library(readxl)
X4_CerinaData <- read_excel("C:/1. School/School/4th Year/Experimental Design/Assignment/4. CerinaData.xlsx")
X4_CerinaData## # A tibble: 33 x 29
## NO. Sex CS Age Enrolled Grade `Describing Sets` `Finite/Infinite Se~
## <dbl> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 1 F S 15 yes 9 No idea No idea
## 2 2 F S 14 yes 8 No idea No idea
## 3 3 F S 14 yes 8 No idea No idea
## 4 4 F S 17 yes 9 No idea Very low
## 5 5 F S 14 yes 9 No idea Very low
## 6 6 F S 13 yes 8 Low Low
## 7 7 M S 17 yes 11 No idea No idea
## 8 8 F S 14 yes 8 Very low Very low
## 9 9 F S 14 yes 9 No idea No idea
## 10 10 F S 16 yes 10 No idea Very low
## # ... with 23 more rows, and 21 more variables: Equality of Sets <chr>,
## # Subset <chr>, Power Set <chr>, Equi Sets <chr>, Set Operations <chr>,
## # Venn Diagram <chr>, Real No System <chr>, Addn of AE <chr>,
## # Subn of AE <chr>, Laws of Exp <chr>, Multn of AE <chr>,
## # Rem'l of Grp Sym <chr>, Div'n of AE <chr>, Synthetic Div'n <chr>,
## # Pre-test Score <dbl>, Post-test Score <dbl>,
## # Relevance of the topics discussed <chr>, ...library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondevy<- X4_CerinaData %>%
na.omit()
devy## # A tibble: 23 x 29
## NO. Sex CS Age Enrolled Grade `Describing Sets` `Finite/Infinite Se~
## <dbl> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 2 F S 14 yes 8 No idea No idea
## 2 5 F S 14 yes 9 No idea Very low
## 3 6 F S 13 yes 8 Low Low
## 4 7 M S 17 yes 11 No idea No idea
## 5 8 F S 14 yes 8 Very low Very low
## 6 9 F S 14 yes 9 No idea No idea
## 7 10 F S 16 yes 10 No idea Very low
## 8 11 F S 14 yes 8 No idea No idea
## 9 12 M S 15 yes 9 No idea No idea
## 10 13 F S 14 No 8 No idea No idea
## # ... with 13 more rows, and 21 more variables: Equality of Sets <chr>,
## # Subset <chr>, Power Set <chr>, Equi Sets <chr>, Set Operations <chr>,
## # Venn Diagram <chr>, Real No System <chr>, Addn of AE <chr>,
## # Subn of AE <chr>, Laws of Exp <chr>, Multn of AE <chr>,
## # Rem'l of Grp Sym <chr>, Div'n of AE <chr>, Synthetic Div'n <chr>,
## # Pre-test Score <dbl>, Post-test Score <dbl>,
## # Relevance of the topics discussed <chr>, ...dev <- select(devy, `Pre-test Score`, `Post-test Score`)
dev## # A tibble: 23 x 2
## `Pre-test Score` `Post-test Score`
## <dbl> <dbl>
## 1 10 9
## 2 8 14
## 3 12 10
## 4 13 11
## 5 7 10
## 6 10 7
## 7 9 8
## 8 7 9
## 9 13 11
## 10 8 11
## # ... with 13 more rowsdev## # A tibble: 23 x 2
## `Pre-test Score` `Post-test Score`
## <dbl> <dbl>
## 1 10 9
## 2 8 14
## 3 12 10
## 4 13 11
## 5 7 10
## 6 10 7
## 7 9 8
## 8 7 9
## 9 13 11
## 10 8 11
## # ... with 13 more rowsmean(dev$`Pre-test Score`)## [1] 9.086957mean(dev$`Post-test Score`)## [1] 10.04348sd(dev$`Pre-test Score`)## [1] 2.466375sd(dev$`Post-test Score`)## [1] 1.8703t.test(dev$`Post-test Score`, dev$`Pre-test Score`,alternative = "two.sided", var.equal=TRUE, paired=TRUE)##
## Paired t-test
##
## data: dev$`Post-test Score` and dev$`Pre-test Score`
## t = 1.6361, df = 22, p-value = 0.1161
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2559605 2.1690040
## sample estimates:
## mean of the differences
## 0.9565217Conclusion: There is no significant difference between the Pre-test Score and Post-test score since the p-value is 0.1161.