CourseKata - ckcode-chapter-b5-quantitative-explanatory

Items Test Book

Book

ckcode ⌲ chapter-b5-quantitative-explanatory

library(coursekata)

# edit the Height2Group_model code to create Height_model
Height2Group_model <- lm(Thumb ~ Height2Group, data = Fingers)

# save the predictions of the Height_model as a new variable in Fingers
Fingers$Height_predict <-

# this code prints out the first 6 observations
head(select(Fingers, Thumb, Height, Height_predict))

# edit the Height2Group_model code to create Height_model
Height_model <- lm(Thumb ~ Height, data = Fingers)

# save the predictions of the Height_model as a new variable in Fingers
Fingers$Height_predict <- predict(Height_model)

# this code prints out the first 6 observations for 3 columns
head(select(Fingers, Thumb, Height, Height_predict))

ex() %>% {
  check_object(., "Height_model") %>%
    check_equal()
  check_object(., "Fingers") %>%
    check_column("Height_predict") %>%
    check_equal()
}

CK Code: B5_Code_Quantitative_01

library(coursekata)

# saves the Height model
Height_model <- lm(Thumb ~ Height, data = Fingers)

# print it out

# saves the Height model
Height_model <- lm(Thumb ~ Height, data = Fingers)

# print it out
Height_model

ex() %>% check_output_expr("Height_model")

CK Code: B5_Code_Regression_01

require(coursekata)

Fingers$SexNum <- as.numeric(Fingers$Sex)

# fit a model of Thumb length based on Sex
Sex_model <- lm()

# fit a model of Thumb length based on SexNum
SexNum_model <- lm()

# this prints the parameter estimates from the two models
Sex_model
SexNum_model

# fit a model of Thumb length based on Sex
Sex_model <- lm(Thumb ~ Sex, data=Fingers)

# fit a model of Thumb length based on SexNum
SexNum_model <- lm(Thumb ~ SexNum, data=Fingers)

# this prints the parameter estimates from the two models
Sex_model
SexNum_model

ex() %>% {
    check_object(., "Sex_model") %>% check_equal()
    check_object(., "SexNum_model") %>% check_equal()
    check_output_expr(., "Sex_model
SexNum_model")
}

CK Code: B5_Code_Comparing_01

require(coursekata)

# this calculates SST
empty_model <- lm(Thumb ~ NULL, data=Fingers)
print("SST")
sum(resid(empty_model)^2)

# this calculates SSE
Height_model <- lm(Thumb ~ Height, data = Fingers)
print("SSE")
sum(resid(Height_model)^2)

# no test

ex() %>% check_error()

CK Code: B5_Code_Error_01

require(coursekata)

# this saves the Height_model
Height_model <- lm(Thumb ~ Height, data = Fingers)

# print the ANOVA tables for this model

# this saves the Height_model
Height_model <- lm(Thumb ~ Height, data = Fingers)

# print the ANOVA tables for this model
supernova(Height_model)

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

CK Code: B5_Code_Assessing_01

require(coursekata)

Fingers <- filter(Fingers, Thumb >= 33 & Thumb <= 100)

# this transforms all Thumb lengths into z-scores
Fingers$zThumb <- zscore(Fingers$Thumb)

# modify this to do the same for Height
Fingers$zHeight <-

# this transforms all Thumb lengths into z-scores
Fingers$zThumb <- zscore(Fingers$Thumb)

# modify this to do the same for Height
Fingers$zHeight <- zscore(Fingers$Height)

ex() %>% check_object("Fingers") %>% {
  check_column(., "zThumb") %>% check_equal()
  check_column(., "zHeight") %>% check_equal()
}

CK Code: B5_Code_Correlation_01

require(coursekata)

Fingers <- Fingers %>%
  filter(Thumb >= 33 & Thumb <= 100) %>%
  mutate(
    zThumb = zscore(Thumb),
    zHeight = zscore(Height)
  )

# this makes a scatterplot of the raw scores
# size makes the points bigger or smaller
gf_point(Thumb ~ Height, data = Fingers, size = 4)

# zThumb and zHeight have already been created for you
# modify the code below to make a scatterplot of the z-scores
gf_point( , data = Fingers, size = 4, color = "navy")

# this makes a scatterplot of the raw scores
# size makes the points bigger or smaller
gf_point(Thumb ~ Height, data = Fingers, size = 4)

# zThumb and zHeight have already been created for you
# modify the code below to make a scatterplot of the z-scores
gf_point(zThumb ~ zHeight, data = Fingers, size = 4, color = "navy")

ex() %>% {
  check_function(., "gf_point", index = 1) %>% {
    check_arg(., "object") %>% check_equal()
    check_arg(., "data") %>% check_equal()
  }
  check_function(., "gf_point", index = 2) %>% {
    check_arg(., "object") %>% check_equal()
    check_arg(., "data") %>% check_equal()
  }
}

CK Code: B5_Code_Correlation_02

require(coursekata)
Fingers <- Fingers %>%
  filter(Thumb >= 33 & Thumb <= 100) %>%
  mutate(
    zThumb = zscore(Thumb),
    zHeight = zscore(Height)
  )
Height_model <- lm(Thumb ~ Height, data = Fingers)

# this fits a regression model of Thumb by Height
lm(Thumb ~ Height, data = Fingers)

# write code to fit a regression model predicting zThumb with zHeight

# this fits a regression model of Thumb by Height
lm(Thumb ~ Height, data = Fingers)

# write code to fit a regression model predicting zThumb with zHeight
lm(zThumb ~ zHeight, data = Fingers)

ex() %>%
  check_function("lm", index = 2) %>%
  check_result() %>%
  check_equal()

CK Code: B5_Code_Correlation_03

require(coursekata)

Fingers <- Fingers %>%
    filter(Thumb >= 33 & Thumb <= 100) %>%
    mutate(
        zThumb = zscore(Thumb),
        zHeight = zscore(Height)
    )

# this calculates the correlation of Thumb and Height
cor(Thumb ~ Height, data = Fingers)

cor(Thumb ~ Height, data = Fingers)

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

CK Code: B5_Code_Correlation_04

require(coursekata)

Fingers <- Fingers %>%
    filter(Thumb >= 33 & Thumb <= 100) %>%
    mutate(
        zThumb = zscore(Thumb),
        zHeight = zscore(Height)
    )

Hand <- select(Fingers, Thumb, Index, Middle, Ring, Pinkie, Height)

# run the cor() function with the Hand data frame

cor(Hand)

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

CK Code: B5_Code_Pearsonsr_01

require(coursekata)

Fingers <- Fingers %>%
    filter(Thumb >= 33 & Thumb <= 100) %>%
    mutate(
        zThumb = zscore(Thumb),
        zHeight = zscore(Height)
    )

# this code finds the square root of the PRE.1529
sqrt(.1529)

# add code to calculate the correlation between Thumb and Height

# this code finds the square root of .1529
sqrt(.1529)
# add code to calculate the correlation between Thumb and Height
cor(Thumb ~ Height, data = Fingers)

ex() %>% check_output_expr("cor(Thumb ~ Height, data = Fingers)")

CK Code: B5_Code_Pearsonsr_02

require(coursekata)

Fingers$ShuffThumb <- shuffle(Fingers$Thumb)
shuffled_b1 <- b1(ShuffThumb ~ Height, data = Fingers)

gf_point(ShuffThumb ~ Height, data = Fingers) %>%
  gf_lm(color = "purple") %>%
  gf_labs(title = paste("Shuffled Data / b1 = ", round(shuffled_b1, digits = 2)))

# no solution; just submit the code from the prompt

ex() %>% check_error()

CK Code: B5_Code_Fitting_01

require(coursekata)
set.seed(42)

# use do() to create 10 shuffled b1s

# use do() to create 10 shuffled b1s
do(10) * b1(shuffle(Thumb) ~ Height, data = Fingers)

ex() %>% {
    check_function(., 'do') %>%
        check_arg('object') %>% check_equal()
    check_function(., 'b1') %>% {
        check_arg(., 'object') %>% check_equal(eval = FALSE)
        check_arg(., 'data') %>% check_equal()
    }
}

CK Code: B5_Code_Fitting_02

require(coursekata)

# run your code here

CK Code: B4_Code_Review2_01

ckcode-chapter-b4-digging-deeper ckcode-chapter-c1-logic-inference

Course Outline

Items Test Book

ckcode ⌲ chapter-b5-quantitative-explanatory

Responses

list Items Test Book

ckcode ⌲ chapter-b5-quantitative-explanatory

Items Test Book