.1 4.7 5.3 5.9 3.7
## [73] 4.9 5.7 5.5 3.8 4.1 5.1 3.8 4.3 3.3 4.4 5.3 2.1 4.9 4.2 4.1 4.9 6.0 4.3
## [91] 4.6 3.8 4.0 4.0 5.7 2.7 3.3 5.1 4.2 5.1 5.3 3.8 4.2 4.6 4.3 5.1 4.9 4.0
## [109] 5.5 5.3 4.8 4.9 4.4 4.8 5.4 3.8 4.5 2.2 3.2 4.6 5.8 4.4 5.2 4.6 4.0 4.8
## [127] 4.2 4.4 4.1 5.1 4.0 5.4 4.5 4.4 5.2 4.2 4.4 3.5 3.6 3.9 4.4 4.5 5.2 4.7
## [145] 3.6 4.3 6.1
not_processed=ab$Processing_Time_Days[ab$Loan_Status=="Not_Processed"]
not_processed
## [1] 7.2 6.5 6.5 7.5 6.9 7.7 6.2 6.6 6.8 6.6 7.5 6.2 6.2 6.7 6.3 7.1 8.2 6.2
## [19] 6.6 6.5 5.0 6.1 9.4 6.8 6.9 7.4 5.6 6.8 8.5 6.3 4.9 5.6 7.2 6.6 7.2 6.2
## [37] 5.5 6.3 6.9 5.3 6.5 6.4 4.9 6.2 6.5 5.8 4.7 7.7 5.7 6.0 7.5 6.4 7.1 6.9
## [55] 7.2 5.4 6.0 7.5 7.1 5.4 6.8 7.5 7.3 7.1 7.0 7.3 6.1 6.5 6.4 7.4 7.4 7.5
## [73] 7.7 6.9 6.6 6.7 5.0 6.4 7.0 4.5 7.1 6.1 7.2 6.6 7.3 6.8 6.5 5.7 5.7 6.4
## [91] 5.8 5.8 6.5 5.6 5.2 5.7 6.2 5.7 6.1 7.4 6.2 5.7 5.0 8.0 6.5 7.2 7.8 6.3
## [109] 5.2 5.9 5.7 6.4 5.5 6.5 7.1 6.4 6.3 8.6 6.9 8.0 5.8 5.8 5.8 5.7 6.6 6.7
## [127] 6.6 6.6 4.8 6.5 6.1 7.0 5.9 6.4 7.3 6.5 7.2 6.6 6.2 6.9 5.8 6.3 5.2 6.4
## [145] 6.3 7.2 6.9 6.2 7.3 7.4 6.9 6.3 7.0
result=t.test(processed,not_processed)
result
##
## Welch Two Sample t-test
##
## data: processed and not_processed
## t = -20.644, df = 293.94, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.205496 -1.821582
## sample estimates:
## mean of x mean of y
## 4.504762 6.518301
if(result$p.value<0.05){print("There is a significant difference")
}else{print("There is no significant difference")}
## [1] "There is a significant difference"
#2
Urban=ab$Processing_Time_Days[ab$Branch_Type=="Urban"]
Urban
## [1] 7.2 3.6 6.5 4.0 7.5 6.9 7.7 5.7 6.6 6.2 6.8 7.5 3.5 6.2 6.7 5.4 2.9 3.9
## [19] 4.4 5.6 5.0 5.9 5.9 7.4 3.8 3.2 5.0 6.3 4.7 7.2 4.7 6.6 2.8 3.6 3.0 5.5
## [37] 6.3 4.5 5.3 6.5 6.4 4.2 6.2 4.3 6.5 7.7 4.9 5.7 6.0 6.4 7.1 6.9 3.6 7.2
## [55] 7.5 7.1 5.4 3.7 6.8 5.5 7.5 4.0 7.3 7.0 4.0 4.5 6.4 5.2 7.4 7.4 3.5 2.8
## [73] 7.7 7.9 5.1 4.7 5.0 5.9 6.4 5.7 7.0 3.8 4.5 7.1 7.2 5.1 4.3 4.4 5.7 6.4
## [91] 5.8 5.8 6.5 4.9 5.2 5.7 6.2 5.7 6.1 4.6 3.8 6.2 5.7 6.5 2.7 7.8 3.3 5.1
## [109] 6.3 5.9 5.7 6.4 5.5 6.5 7.1 3.8 4.6 8.6 6.9 5.1 4.9 4.0 8.0 5.8 5.8 5.3
## [127] 4.4 4.8 6.7 6.6 4.5 2.2 4.8 4.6 6.1 5.2 4.6 7.3 4.8 7.2 6.6 4.2 6.2 6.9
## [145] 5.8 4.2 3.5 5.2 6.3 4.4 7.2 6.9 7.3 7.4 5.2 4.7 6.3 6.1
Rural=ab$Processing_Time_Days[ab$Branch_Type=="Rural"]
Rural
## [1] 3.9 6.5 5.5 2.6 6.2 6.6 6.2 4.7 6.3 7.1 8.2 6.2 6.6 6.5 5.0 6.1 9.4 6.8
## [19] 5.0 6.9 5.8 4.8 4.5 5.2 3.5 5.6 6.8 4.0 4.8 8.5 5.4 4.6 4.9 5.6 5.7 4.4
## [37] 7.2 6.2 6.9 2.9 4.2 4.6 4.9 3.7 5.8 4.7 4.0 5.6 5.3 7.5 4.2 4.4 5.3 4.0
## [55] 5.4 6.0 4.6 4.5 5.4 7.1 7.3 6.1 6.5 4.3 5.4 7.5 6.9 5.3 6.6 6.7 3.7 4.9
## [73] 5.5 6.1 4.1 6.6 3.8 3.3 7.3 5.3 6.8 2.1 6.5 4.9 4.2 5.7 4.1 5.6 6.0 4.3
## [91] 7.4 4.0 4.0 5.0 8.0 5.7 7.2 4.2 5.2 5.1 5.3 6.4 4.2 6.3 4.3 5.5 5.8 5.7
## [109] 4.8 6.6 4.9 5.4 3.8 6.6 3.2 6.5 7.0 5.8 4.4 5.9 6.4 4.0 6.5 4.4 4.1 5.1
## [127] 4.0 5.4 4.5 4.4 5.2 4.4 6.3 6.4 3.6 3.9 6.2 4.5 6.9 3.6 4.3 7.0
result=t.test(Urban,Rural)
result
##
## Welch Two Sample t-test
##
## data: Urban and Rural
## t = 1.4149, df = 297.23, p-value = 0.1581
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08361423 0.51140884
## sample estimates:
## mean of x mean of y
## 5.632911 5.419014
if(result$p.value<0.05){print("There is a significant difference")
}else{print("There is no significant difference")}
## [1] "There is no significant difference"