QuestionQuestion

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# Question 1: (12 Marks)
# You are asked to evaluate COVID-19 fatality data to see if all age groups are equally affected.
# To test this, you randomly select 10,000 people who died from the virus and mark down their ages.
# Is there evidence to suggest that one age group is more affected than others?
###
agegroups = c("Child", "Teen", "Adult", "Senior")
observed = c(100, 100, 350, 400)

# A) Give the Expected values in the vector below. Then, run the next line of code to complete the dataset:
expected = c(,,,) #Fill in these values!
coviddata = data.frame(agegroups, observed, expected)

# B) Is a Chi-Square test appropriate to use here? Explain why or why not.
# C) State a null and alternative hypothesis.
# D) Conduct an appropriate statistics test. Assume conditions are met.
# E) Can you conclude that COVID-19 has affected all age groups equally? Why or why not?
# F) Back up your answer above with a ggplot (feel free to use colour-coding with tidyverse!)

###
# Question 2: (11 Marks)
# As a business analyst with a local mining company, you are asked to evaluate if mill shutdown
# is equally distributed throughout the year, or if certain months have more shutdowns than expected.
# You randomly select 84 days throughout the year and record if the mill was shutdown. Is
# there evidence to suggest that mill shutdown is not associated with months of the year?
###
months = c("Jan", "Feb", "Mar", "Apr", "May", "June", "July", "Aug", "Sept", "Oct", "Nov", "Dec")
observedshutdowns = c(2,6,5,7,8,16,10,8,8,5,4,2)
expected = c(7,7,7,7,7,7,7,7,7,7,7,7)
shutdowndata = data.frame(months, observedshutdowns, expected)

# A) State the null and alternative hypothesis.
# B) Conduct a chi-square test. Assume conditions are met.
# C) Can you conclude that mill data is associated with particular months of the year? Why or why not?
# D) If you answered No to the above, which month is most out of the normal?
# E) Back up your answer with a ggplot. Make sure your months are in the proper order.

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###
# Question 1: (12 Marks)
# You are asked to evaluate COVID-19 fatality data to see if all age groups are equally affected.
# To test this, you randomly select 10,000 people who died from the virus and mark down their ages.
# Is there evidence to suggest that one age group is more affected than others?
###
agegroups = c("Child", "Teen", "Adult", "Senior")
observed = c(100, 100, 350, 400)


# A) Give the Expected values in the vector below. Then, run the next line of code to complete the dataset:
expected = c(2500 * 0.095,2500 * 0.095,2500 * 0.095,2500 * 0.095) #Fill in these values!
coviddata = data.frame(agegroups, observed, expected)

# B) Is a Chi-Square test appropriate to use here? Explain why or why not.
# It is appropriate to use if we know how many people for each age category are observed to collect the
# data on the person who died. Because of the lack of information, I assume that 2500 people are observed...

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