STAT 50 MIDTERM EXAM
JORIELYN S. MANLA
2023-03-23
Question 1.
Q1<-Jorielyn%>%
filter(AveFK != "NA")%>%
mutate(AveFKFrequency = ifelse(AveFK<=1.49, "Very Poor",
ifelse(AveFK<=2.49, "Below Average",
ifelse(AveFK<=3.49, "Average",
ifelse(AveFK<=4.49, "Above Average", "Excellent")))))%>%
group_by(School,AveFKFrequency)%>%
summarise(Frequency=n())%>%
mutate(Percentage =round(Frequency/sum(Frequency)*100, 2))`summarise()` has grouped output by 'School'. You can override using the
`.groups` argument.Q1# A tibble: 7 × 4
# Groups: School [2]
School AveFKFrequency Frequency Percentage
<chr> <chr> <int> <dbl>
1 VCCS Above Average 41 66.1
2 VCCS Average 7 11.3
3 VCCS Excellent 14 22.6
4 VNHS Above Average 68 70.8
5 VNHS Average 7 7.29
6 VNHS Below Average 2 2.08
7 VNHS Excellent 19 19.8 Question 2.
Q2<-Jorielyn%>%
filter(AveSC != "NA")%>%
mutate(AveSCFrequency = ifelse(AveSC<=1.49, "Very Poor",
ifelse(AveSC<=2.49, "Below Average",
ifelse(AveSC<=3.49, "Average",
ifelse(AveSC<=4.49, "Above Average", "Excellent")))))%>%
group_by(School,AveSCFrequency)%>%
summarise(Frequency=n())%>%
mutate(Percentage =round(Frequency/sum(Frequency)*100, 2))`summarise()` has grouped output by 'School'. You can override using the
`.groups` argument.Q2# A tibble: 8 × 4
# Groups: School [2]
School AveSCFrequency Frequency Percentage
<chr> <chr> <int> <dbl>
1 VCCS Above Average 38 61.3
2 VCCS Average 14 22.6
3 VCCS Below Average 1 1.61
4 VCCS Excellent 9 14.5
5 VNHS Above Average 58 60.4
6 VNHS Average 24 25
7 VNHS Below Average 1 1.04
8 VNHS Excellent 13 13.5 Question 3.
Q3<-Jorielyn%>%
filter(AveFK != "NA")%>%
mutate(AvePIFrequency = ifelse(AvePI<=1.49, "Very Poor",
ifelse(AvePI<=2.49, "Below Average",
ifelse(AvePI<=3.49, "Average",
ifelse(AvePI<=4.49, "Above Average", "Excellent")))))%>%
group_by(School,AvePIFrequency)%>%
summarise(Frequency=n())%>%
mutate(Percentage =round(Frequency/sum(Frequency)*100, 2))`summarise()` has grouped output by 'School'. You can override using the
`.groups` argument.Q3# A tibble: 9 × 4
# Groups: School [2]
School AvePIFrequency Frequency Percentage
<chr> <chr> <int> <dbl>
1 VCCS Above Average 39 62.9
2 VCCS Average 15 24.2
3 VCCS Below Average 1 1.61
4 VCCS Excellent 7 11.3
5 VNHS Above Average 49 51.0
6 VNHS Average 34 35.4
7 VNHS Below Average 7 7.29
8 VNHS Excellent 5 5.21
9 VNHS Very Poor 1 1.04Question 4.
Q4<-Jorielyn%>%
filter(AveFK != "NA")%>%
mutate(AveRRFrequency = ifelse(AveRR<=1.49, "Very Poor",
ifelse(AveRR<=2.49, "Below Average",
ifelse(AveRR<=3.49, "Average",
ifelse(AveRR<=4.49, "Above Average", "Excellent")))))%>%
group_by(School,AveRRFrequency)%>%
summarise(Frequency=n())%>%
mutate(Percentage =round(Frequency/sum(Frequency)*100, 2))`summarise()` has grouped output by 'School'. You can override using the
`.groups` argument.Q4# A tibble: 7 × 4
# Groups: School [2]
School AveRRFrequency Frequency Percentage
<chr> <chr> <int> <dbl>
1 VCCS Above Average 43 69.4
2 VCCS Average 11 17.7
3 VCCS Excellent 8 12.9
4 VNHS Above Average 67 69.8
5 VNHS Average 21 21.9
6 VNHS Below Average 3 3.12
7 VNHS Excellent 5 5.21library(rstatix)
Phase <- Jorielyn%>%
gather(key ="Phase", value = "CFUcount", 'Adlay with wash', 'Adlay with milk', 'Adlay with milk and molasses')%>%
convert_as_factor(Phase)
Phase# A tibble: 192 × 11
PhaseInterval Tempe…¹ ...6 ...7 ...8 ...9 ...10 ...11 ...12 Phase CFUco…²
<chr> <chr> <lgl> <lgl> <chr> <chr> <chr> <chr> <chr> <fct> <dbl>
1 3- day 25°C NA NA <NA> <NA> <NA> <NA> <NA> Adla… 759
2 3- day 25°C NA NA Conc… dip Day Weig… Color Adla… 736
3 3- day 25°C NA NA 1 15 4 <NA> <NA> Adla… 891
4 3- day 30°C NA NA 1 15 4 <NA> <NA> Adla… 573
5 3- day 30°C NA NA 1 15 4 <NA> <NA> Adla… 584
6 3- day 30°C NA NA 1 15 4 <NA> <NA> Adla… 482
7 3- day 35°C NA NA 1 30 4 <NA> <NA> Adla… 384
8 3- day 35°C NA NA 1 30 4 <NA> <NA> Adla… 264
9 3- day 35°C NA NA 1 30 4 <NA> <NA> Adla… 208
10 5-day 25°C NA NA 1 30 4 <NA> <NA> Adla… 888
# … with 182 more rows, and abbreviated variable names ¹Temperature, ²CFUcountQuestion 5.
#Summary statistics
Phase1<-Phase%>%
group_by(PhaseInterval) %>%
get_summary_stats(CFUcount, type = "mean_sd")
Phase1# A tibble: 3 × 5
PhaseInterval variable n mean sd
<chr> <fct> <dbl> <dbl> <dbl>
1 3- day CFUcount 26 488. 264.
2 5-day CFUcount 27 284. 269.
3 7-day CFUcount 27 182. 271.Question 6.
#Summary statistics
Phase2<-Phase%>%
group_by(Temperature) %>%
get_summary_stats(CFUcount, type = "mean_sd")
Phase2# A tibble: 3 × 5
Temperature variable n mean sd
<chr> <fct> <dbl> <dbl> <dbl>
1 25°C CFUcount 27 480. 353.
2 30°C CFUcount 26 235. 194.
3 35°C CFUcount 27 229. 243.Question 7.
#Summary statistics
Phase3<-Phase%>%
group_by(`Phase`) %>%
get_summary_stats(CFUcount, type = "mean_sd")
Phase3# A tibble: 3 × 5
Phase variable n mean sd
<fct> <fct> <dbl> <dbl> <dbl>
1 Adlay with milk CFUcount 26 190 232.
2 Adlay with milk and molasses CFUcount 27 293. 261.
3 Adlay with wash CFUcount 27 459. 323.Question 8.
#Summary statistics
Phase4<-Phase%>%
group_by(PhaseInterval,Temperature) %>%
get_summary_stats(CFUcount, type = "mean_sd")
Phase4# A tibble: 9 × 6
PhaseInterval Temperature variable n mean sd
<chr> <chr> <fct> <dbl> <dbl> <dbl>
1 3- day 25°C CFUcount 9 641. 244.
2 3- day 30°C CFUcount 8 347. 187.
3 3- day 35°C CFUcount 9 459. 282.
4 5-day 25°C CFUcount 9 460. 346.
5 5-day 30°C CFUcount 9 250. 218.
6 5-day 35°C CFUcount 9 142. 98.7
7 7-day 25°C CFUcount 9 340. 416.
8 7-day 30°C CFUcount 9 120. 108.
9 7-day 35°C CFUcount 9 85.4 104. Question 9.
#Summary statistics
Phase5<-Phase%>%
group_by(Temperature, Phase) %>%
get_summary_stats(CFUcount, type = "mean_sd")
Phase5# A tibble: 9 × 6
Temperature Phase variable n mean sd
<chr> <fct> <fct> <dbl> <dbl> <dbl>
1 25°C Adlay with milk CFUcount 9 243. 230.
2 25°C Adlay with milk and molasses CFUcount 9 416. 330.
3 25°C Adlay with wash CFUcount 9 783. 267.
4 30°C Adlay with milk CFUcount 8 84 57.6
5 30°C Adlay with milk and molasses CFUcount 9 193. 98.9
6 30°C Adlay with wash CFUcount 9 410. 213.
7 35°C Adlay with milk CFUcount 9 232. 309.
8 35°C Adlay with milk and molasses CFUcount 9 269. 271.
9 35°C Adlay with wash CFUcount 9 186. 135. Question 10.
#Data1:
NumberofResponses <- Q2$Frequency
var1<-data.frame(Q2, NumberofResponses)
var1 School AveSCFrequency Frequency Percentage NumberofResponses
1 VCCS Above Average 38 61.29 38
2 VCCS Average 14 22.58 14
3 VCCS Below Average 1 1.61 1
4 VCCS Excellent 9 14.52 9
5 VNHS Above Average 58 60.42 58
6 VNHS Average 24 25.00 24
7 VNHS Below Average 1 1.04 1
8 VNHS Excellent 13 13.54 13library(ggplot2)
ggplot(var1, aes(x= AveSCFrequency, y= NumberofResponses)) +
geom_bar(stat="identity")
#Data 2:
A<- Jorielyn%>%
filter(Temperature== "25°C")
B<- Jorielyn%>%
filter(Temperature == "30°C")
C<- Jorielyn%>%
filter(Temperature == "35°C")D<-nrow(A)
E<-nrow(B)
G<-nrow(C)x<- c(D,E,G)
labels <- c("25°C","30°C","35°C")
pie(x, main = "Temperature",col = cm.colors(length(x)))
legend("topright", c("25°C","30°C","35°C"), cex = 0.8,
fill = cm.colors(length(x)))