## ----------------------------------------------------------------------------- knitr::opts_chunk$set(fig.width = 6, fig.height = 4.5, message = FALSE, warning = FALSE) options(digits = 4) ## ----------------------------------------------------------------------------- library(dplyr) library(infer) ## ----------------------------------------------------------------------------- # load in the dataset data(gss) # take a look at its structure dplyr::glimpse(gss) ## ----------------------------------------------------------------------------- x_bar <- gss |> specify(response = hours) |> calculate(stat = "mean") ## ----------------------------------------------------------------------------- x_bar <- gss |> observe(response = hours, stat = "mean") ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(response = hours) |> hypothesize(null = "point", mu = 40) |> generate(reps = 1000) |> calculate(stat = "mean") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = x_bar, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = x_bar, direction = "two-sided") ## ----------------------------------------------------------------------------- t_bar <- gss |> specify(response = hours) |> hypothesize(null = "point", mu = 40) |> calculate(stat = "t") ## ----------------------------------------------------------------------------- t_bar <- gss |> observe(response = hours, null = "point", mu = 40, stat = "t") ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(response = hours) |> hypothesize(null = "point", mu = 40) |> generate(reps = 1000) |> calculate(stat = "t") ## ----------------------------------------------------------------------------- null_dist_theory <- gss |> specify(response = hours) |> assume("t") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = t_bar, direction = "two-sided") ## ----------------------------------------------------------------------------- visualize(null_dist_theory) + shade_p_value(obs_stat = t_bar, direction = "two-sided") ## ----------------------------------------------------------------------------- visualize(null_dist, method = "both") + shade_p_value(obs_stat = t_bar, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = t_bar, direction = "two-sided") ## ----------------------------------------------------------------------------- gss |> t_test(response = hours, mu = 40) ## ----------------------------------------------------------------------------- x_tilde <- gss |> specify(response = age) |> calculate(stat = "median") ## ----------------------------------------------------------------------------- x_tilde <- gss |> observe(response = age, stat = "median") ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(response = age) |> hypothesize(null = "point", med = 40) |> generate(reps = 1000) |> calculate(stat = "median") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = x_tilde, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = x_tilde, direction = "two-sided") ## ----------------------------------------------------------------------------- set.seed(1) gss_paired <- gss |> mutate( hours_previous = hours + 5 - rpois(nrow(gss), 4.8), diff = hours - hours_previous ) gss_paired |> select(hours, hours_previous, diff) ## ----------------------------------------------------------------------------- x_tilde <- gss_paired |> specify(response = diff) |> calculate(stat = "mean") ## ----------------------------------------------------------------------------- x_tilde <- gss_paired |> observe(response = diff, stat = "mean") ## ----------------------------------------------------------------------------- null_dist <- gss_paired |> specify(response = diff) |> hypothesize(null = "paired independence") |> generate(reps = 1000, type = "permute") |> calculate(stat = "mean") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = x_tilde, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = x_tilde, direction = "two-sided") ## ----------------------------------------------------------------------------- p_hat <- gss |> specify(response = sex, success = "female") |> calculate(stat = "prop") ## ----------------------------------------------------------------------------- p_hat <- gss |> observe(response = sex, success = "female", stat = "prop") ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(response = sex, success = "female") |> hypothesize(null = "point", p = .5) |> generate(reps = 1000) |> calculate(stat = "prop") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = p_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = p_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist <- gss |> dplyr::mutate(is_female = (sex == "female")) |> specify(response = is_female, success = "TRUE") |> hypothesize(null = "point", p = .5) |> generate(reps = 1000) |> calculate(stat = "prop") ## ----------------------------------------------------------------------------- p_hat <- gss |> specify(response = sex, success = "female") |> hypothesize(null = "point", p = .5) |> calculate(stat = "z") ## ----------------------------------------------------------------------------- p_hat <- gss |> observe(response = sex, success = "female", null = "point", p = .5, stat = "z") ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(response = sex, success = "female") |> hypothesize(null = "point", p = .5) |> generate(reps = 1000, type = "draw") |> calculate(stat = "z") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = p_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = p_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- prop_test(gss, college ~ NULL, p = .2) ## ----------------------------------------------------------------------------- d_hat <- gss |> specify(college ~ sex, success = "no degree") |> calculate(stat = "diff in props", order = c("female", "male")) ## ----------------------------------------------------------------------------- d_hat <- gss |> observe( college ~ sex, success = "no degree", stat = "diff in props", order = c("female", "male") ) ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(college ~ sex, success = "no degree") |> hypothesize(null = "independence") |> generate(reps = 1000) |> calculate(stat = "diff in props", order = c("female", "male")) ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = d_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = d_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- r_hat <- gss |> specify(college ~ sex, success = "no degree") |> calculate(stat = "ratio of props", order = c("female", "male")) ## ----------------------------------------------------------------------------- r_hat <- gss |> observe(college ~ sex, success = "no degree", stat = "ratio of props", order = c("female", "male")) ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(college ~ sex, success = "no degree") |> hypothesize(null = "independence") |> generate(reps = 1000) |> calculate(stat = "ratio of props", order = c("female", "male")) ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = r_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = r_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- or_hat <- gss |> specify(college ~ sex, success = "no degree") |> calculate(stat = "odds ratio", order = c("female", "male")) ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(college ~ sex, success = "no degree") |> hypothesize(null = "independence") |> generate(reps = 1000) |> calculate(stat = "odds ratio", order = c("female", "male")) ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = or_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = or_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- z_hat <- gss |> specify(college ~ sex, success = "no degree") |> hypothesize(null = "independence") |> calculate(stat = "z", order = c("female", "male")) ## ----------------------------------------------------------------------------- z_hat <- gss |> observe(college ~ sex, success = "no degree", stat = "z", order = c("female", "male")) ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(college ~ sex, success = "no degree") |> hypothesize(null = "independence") |> generate(reps = 1000) |> calculate(stat = "z", order = c("female", "male")) ## ----------------------------------------------------------------------------- null_dist_theory <- gss |> specify(college ~ sex, success = "no degree") |> assume("z") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = z_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- visualize(null_dist_theory) + shade_p_value(obs_stat = z_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- visualize(null_dist, method = "both") + shade_p_value(obs_stat = z_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = z_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- prop_test(gss, college ~ sex, order = c("female", "male")) ## ----------------------------------------------------------------------------- Chisq_hat <- gss |> specify(response = finrela) |> hypothesize( null = "point", p = c( "far below average" = 1 / 6, "below average" = 1 / 6, "average" = 1 / 6, "above average" = 1 / 6, "far above average" = 1 / 6, "DK" = 1 / 6 ) ) |> calculate(stat = "Chisq") ## ----------------------------------------------------------------------------- Chisq_hat <- gss |> observe( response = finrela, null = "point", p = c( "far below average" = 1 / 6, "below average" = 1 / 6, "average" = 1 / 6, "above average" = 1 / 6, "far above average" = 1 / 6, "DK" = 1 / 6 ), stat = "Chisq" ) ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(response = finrela) |> hypothesize( null = "point", p = c( "far below average" = 1 / 6, "below average" = 1 / 6, "average" = 1 / 6, "above average" = 1 / 6, "far above average" = 1 / 6, "DK" = 1 / 6 ) ) |> generate(reps = 1000, type = "draw") |> calculate(stat = "Chisq") ## ----------------------------------------------------------------------------- null_dist_theory <- gss |> specify(response = finrela) |> assume("Chisq") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = Chisq_hat, direction = "greater") ## ----------------------------------------------------------------------------- visualize(null_dist_theory) + shade_p_value(obs_stat = Chisq_hat, direction = "greater") ## ----------------------------------------------------------------------------- visualize(null_dist_theory, method = "both") + shade_p_value(obs_stat = Chisq_hat, direction = "greater") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = Chisq_hat, direction = "greater") ## ----------------------------------------------------------------------------- chisq_test( gss, response = finrela, p = c( "far below average" = 1 / 6, "below average" = 1 / 6, "average" = 1 / 6, "above average" = 1 / 6, "far above average" = 1 / 6, "DK" = 1 / 6 ) ) ## ----------------------------------------------------------------------------- Chisq_hat <- gss |> specify(formula = finrela ~ sex) |> hypothesize(null = "independence") |> calculate(stat = "Chisq") ## ----------------------------------------------------------------------------- Chisq_hat <- gss |> observe(formula = finrela ~ sex, stat = "Chisq") ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(finrela ~ sex) |> hypothesize(null = "independence") |> generate(reps = 1000, type = "permute") |> calculate(stat = "Chisq") ## ----------------------------------------------------------------------------- null_dist_theory <- gss |> specify(finrela ~ sex) |> assume(distribution = "Chisq") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = Chisq_hat, direction = "greater") ## ----------------------------------------------------------------------------- visualize(null_dist_theory) + shade_p_value(obs_stat = Chisq_hat, direction = "greater") ## ----------------------------------------------------------------------------- visualize(null_dist, method = "both") + shade_p_value(obs_stat = Chisq_hat, direction = "greater") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = Chisq_hat, direction = "greater") ## ----------------------------------------------------------------------------- gss |> chisq_test(formula = finrela ~ sex) ## ----------------------------------------------------------------------------- d_hat <- gss |> specify(age ~ college) |> calculate(stat = "diff in means", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- d_hat <- gss |> observe(age ~ college, stat = "diff in means", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(age ~ college) |> hypothesize(null = "independence") |> generate(reps = 1000, type = "permute") |> calculate(stat = "diff in means", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = d_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = d_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- t_hat <- gss |> specify(age ~ college) |> hypothesize(null = "independence") |> calculate(stat = "t", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- t_hat <- gss |> observe(age ~ college, stat = "t", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(age ~ college) |> hypothesize(null = "independence") |> generate(reps = 1000, type = "permute") |> calculate(stat = "t", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- null_dist_theory <- gss |> specify(age ~ college) |> assume("t") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = t_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- visualize(null_dist_theory) + shade_p_value(obs_stat = t_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- visualize(null_dist, method = "both") + shade_p_value(obs_stat = t_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = t_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- d_hat <- gss |> specify(age ~ college) |> calculate(stat = "diff in medians", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- d_hat <- gss |> observe(age ~ college, stat = "diff in medians", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(age ~ college) |> # alt: response = age, explanatory = season hypothesize(null = "independence") |> generate(reps = 1000, type = "permute") |> calculate(stat = "diff in medians", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = d_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = d_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- F_hat <- gss |> specify(age ~ partyid) |> calculate(stat = "F") ## ----------------------------------------------------------------------------- F_hat <- gss |> observe(age ~ partyid, stat = "F") ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(age ~ partyid) |> hypothesize(null = "independence") |> generate(reps = 1000, type = "permute") |> calculate(stat = "F") ## ----------------------------------------------------------------------------- null_dist_theory <- gss |> specify(age ~ partyid) |> hypothesize(null = "independence") |> assume(distribution = "F") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = F_hat, direction = "greater") ## ----------------------------------------------------------------------------- visualize(null_dist_theory) + shade_p_value(obs_stat = F_hat, direction = "greater") ## ----------------------------------------------------------------------------- visualize(null_dist, method = "both") + shade_p_value(obs_stat = F_hat, direction = "greater") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = F_hat, direction = "greater") ## ----------------------------------------------------------------------------- slope_hat <- gss |> specify(hours ~ age) |> calculate(stat = "slope") ## ----------------------------------------------------------------------------- slope_hat <- gss |> observe(hours ~ age, stat = "slope") ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(hours ~ age) |> hypothesize(null = "independence") |> generate(reps = 1000, type = "permute") |> calculate(stat = "slope") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = slope_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = slope_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- correlation_hat <- gss |> specify(hours ~ age) |> calculate(stat = "correlation") ## ----------------------------------------------------------------------------- correlation_hat <- gss |> observe(hours ~ age, stat = "correlation") ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(hours ~ age) |> hypothesize(null = "independence") |> generate(reps = 1000, type = "permute") |> calculate(stat = "correlation") ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = correlation_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = correlation_hat, direction = "two-sided") ## ----------------------------------------------------------------------------- obs_fit <- gss |> specify(hours ~ age + college) |> fit() ## ----------------------------------------------------------------------------- null_dist <- gss |> specify(hours ~ age + college) |> hypothesize(null = "independence") |> generate(reps = 1000, type = "permute") |> fit() ## ----------------------------------------------------------------------------- null_dist2 <- gss |> specify(hours ~ age + college) |> hypothesize(null = "independence") |> generate(reps = 1000, type = "permute", variables = c(age, college)) |> fit() ## ----------------------------------------------------------------------------- visualize(null_dist) + shade_p_value(obs_stat = obs_fit, direction = "two-sided") ## ----------------------------------------------------------------------------- null_dist |> get_p_value(obs_stat = obs_fit, direction = "two-sided") ## ----------------------------------------------------------------------------- x_bar <- gss |> specify(response = hours) |> calculate(stat = "mean") ## ----------------------------------------------------------------------------- x_bar <- gss |> observe(response = hours, stat = "mean") ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(response = hours) |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "mean") ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- get_ci(boot_dist, type = "se", point_estimate = x_bar) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- sampling_dist <- gss |> specify(response = hours) |> assume(distribution = "t") ## ----------------------------------------------------------------------------- theor_ci <- get_ci(sampling_dist, point_estimate = x_bar) theor_ci visualize(sampling_dist) + shade_confidence_interval(endpoints = theor_ci) ## ----------------------------------------------------------------------------- t_hat <- gss |> specify(response = hours) |> hypothesize(null = "point", mu = 40) |> calculate(stat = "t") ## ----------------------------------------------------------------------------- t_hat <- gss |> observe(response = hours, null = "point", mu = 40, stat = "t") ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(response = hours) |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "t") ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- boot_dist |> get_ci(type = "se", point_estimate = t_hat) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- p_hat <- gss |> specify(response = sex, success = "female") |> calculate(stat = "prop") ## ----------------------------------------------------------------------------- p_hat <- gss |> observe(response = sex, success = "female", stat = "prop") ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(response = sex, success = "female") |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "prop") ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- boot_dist |> get_ci(type = "se", point_estimate = p_hat) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- sampling_dist <- gss |> specify(response = sex, success = "female") |> assume(distribution = "z") ## ----------------------------------------------------------------------------- theor_ci <- get_ci(sampling_dist, point_estimate = p_hat) theor_ci visualize(sampling_dist) + shade_confidence_interval(endpoints = theor_ci) ## ----------------------------------------------------------------------------- d_hat <- gss |> specify(hours ~ college) |> calculate(stat = "diff in means", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- d_hat <- gss |> observe(hours ~ college, stat = "diff in means", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(hours ~ college) |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "diff in means", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- boot_dist |> get_ci(type = "se", point_estimate = d_hat) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- sampling_dist <- gss |> specify(hours ~ college) |> assume(distribution = "t") ## ----------------------------------------------------------------------------- theor_ci <- get_ci(sampling_dist, point_estimate = d_hat) theor_ci visualize(sampling_dist) + shade_confidence_interval(endpoints = theor_ci) ## ----------------------------------------------------------------------------- d_hat <- gss |> specify(hours ~ college) |> calculate(stat = "ratio of means", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- d_hat <- gss |> observe(hours ~ college, stat = "ratio of means", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(hours ~ college) |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "ratio of means", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- boot_dist |> get_ci(type = "se", point_estimate = d_hat) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- t_hat <- gss |> specify(hours ~ college) |> calculate(stat = "t", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- t_hat <- gss |> observe(hours ~ college, stat = "t", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(hours ~ college) |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "t", order = c("degree", "no degree")) ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- boot_dist |> get_ci(type = "se", point_estimate = t_hat) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- d_hat <- gss |> specify(college ~ sex, success = "degree") |> calculate(stat = "diff in props", order = c("female", "male")) ## ----------------------------------------------------------------------------- d_hat <- gss |> observe(college ~ sex, success = "degree", stat = "diff in props", order = c("female", "male")) ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(college ~ sex, success = "degree") |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "diff in props", order = c("female", "male")) ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- boot_dist |> get_ci(type = "se", point_estimate = d_hat) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- sampling_dist <- gss |> specify(college ~ sex, success = "degree") |> assume(distribution = "z") ## ----------------------------------------------------------------------------- theor_ci <- get_ci(sampling_dist, point_estimate = d_hat) theor_ci visualize(sampling_dist) + shade_confidence_interval(endpoints = theor_ci) ## ----------------------------------------------------------------------------- z_hat <- gss |> specify(college ~ sex, success = "degree") |> calculate(stat = "z", order = c("female", "male")) ## ----------------------------------------------------------------------------- z_hat <- gss |> observe(college ~ sex, success = "degree", stat = "z", order = c("female", "male")) ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(college ~ sex, success = "degree") |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "z", order = c("female", "male")) ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- boot_dist |> get_ci(type = "se", point_estimate = z_hat) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- slope_hat <- gss |> specify(hours ~ age) |> calculate(stat = "slope") ## ----------------------------------------------------------------------------- slope_hat <- gss |> observe(hours ~ age, stat = "slope") ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(hours ~ age) |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "slope") ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- boot_dist |> get_ci(type = "se", point_estimate = slope_hat) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- correlation_hat <- gss |> specify(hours ~ age) |> calculate(stat = "correlation") ## ----------------------------------------------------------------------------- correlation_hat <- gss |> observe(hours ~ age, stat = "correlation") ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(hours ~ age) |> generate(reps = 1000, type = "bootstrap") |> calculate(stat = "correlation") ## ----------------------------------------------------------------------------- percentile_ci <- get_ci(boot_dist) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = percentile_ci) ## ----------------------------------------------------------------------------- standard_error_ci <- boot_dist |> get_ci(type = "se", point_estimate = correlation_hat) visualize(boot_dist) + shade_confidence_interval(endpoints = standard_error_ci) ## ----------------------------------------------------------------------------- obs_fit <- gss |> specify(hours ~ age + college) |> fit() ## ----------------------------------------------------------------------------- boot_dist <- gss |> specify(hours ~ age + college) |> generate(reps = 1000, type = "bootstrap") |> fit() ## ----------------------------------------------------------------------------- conf_ints <- get_confidence_interval( boot_dist, level = .95, point_estimate = obs_fit ) ## ----------------------------------------------------------------------------- visualize(boot_dist) + shade_confidence_interval(endpoints = conf_ints)