# data[].attributes.wish_content.attachment_props.question
Based on the data frame above, what is the correct code to filter students who scored above 75 and passed the course?

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
filtered_students <- students %>%  filter(Score > 75 & Passed == 'Pass')

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
filtered_students <- students %>%  filter(Score > 75 | Passed == 'Pass')

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
filtered_students <- students %>%  filter(Score > 75 & Passed = 'Pass')

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
filtered_students <- filter(Score > 75 & Passed = 'Pass')

# data[].attributes.wish_content.attachment_props.question
If you want to combine data frame students and students_year together, what is the correct code?

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
students_c = merge(students, students_year, by= c('StudentID'， ‘Name', 'Score', 'Passed')

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
students_c = merge(students, students_year, by= c('StudentID'， ‘Name')

# data[].attributes.wish_content.attachment_props.question
If you want to visualize and compare the score differences in three subjects among five students, which function should you use?

# data[].attributes.wish_content.attachment_props.question
If you conducted an experiment with a treatment and control group and collected data after they received the intervention, which function should you use to test the treatment effect?

# data[].attributes.wish_content.attachment_props.question
If the output of the reliability test shows that the reliability after dropping item1, item2, and item3 is 0.5, 0.4, and 0.71, respectively, which item should you drop to reach acceptable reliability?

# data[].attributes.wish_content.attachment_props.question
Based on the regression output above, which of the following interpretations is correct regarding the coefficient for the factor variable Region?

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
The coefficient for RegionNorth (5.6789) indicates that sales in the North region are expected to be $5.68 lower than in the reference region when holding all other variables constant.

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
The coefficient for RegionSouth (3.4567) indicates that sales in the South region are expected to be $3.46 higher than in the North region when holding all other variables constant.

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
The coefficient for RegionNorth (5.6789) indicates that sales in the North region are expected to be $5.68 higher than in the reference region when holding all other variables constant.

# data[].attributes.wish_content.attachment_props.poll_choices[].choice_text
The coefficient for RegionSouth (3.4567) indicates that sales in the South region are expected to be $3.46 lower than in the North region when holding all other variables constant.

# data[].attributes.wish_content.attachment_props.question
Given the rpart plot output, if you see a person with a credit score of 680, residential assets of 8.4e+6, a loan term of 10, and a loan amount of 20e+6, would you predict that this person will delay loan payment or not?