CourseKata - ckcode-chapter-c3-confidence-intervals

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ckcode ⌲ chapter-c3-confidence-intervals

require(coursekata)

# run this a few times
b1(Tip ~ Condition, data = TipExperiment)
b1(Tip ~ Condition, data = resample(TipExperiment))

# no solution; commenting for submit button

ex() %>% check_error()

CK Code: ch11-02-01-code

require(coursekata)

# modify this to make 1000 bootstrapped b1s
sdob1_boot <- do( ) * b1(Tip ~ Condition, data = resample(TipExperiment))

# visualize sdob1_boot with a histogram

# modify this to make 1000 bootstrapped b1s
sdob1_boot <- do(1000) * b1(Tip ~ Condition, data = resample(TipExperiment))

# visualize sdob1_boot with a histogram
gf_histogram(~b1, data = sdob1_boot)

ex() %>% {
  check_function(., "do") %>%
    check_arg("object") %>%
    check_equal()
  check_or(.,
    check_function(., "gf_histogram") %>% {
      check_arg(., "object") %>% check_equal()
      check_arg(., "data") %>% check_equal(eval = FALSE)
    },
    override_solution(., '{
      sdob1_boot <- do(1000) * b1(Tip ~ Condition, data = resample(TipExperiment))
      gf_histogram(sdob1_boot, ~b1)
    }') %>%
      check_function("gf_histogram") %>% {
        check_arg(., "object") %>% check_equal(eval = FALSE)
        check_arg(., "gformula") %>% check_equal()
      },
    override_solution(., '{
      sdob1_boot <- do(1000) * b1(Tip ~ Condition, data = resample(TipExperiment))
      gf_histogram(~sdob1_boot$b1)
    }') %>%
      check_function("gf_histogram") %>%
      check_arg("object") %>%
      check_equal(eval = FALSE)
    )
  }

CK Code: ch11-02-02-code

require(coursekata)

# we have created the sampling distribution for you
sdob1_boot <- do(1000) * b1(Tip ~ Condition, data = resample(TipExperiment))

# run favstats to check out the mean of the sampling distribution

# we have created the sampling distribution for you
sdob1_boot <- do(1000) * b1(Tip ~ Condition, data = resample(TipExperiment))

# run favstats to check out the mean of the sampling distribution
favstats(~b1, data = sdob1_boot)

ex() %>%
       check_function("favstats") %>%
       check_result() %>%
       check_equal()

CK Code: ch11-02-03-code

require(coursekata)

sdob1_boot <- do(1000) * b1(Tip ~ Condition, data = resample(TipExperiment))
sdob1_boot <- arrange(sdob1_boot, b1)

# we’ve written code to print the 26th b1
sdob1_boot$b1[26]

# write code to print the 975th b1

# we’ve written code to print the 26th b1
sdob1_boot$b1[26]

# write code to print the 975th b1
sdob1_boot$b1[975]

ex() %>% {
        check_output_expr(., "sdob1_boot$b1[26]")
        check_output_expr(., "sdob1_boot$b1[975]")
}

CK Code: ch11-02-04-code

require(coursekata)

sdob1 <- do(1000) * b1(shuffle(Tip) ~ Condition, data = TipExperiment)
sdob1_boot <- do(1000) * b1(Tip ~ Condition, data = resample(TipExperiment))

# use favstats to find the standard error of these sampling distributions
favstats( )
favstats( )

sdob1 <- do(1000) * b1(shuffle(Tip) ~ Condition, data = TipExperiment)
sdob1_boot <- do(1000) * b1(Tip ~ Condition, data = resample(TipExperiment))

# use favstats to find the standard error of these sampling distributions
favstats(~ b1, data = sdob1)
favstats(~ b1, data = sdob1_boot)

ex() %>% {
       check_function(., "favstats", index = 1) %>%
         check_result() %>%
         check_equal()
       check_function(., "favstats", index = 2) %>%
         check_result() %>%
         check_equal()
   }

CK Code: ch11-03-01-code

require(coursekata)

# create the condition model of tip and save it as Condition_model
Condition_model <-
confint(Condition_model)

# create the condition model of tip and save it as Condition_model
Condition_model <- lm(Tip ~ Condition, data = TipExperiment)
confint(Condition_model)

ex() %>%
    check_function("lm") %>%
    check_result() %>%
    check_equal()

CK Code: ch11-03-02-code

require(coursekata)

# we’ve created the model for you
Condition_model <- lm(Tip ~ Condition, data = TipExperiment)

# use confint to find the 95% confidence intervals for both parameters

# we’ve created the model for you
Condition_model <- lm(Tip ~ Condition, data = TipExperiment)

# use confint to find the 95% confidence intervals for both parameters
confint(Condition_model)

ex() %>%
    check_function("confint") %>%
    check_result() %>%
    check_equal()

CK Code: ch11-05-01-code

require(coursekata)

# here’s code to find the best-fitting empty model of Tip
empty_model <- lm(Tip ~ NULL, data = TipExperiment)

# use confint with the empty model (instead of Condition_model)
confint(Condition_model)

# here’s code to find the best-fitting empty model of Tip
empty_model <- lm(Tip ~ NULL, data = TipExperiment)

# use confint with the empty model (instead of Condition_model)
confint(empty_model)

ex() %>%
    check_function("confint") %>%
    check_result() %>%
    check_equal()

CK Code: ch11-05-02-code

require(coursekata)

# Simulate Check
set.seed(22)
x <- round(rnorm(1000, mean=15, sd=10), digits=1)
y <- x[x > 5 & x < 30]
TipPct <- sample(y, 44)
TipExperiment$Check <- (TipExperiment$Tip / TipPct) * 100

# we’ve created the Check model for you
Check_model <- lm(Tip ~ Check, data = TipExperiment)

# find the confidence interval around the slope

# we’ve created the Check model for you
Check_model <- lm(Tip ~ Check, data = TipExperiment)

# find the confidence interval around the slope
confint(Check_model)

ex() %>%
    check_function("confint") %>%
    check_result() %>%
    check_equal()

CK Code: ch11-05-03-code

require(coursekata)

# Simulate Check
set.seed(22)
x <- round(rnorm(1000, mean=15, sd=10), digits=1)
y <- x[x > 5 & x < 30]
TipPct <- sample(y, 44)
TipExperiment$Check <- (TipExperiment$Tip / TipPct) * 100

# make a bootstrapped sampling distribution
sdob1_boot <-

# we’ve added some code to visualize this distribution in a histogram
gf_histogram(~ b1, data = sdob1_boot, fill = ~middle(b1, .95), bins = 100)

# make a bootstrapped sampling distribution
sdob1_boot <- do(1000) * b1(Tip ~ Check, data = resample(TipExperiment))

# we’ve added some code to visualize this distribution in a histogram
gf_histogram(~ b1, data = sdob1_boot, fill = ~middle(b1, .95), bins = 100)

ex() %>%
  check_object("sdob1_boot") %>%
  check_equal()

CK Code: ch11-05-04-code

require(coursekata)

# Simulate Check
set.seed(22)
x <- round(rnorm(1000, mean=15, sd=10), digits=1)
y <- x[x > 5 & x < 30]
TipPct <- sample(y, 44)
TipExperiment$Check <- (TipExperiment$Tip / TipPct) * 100

# we have made a bootstrapped sampling distribution
sdob1_boot <- do(1000) * b1(Tip ~ Check, data = resample(TipExperiment))

# modify the code below to arrange the sampling distribution in order by b1
sdob1_boot <- arrange()

# find the 26th and 975th b1
sdob1_boot$b1[ ]
sdob1_boot$b1[ ]

# we have made a bootstrapped sampling distribution
sdob1_boot <- do(1000) * b1(Tip ~ Check, data = resample(TipExperiment))

# modify the code below to arrange the sampling distribution in order by b1
sdob1_boot <- arrange(sdob1_boot, b1)

# find the 26th and 975th b1
sdob1_boot$b1[26]
sdob1_boot$b1[975]

ex() %>% {
  check_output_expr(., "sdob1_boot$b1[26]")
  check_output_expr(., "sdob1_boot$b1[975]")
}

CK Code: ch11-05-05-code

require(coursekata)

# import game_data
students_per_game <- 35
game_data <- data.frame(
    outcome = c(16,8,9,9,7,14,5,7,11,15,11,9,13,14,11,11,12,14,11,6,13,13,9,12,8,6,15,10,10,8,7,1,16,18,8,11,13,9,8,14,11,9,13,10,18,12,12,13,16,16,13,13,9,14,16,12,16,11,10,16,14,13,14,15,12,14,8,12,10,13,17,20,14,13,15,17,14,15,14,12,13,12,17,12,12,9,11,19,10,15,14,10,10,21,13,13,13,13,17,14,14,14,16,12,19),
    game = c(rep("A", students_per_game), rep("B", students_per_game), rep("C", students_per_game))
)

# we have fit and saved game_model for you
game_model <- lm(outcome ~ game, data = game_data)
# add plot = TRUE
pairwise(game_model)

# we have fit and saved game_model for you
game_model <- lm(outcome ~ game, data = game_data)

# add plot = TRUE
pairwise(game_model, plot = TRUE)

ex() %>%
    check_function(., "pairwise") %>%
    check_arg("plot") %>%
    check_equal()

CK Code: ch11-05-06-code

require(coursekata)

# here we have saved the Condition model
Condition_model <- lm(Tip ~ Condition, data=TipExperiment)

# modify these to find the 90% and 99% CI
confint(Condition_model)
confint(Condition_model)

# here we have saved the Condition model
Condition_model <- lm(Tip ~ Condition, data=TipExperiment)

# modify these to find the 90% and 99% CI
confint(Condition_model, level = .90)
confint(Condition_model, level = .99)

ex() %>% {
       check_function(., "confint", index = 1) %>%
         check_result() %>%
         check_equal()
       check_function(., "confint", index = 2) %>%
         check_result() %>%
         check_equal()
   }

CK Code: ch11-06-01-code

require(coursekata)

TipExp2 <- rbind(TipExperiment, TipExperiment)

# this calculates the confidence interval from the original 44 tables
confint(lm(Tip ~ Condition, data = TipExperiment))

# calculate the confidence interval for TipExp2 containing 88 tables

# this calculates the confidence interval from the original 44 tables
confint(lm(Tip ~ Condition, data = TipExperiment))

# calculate the confidence interval for TipExp2 containing 88 tables
confint(lm(Tip ~ Condition, data = TipExp2))

ex() %>% {
       check_function(., "confint", index = 1) %>%
         check_result() %>%
         check_equal()
       check_function(., "confint", index = 2) %>%
         check_result() %>%
         check_equal()
   }

CK Code: ch11-06-02-code

ckcode-chapter-c2-model-comparison ckcode-chapter-d1-intro-multivariate-models

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