For the conditional probability of left-right by gender in Switzerland, we initially recoded Irscale so that...
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For the conditional probability of left-right by gender in Switzerland, we initially recoded Irscale so that values between 0 to 3 represent "left' and 7 to 10 to represent 'right'. Generate the probability table now for moderate, representing values 4 to 6, vs. non-moderate (now representing values of 0:3 & 7:10), For the purpose of the task, you must generate the probability table, as we did above, but now for what we deem as the moderate and non-moderate categories. Hint 1: this is how we recoded initially. You will need to replace what we did for "Left", "Right", and "NA". switzerland data <- es >1 filter (entry "CH") > mutate( gndr 1rscale recode(gndr, "I "Male", case when() (1) "Female", 1rscale sint 0:3 "Left", irscale sins 7:10 "Right", irscale sins c(4, 5, 6, 77, 88, 99) NA character, TRUE as.character(Irscale) -NA_character_), Hint 2: all the code above for generating the probability table should not have references to left and right, but rather now as moderate and non-moderate. For example in: data.frame(Gender Calculate the total counts for each gender (sum of Left and Right) contingency_wide$Total <- contingency wide$Left + contingency wide Right Add a zov with the grand totals for Left, Right, and overall contingency vide <- rbind(contingency wide, Total", sum(contingency vide$Left), Left Right sum(contingency_videsRight), Totalsum (contingency vide$Total))) Hint 3: make sure not to skip the step where we generate the contingency table of counts. Provide the entire code necessary to produce the revised probability table as directed in the prompt. Task 2 Now that you have your revised probability table, generate the new visualization that is referred to above in the tutorial as task2plot'. The new plot should show the conditional probability by gender of moderate v. non-moderate. The new subtitle should be: Given a person's gender, what's the probability of being Moderate vs. Non-Moderate in Switzerland? Provide the entire code necessary to generate the revised visualization. Task 3 Refer above to task3plot, the one where we see clsprty relative to yrben for France. First, why do the two lines, referring to 'Yes' and 'No' "criss-cross" at a certain point and then a gap emerges between the two? Second, what is your general takeaway from the graph regarding more recent cohorts (e., more recent birth years as visualized)? For the conditional probability of left-right by gender in Switzerland, we initially recoded Irscale so that values between 0 to 3 represent "left' and 7 to 10 to represent 'right'. Generate the probability table now for moderate, representing values 4 to 6, vs. non-moderate (now representing values of 0:3 & 7:10), For the purpose of the task, you must generate the probability table, as we did above, but now for what we deem as the moderate and non-moderate categories. Hint 1: this is how we recoded initially. You will need to replace what we did for "Left", "Right", and "NA". switzerland data <- es >1 filter (entry "CH") > mutate( gndr 1rscale recode(gndr, "I "Male", case when() (1) "Female", 1rscale sint 0:3 "Left", irscale sins 7:10 "Right", irscale sins c(4, 5, 6, 77, 88, 99) NA character, TRUE as.character(Irscale) -NA_character_), Hint 2: all the code above for generating the probability table should not have references to left and right, but rather now as moderate and non-moderate. For example in: data.frame(Gender Calculate the total counts for each gender (sum of Left and Right) contingency_wide$Total <- contingency wide$Left + contingency wide Right Add a zov with the grand totals for Left, Right, and overall contingency vide <- rbind(contingency wide, Total", sum(contingency vide$Left), Left Right sum(contingency_videsRight), Totalsum (contingency vide$Total))) Hint 3: make sure not to skip the step where we generate the contingency table of counts. Provide the entire code necessary to produce the revised probability table as directed in the prompt. Task 2 Now that you have your revised probability table, generate the new visualization that is referred to above in the tutorial as task2plot'. The new plot should show the conditional probability by gender of moderate v. non-moderate. The new subtitle should be: Given a person's gender, what's the probability of being Moderate vs. Non-Moderate in Switzerland? Provide the entire code necessary to generate the revised visualization. Task 3 Refer above to task3plot, the one where we see clsprty relative to yrben for France. First, why do the two lines, referring to 'Yes' and 'No' "criss-cross" at a certain point and then a gap emerges between the two? Second, what is your general takeaway from the graph regarding more recent cohorts (e., more recent birth years as visualized)?
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Income Tax Fundamentals 2013
ISBN: 9781285586618
31st Edition
Authors: Gerald E. Whittenburg, Martha Altus Buller, Steven L Gill
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