• Data
    • dat
  • Analysis 1: A1
    • Stored Results
  • Analysis 2: A2
    • Stored Results

Data

dat

  • id (string)
  • pet (string)
    • Levels
      • dog
      • cat
    • Ordered: FALSE
  • time (string)
    • Levels
      • morning
      • night
    • Ordered: FALSE
  • y (float): Happiness Score
dat <- read.csv('data/dat_data.tsv', sep='\t')

Analysis 1: A1

scienceverse::t_test(y ~ pet, dat)
#> 
#>  Welch Two Sample t-test
#> 
#> data:  y by pet
#> t = -0.3, df = 117, p-value = 0.7
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#>  -2.19  1.57
#> sample estimates:
#> mean in group cat mean in group dog 
#>              8.62              8.93

Stored Results

  • statistic: 0.3234
  • parameter: 117.3873
  • p.value: 0.747
  • conf.int:
    1. -1.5737
    2. 2.1881
  • estimate:
    1. 8.9267
    2. 8.6195
  • null.value: 0
  • stderr: 0.9498
  • alternative: two.sided
  • method: Welch Two Sample t-test
  • data.name: y by pet
  • n:
    1. 60
    2. 60

Analysis 2: A2

scienceverse::t_test(y ~ time, dat)
#> 
#>  Welch Two Sample t-test
#> 
#> data:  y by time
#> t = -0.5, df = 118, p-value = 0.6
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#>  -2.37  1.39
#> sample estimates:
#> mean in group morning   mean in group night 
#>                  8.53                  9.02

Stored Results

  • statistic: -0.5117
  • parameter: 117.5653
  • p.value: 0.6098
  • conf.int:
    1. -2.3653
    2. 1.3939
  • estimate:
    1. 8.5302
    2. 9.0159
  • null.value: 0
  • stderr: 0.9491
  • alternative: two.sided
  • method: Welch Two Sample t-test
  • data.name: y by time
  • n:
    1. 60
    2. 60