temp_copied

library(infer)  #<<

library(infer)

gss |> tibble()   #<<

# A tibble: 500 × 11
    year   age sex    college   partyid hompop hours income class finrela weight
   <dbl> <dbl> <fct>  <fct>     <fct>    <dbl> <dbl> <ord>  <fct> <fct>    <dbl>
 1  2014    36 male   degree    ind          3    50 $2500… midd… below …  0.896
 2  1994    34 female no degree rep          4    31 $2000… work… below …  1.08 
 3  1998    24 male   degree    ind          1    40 $2500… work… below …  0.550
 4  1996    42 male   no degree ind          4    40 $2500… work… above …  1.09 
 5  1994    31 male   degree    rep          2    40 $2500… midd… above …  1.08 
 6  1996    32 female no degree rep          4    53 $2500… midd… average  1.09 
 7  1990    48 female no degree dem          2    32 $2500… work… below …  1.06 
 8  2016    36 female degree    ind          1    20 $2500… midd… above …  0.478
 9  2000    30 female degree    rep          5    40 $2500… midd… average  1.10 
10  1998    33 female no degree dem          2    40 $1500… work… far be…  0.550
# ℹ 490 more rows

library(infer)

gss |> tibble() |>
  specify(age ~ partyid)   #<<

Response: age (numeric)
Explanatory: partyid (factor)
# A tibble: 500 × 2
     age partyid
   <dbl> <fct>  
 1    36 ind    
 2    34 rep    
 3    24 ind    
 4    42 ind    
 5    31 rep    
 6    32 rep    
 7    48 dem    
 8    36 ind    
 9    30 rep    
10    33 dem    
# ℹ 490 more rows

library(infer)

gss |> tibble() |>
  specify(age ~ partyid) |>
  calculate(stat = "F")   #<<

Response: age (numeric)
Explanatory: partyid (factor)
# A tibble: 1 × 1
   stat
  <dbl>
1  2.48

library(infer)

gss |> tibble() |>
  specify(age ~ partyid) |>
  calculate(stat = "F") ->
F_hat  #<<

library(infer)

gss |> tibble() |>
  specify(age ~ partyid) |>
  calculate(stat = "F") ->
F_hat

gss   #<<

# A tibble: 500 × 11
    year   age sex    college   partyid hompop hours income class finrela weight
   <dbl> <dbl> <fct>  <fct>     <fct>    <dbl> <dbl> <ord>  <fct> <fct>    <dbl>
 1  2014    36 male   degree    ind          3    50 $2500… midd… below …  0.896
 2  1994    34 female no degree rep          4    31 $2000… work… below …  1.08 
 3  1998    24 male   degree    ind          1    40 $2500… work… below …  0.550
 4  1996    42 male   no degree ind          4    40 $2500… work… above …  1.09 
 5  1994    31 male   degree    rep          2    40 $2500… midd… above …  1.08 
 6  1996    32 female no degree rep          4    53 $2500… midd… average  1.09 
 7  1990    48 female no degree dem          2    32 $2500… work… below …  1.06 
 8  2016    36 female degree    ind          1    20 $2500… midd… above …  0.478
 9  2000    30 female degree    rep          5    40 $2500… midd… average  1.10 
10  1998    33 female no degree dem          2    40 $1500… work… far be…  0.550
# ℹ 490 more rows

library(infer)

gss |> tibble() |>
  specify(age ~ partyid) |>
  calculate(stat = "F") ->
F_hat

gss |>
  specify(age ~ partyid)   #<<

Response: age (numeric)
Explanatory: partyid (factor)
# A tibble: 500 × 2
     age partyid
   <dbl> <fct>  
 1    36 ind    
 2    34 rep    
 3    24 ind    
 4    42 ind    
 5    31 rep    
 6    32 rep    
 7    48 dem    
 8    36 ind    
 9    30 rep    
10    33 dem    
# ℹ 490 more rows

library(infer)

gss |> tibble() |>
  specify(age ~ partyid) |>
  calculate(stat = "F") ->
F_hat

gss |>
  specify(age ~ partyid) |>
  hypothesize(null = "independence")   #<<

Response: age (numeric)
Explanatory: partyid (factor)
Null Hypothesis: indepe...
# A tibble: 500 × 2
     age partyid
   <dbl> <fct>  
 1    36 ind    
 2    34 rep    
 3    24 ind    
 4    42 ind    
 5    31 rep    
 6    32 rep    
 7    48 dem    
 8    36 ind    
 9    30 rep    
10    33 dem    
# ℹ 490 more rows

library(infer)

gss |> tibble() |>
  specify(age ~ partyid) |>
  calculate(stat = "F") ->
F_hat

gss |>
  specify(age ~ partyid) |>
  hypothesize(null = "independence") |>
  generate(reps = 1000,  #<<
           type = "permute")   #<<

Response: age (numeric)
Explanatory: partyid (factor)
Null Hypothesis: indepe...
# A tibble: 500,000 × 3
# Groups:   replicate [1,000]
     age partyid replicate
   <dbl> <fct>       <int>
 1    33 ind             1
 2    30 rep             1
 3    60 ind             1
 4    29 ind             1
 5    22 rep             1
 6    34 rep             1
 7    47 dem             1
 8    61 ind             1
 9    37 rep             1
10    66 dem             1
# ℹ 499,990 more rows

library(infer)

gss |> tibble() |>
  specify(age ~ partyid) |>
  calculate(stat = "F") ->
F_hat

gss |>
  specify(age ~ partyid) |>
  hypothesize(null = "independence") |>
  generate(reps = 1000,
           type = "permute") |>
  calculate(stat = "F")   #<<

Response: age (numeric)
Explanatory: partyid (factor)
Null Hypothesis: indepe...
# A tibble: 1,000 × 2
   replicate  stat
       <int> <dbl>
 1         1 4.60 
 2         2 0.352
 3         3 0.260
 4         4 1.26 
 5         5 0.291
 6         6 0.243
 7         7 0.866
 8         8 0.665
 9         9 0.147
10        10 1.74 
# ℹ 990 more rows

library(infer)

gss |> tibble() |>
  specify(age ~ partyid) |>
  calculate(stat = "F") ->
F_hat

gss |>
  specify(age ~ partyid) |>
  hypothesize(null = "independence") |>
  generate(reps = 1000,
           type = "permute") |>
  calculate(stat = "F") |>
  visualize()   #<<

library(infer)

gss |> tibble() |>
  specify(age ~ partyid) |>
  calculate(stat = "F") ->
F_hat

gss |>
  specify(age ~ partyid) |>
  hypothesize(null = "independence") |>
  generate(reps = 1000,
           type = "permute") |>
  calculate(stat = "F") |>
  visualize() +
  shade_p_value(obs_stat = F_hat,  #<<
                direction = "greater")  #<<