Quiz 3

The data set is from a case-control study of smoking and Alzheimer’s disease. The data set has two variables of main interest:

• smoking a factor with four levels “None”, “<10”, “10-20”, and “>20” (cigarettes per day)
• disease a factor with three levels “Alzheimer”, “Other dementias”, and “Other diagnoses”.

Q1 Describe the largest group that has other dementias. Discuss it by number of cigarettes per day.

The largest group in the “other dementias” category are non-smokers.

Q2 Describe one group that has more cases than expected given independence (by chance). Discuss it by number of cigarettes per day.

The group that has more cases than expected given independence is people in the “other dementias” category who smoke more than 20 cigarretts a day.

Q3 Does smoking seem to matter in determining other dementias? Discuss your reason using the masaic chart above.

I would say no, smoking does not seem to matter in determining “other dementias”. The number of people who smoke at all and do not smoke are relatively even, therefore making it very hard to find a strong relationship between smoking and other dementia cases.

Q4 Create correlation plot for RailTrail.

Hint: The RailTrail data set is from the mosaicData package.

Q5 List all four variables that have negative correlation with the number of trail users (volume).

The four variables that have negative correlation with the number of trail users (or the volume) is the spring season, the fall season, cloudcover, and precipitation.

Q6 What season seems to be least popular for trail users?

Fall seems to be the least popular season for trail user. Fall has the largest negative correlation with volume according to our data.

Q7 The correlation coefficient between hightemp and cloudcover is quite small. Would you be sure that the two variables are not related at all?

Hint: One word answer (e.g., yes or no) is NOT enough. Explain why.

No, I’m sure that the two variables must be related considering they are both attributed to weather. However, this small correlation coefficient could possibly be explained by other variables that go along with weather. These variables could include season, humidity, or a vareity of weather variables. These correlation coefficients are only linear, so we must consider that there may be some non-linear variables that would show a different relationship between the two.

Q7.a Continued from Q7. Create scatter plot. After examing the scatter plot, would you conclude that the two variables are not related at all?

Hint: Discuss your reason by explaining your scatter plot.

I would say that there is very little relatioship between the two. I can tell because the plots are very scattered and it is almost impossible to verify a pattern or relationship.

Q8 Hide the messages, the code and its results on the webpage.

Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.