os_vartest performs tests on the equality of standard deviations (variances).It tests that the standard deviation of a sample is equal to a hypothesized value.

os_vartest(x, sd, confint = 0.95, alternative = c("both", "less", "greater",
  "all"), ...)

Arguments

x
a numeric vector
sd
hypothesised standard deviation
confint
confidence level
alternative
a character string specifying the alternative hypothesis, must be one of "both" (default), "greater", "less" or "all". You can specify just the initial letter
...
additional arguments passed to or from other methods

Value

os_vartest returns an object of class "os_vartest". An object of class "os_vartest" is a list containing the following components:

References

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

See also

var.test

Examples

# lower tail os_vartest(mtcars$mpg, 5, alternative = 'less')
#> One-Sample Statistics #> ----------------------------------------------------------------------------- #> Variable Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] #> ----------------------------------------------------------------------------- #> mpg 32 20.0906 1.0654 6.0269 3.8737 10.6526 #> ----------------------------------------------------------------------------- #> #> Lower Tail Test #> --------------- #> Ho: sd(mpg) >= 5 #> Ha: sd(mpg) < 5 #> #> Chi-Square Test for Variance #> ------------------------------------- #> Variable c DF Sig #> ------------------------------------- #> mpg 45.041 31 0.9506 #> -------------------------------------
# upper tail os_vartest(mtcars$mpg, 5, alternative = 'greater')
#> One-Sample Statistics #> ----------------------------------------------------------------------------- #> Variable Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] #> ----------------------------------------------------------------------------- #> mpg 32 20.0906 1.0654 6.0269 3.8737 10.6526 #> ----------------------------------------------------------------------------- #> #> Upper Tail Test #> --------------- #> Ho: sd(mpg) <= 5 #> Ha: sd(mpg) > 5 #> #> Chi-Square Test for Variance #> ------------------------------------- #> Variable c DF Sig #> ------------------------------------- #> mpg 45.041 31 0.0494 #> -------------------------------------
# both tails os_vartest(mtcars$mpg, 5, alternative = 'both')
#> One-Sample Statistics #> ----------------------------------------------------------------------------- #> Variable Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] #> ----------------------------------------------------------------------------- #> mpg 32 20.0906 1.0654 6.0269 3.8737 10.6526 #> ----------------------------------------------------------------------------- #> #> Two Tail Test #> --------------- #> Ho: sd(mpg) = 5 #> Ha: sd(mpg) != 5 #> #> Chi-Square Test for Variance #> ------------------------------------- #> Variable c DF Sig #> ------------------------------------- #> mpg 45.041 31 0.0989 #> -------------------------------------
# all tails os_vartest(mtcars$mpg, 5, alternative = 'all')
#> One-Sample Statistics #> ----------------------------------------------------------------------------- #> Variable Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] #> ----------------------------------------------------------------------------- #> mpg 32 20.0906 1.0654 6.0269 3.8737 10.6526 #> ----------------------------------------------------------------------------- #> #> Ho: sd(mpg) = 5 #> #> Ha: sd < 5 Ha: sd != 5 Ha: sd > 5 #> c = 45.0412 c = 45.0412 c = 45.0412 #> Pr(C < c) = 0.9506 2 * Pr(C > c) = 0.0989 Pr(C > c) = 0.0494