lab 11

In analysis one, the data wasnt signficiant between precint 3 and 18 because it was above 0.05.But for precints 8 and 18 and 8 and 3, they differed between because it was 0.00.

if (!require("tidyverse"))
  install.packages("tidyverse")

## Loading required package: tidyverse

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tidyverse)

mydata <- read.csv("https://raw.githubusercontent.com/drkblake/Data/main/Weekly18and23.csv")

mydata$V1 <- mydata$Minutes_2018
mydata$V2 <- mydata$Minutes_2023

mydata$PairDifferences <- mydata$V2 - mydata$V1

mydata %>%
  select(PairDifferences) %>%
  summarise(
    count = n(),
    mean = mean(PairDifferences, na.rm = TRUE),
    sd = sd(PairDifferences, na.rm = TRUE),
    min = min(PairDifferences, na.rm = TRUE),
    max = max(PairDifferences, na.rm = TRUE))

##   count mean       sd   min  max
## 1    52 4.75 9.017206 -15.3 26.8

mydata %>%
  select(V1, V2) %>%
  summarise_all(list(Mean = mean, SD = sd))

##    V1_Mean  V2_Mean    V1_SD    V2_SD
## 1 8.763462 13.51346 5.951867 5.998203

options(scipen = 999)
t.test(mydata$V2, mydata$V1,
       paired = TRUE)

## 
##  Paired t-test
## 
## data:  mydata$V2 and mydata$V1
## t = 3.7986, df = 51, p-value = 0.0003888
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  2.239594 7.260406
## sample estimates:
## mean difference 
##            4.75

For analysis two, the data sets differed significantly. The p-value was 0.0007732. The comparison between them was a big difference.

mydata <- read.csv("https://raw.githubusercontent.com/drkblake/Data/main/RichPoor.csv")

if (!require("tidyverse"))
  install.packages("tidyverse")
library(tidyverse)

mydata$DV <- mydata$Minutes
mydata$IV <- mydata$Precinct

group_by(mydata, IV) %>%
  summarise(
    count = n(),
    mean = mean(DV, na.rm = TRUE),
    sd = sd(DV, na.rm = TRUE),
    min = min(DV, na.rm = TRUE),
    max = max(DV, na.rm = TRUE))

## # A tibble: 2 × 6
##   IV          count  mean    sd   min   max
##   <chr>       <int> <dbl> <dbl> <dbl> <dbl>
## 1 Precinct 12   159 14.3   7.05  -4.6  34.6
## 2 Precinct 8    132  9.15  4.48  -2.9  18.3

options(scipen = 999)
t.test(mydata$DV ~ mydata$IV,
       var.equal = FALSE)

## 
##  Welch Two Sample t-test
## 
## data:  mydata$DV by mydata$IV
## t = 7.5258, df = 271.55, p-value = 0.0000000000007732
## alternative hypothesis: true difference in means between group Precinct 12 and group Precinct 8 is not equal to 0
## 95 percent confidence interval:
##  3.787442 6.471049
## sample estimates:
## mean in group Precinct 12  mean in group Precinct 8 
##                  14.27925                   9.15000

For analysis three, 2018 and 2023 differed because the p-value is 0.00338. The response times in 2023 were larger.

if (!require("tidyverse"))
  install.packages("tidyverse")
library(tidyverse)

mydata <- read.csv("https://raw.githubusercontent.com/drkblake/Data/main/Weekly18and23.csv")

mydata$V1 <- mydata$Minutes_2018
mydata$V2 <- mydata$Minutes_2023

mydata$PairDifferences <- mydata$V2 - mydata$V1

mydata %>%
  select(PairDifferences) %>%
  summarise(
    count = n(),
    mean = mean(PairDifferences, na.rm = TRUE),
    sd = sd(PairDifferences, na.rm = TRUE),
    min = min(PairDifferences, na.rm = TRUE),
    max = max(PairDifferences, na.rm = TRUE))

##   count mean       sd   min  max
## 1    52 4.75 9.017206 -15.3 26.8

mydata %>%
  select(V1, V2) %>%
  summarise_all(list(Mean = mean, SD = sd))

##    V1_Mean  V2_Mean    V1_SD    V2_SD
## 1 8.763462 13.51346 5.951867 5.998203

options(scipen = 999)
t.test(mydata$V2, mydata$V1,
       paired = TRUE)

## 
##  Paired t-test
## 
## data:  mydata$V2 and mydata$V1
## t = 3.7986, df = 51, p-value = 0.0003888
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  2.239594 7.260406
## sample estimates:
## mean difference 
##            4.75