One numerical variable (standardized mean \(t\))
@@ -228,7 +228,7 @@ One numerical variable (stan
## # A tibble: 1 × 1
## p_value
## <dbl>
-## 1 0.038
+## 1 0.032
Alternatively, using the t_test wrapper:
gss %>%
@@ -274,7 +274,7 @@
+
## 1 0.012
-
## tibble [500 × 11] (S3: tbl_df/tbl/data.frame)
-## $ year : num [1:500] 2014 1994 1998 1996 1994 ...
-## $ age : num [1:500] 36 34 24 42 31 32 48 36 30 33 ...
-## $ sex : Factor w/ 2 levels "male","female": 1 2 1 1 1 2 2 2 2 2 ...
-## $ college: Factor w/ 2 levels "no degree","degree": 2 1 2 1 2 1 1 2 2 1 ...
-## $ partyid: Factor w/ 5 levels "dem","ind","rep",..: 2 3 2 2 3 3 1 2 3 1 ...
-## $ hompop : num [1:500] 3 4 1 4 2 4 2 1 5 2 ...
-## $ hours : num [1:500] 50 31 40 40 40 53 32 20 40 40 ...
-## $ income : Ord.factor w/ 12 levels "lt $1000"<"$1000 to 2999"<..: 12 11 12 12 12 12 12 12 12 10 ...
-## $ class : Factor w/ 6 levels "lower class",..: 3 2 2 2 3 3 2 3 3 2 ...
-## $ finrela: Factor w/ 6 levels "far below average",..: 2 2 2 4 4 3 2 4 3 1 ...
-## $ weight : num [1:500] 0.896 1.083 0.55 1.086 1.083 ...
+
## tibble [500 × 11] (S3: tbl_df/tbl/data.frame)
+## $ year : num [1:500] 2014 1994 1998 1996 1994 ...
+## $ age : num [1:500] 36 34 24 42 31 32 48 36 30 33 ...
+## $ sex : Factor w/ 2 levels "male","female": 1 2 1 1 1 2 2 2 2 2 ...
+## $ college: Factor w/ 2 levels "no degree","degree": 2 1 2 1 2 1 1 2 2 1 ...
+## $ partyid: Factor w/ 5 levels "dem","ind","rep",..: 2 3 2 2 3 3 1 2 3 1 ...
+## $ hompop : num [1:500] 3 4 1 4 2 4 2 1 5 2 ...
+## $ hours : num [1:500] 50 31 40 40 40 53 32 20 40 40 ...
+## $ income : Ord.factor w/ 12 levels "lt $1000"<"$1000 to 2999"<..: 12 11 12 12 12 12 12 12 12 10 ...
+## $ class : Factor w/ 6 levels "lower class",..: 3 2 2 2 3 3 2 3 3 2 ...
+## $ finrela: Factor w/ 6 levels "far below average",..: 2 2 2 4 4 3 2 4 3 1 ...
+## $ weight : num [1:500] 0.896 1.083 0.55 1.086 1.083 ...
As an example, we’ll run an analysis of variance on age and partyid, testing whether the age of a respondent is independent of their political party affiliation.
Calculating the observed statistic,
-
## # A tibble: 1 × 1
-## p_value
-## <dbl>
-## 1 0.06
+
## # A tibble: 1 × 1
+## p_value
+## <dbl>
+## 1 0.06
Note that the formula and non-formula interfaces (i.e. age ~ partyid vs. response = age, explanatory = partyid) work for all implemented inference procedures in infer. Use whatever is more natural for you. If you will be doing modeling using functions like lm() and glm(), though, we recommend you begin to use the formula y ~ x notation as soon as possible.
Other resources are available in the package vignettes! See vignette("observed_stat_examples") for more examples like the one above, and vignette("infer") for discussion of the underlying principles of the package design.
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index 3a60dee3..e67091aa 100644
--- a/news/index.html
+++ b/news/index.html
@@ -1,5 +1,5 @@
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