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MATH 2565 (19)
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Department
Mathematics and Statistics
Course
MATH 2565
Professor
Georges Monette
Semester
Winter

Description
**** Introduction Exercise Question 5 by Dijana Danilovic - Friday, 31 January 2014, 10:50 AM 5. What is the difference between causal inference and predictive inference? Causal inference is when you are focusing on what happens to a variable, y, when you change another variable, x. Predictive inference is different because it focuses on predicting observations and what is going to happen to a variable, y, using past observations and information. Causal inference is focused on an immediate change, whereas predictive inference focuses on future predictions. Should we insist on relying only on experimental data for causal inference? Discuss. Yes. Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. In a causal inference, one reasons to the conclusion that something is, or is likely to be, the cause of something else. For example quitting smoking will get you unhealthy. An advantage of causal inference can determine the cause and effect of a study. An example could be whether or not nicotine affects one's driving ability (which it does) and also experiments are the only means by which cause and effect can be established. **** Chapter 1 Reading Question 7 - Problems With Observational Data by Ahmed Bashoeh - Thursday, 6 February 2014, 12:09 PM Since observational data are not 100% correct and there could be many confounding effects, so if you want to know casual casual inference (that X causes Y) assumptions have to be made, for example an individual may be more likely to receive a treatment because the individual has one or more additional disorders or diseases or the fact that some individuals received the treatment because of their personal or health characteristics, so making a conclusion is hard. Observational data comes with many possible biases, for example since observational data is non-random (X is not randomly assigned but it could consist of a random sample from a population), there is a sample bias meaning some members of the population is less likely to be included than others. ****Introduction 4: Difference between observational data and experimental data. by Nicholas Polsinelli - Saturday, 8 February 2014, 10:41 PM What is the difference between observational data and experimental data? Observational study and experiments are the two major types of study involved in research. Both observational data and experimental data involve observation; the difference between these two types of study is in the way the observation is done. Experimental requires you to observe the result of when you make a change. ” In an experimental study there is human interaction and it’s the actual researcher that manipulates the variable. Experimental data can be qualitative or quantitative. In this type of study, the researcher relies more on data collected. Observational Data is the relationship between changes that already exist. According to Duke University, “observational studies involve collecting and comparing units from existing databases that have nonrandom treatment assignments. With observational, the researcher has no control over the data and does not affect the population.(ex. Number of homeless people) ****Chapter 1 - Reading question 4; Models in Everyday Life by Amit Bhambi - Sunday, 9 February 2014, 6:01 PM 4. Give three examples of models that you use in everyday life. For each, say what is the purpose of the model and in what ways the representation differs from the real thing. Time Spend studying, the purpose of this model is to determine the time need to study to earn the grade you want, and it differs because you set the time before you start studying and do not take into account getting stuck, or going as a faster and might end up need more or else time to earn the grade. Saving for the future, the purpose of this model is to save for emergence’s or some you want in the future, and it differs because the emergency might not come, or the thing you want might now, you might not want or need in the future anymore. How much fun can you have, the purpose of this model is to determine if you can have fun now or later, and it differs because you think you can have fun now and do the homework in 2 hrs later, so you only need to set 2 hrs aside to get it done, but then you get stuck and need a lot more time, and you end up staying up all night getting it done, and then feel horrible due to lack of sleep for the whole next day, and you pass out as soon as you get home, when you could have just done the work first, went to sleep on time and had the whole next day to have fun, instead of having fun for 3 hrs the day before. ****Chapter 1 Question 3: Personalities by Filip Miskic - Sunday, 9 February 2014, 5:50 PM If an individual has the personality trait of being patient, it would imply that the person is “able to accept or tolerate delays, problems, or suffering without becoming annoyed or anxious.” (Oxford, 1) Based on that information, I would assume that I would be able to poke the individual repeatedly without making that person angry. Usually this kind of behaviour would invoke a response whereby I would have to endure either a physical or verbally abusive repercussions. Finally, this explains with that we know how to act to get a particular response or what to expect from an individual because we anticipate it . This used mostly by match-making sites as people with similar traits are match together for better understanding ****Chapter 2 - Reading question 2; data frame by Amit Bhambi - Sunday, 9 February 2014, 7:08 PM How are variables and cases arranged in a data frame? In a data frame, each row refers to a CASE, and each column refers to a VARIABLE. To create a Data Frame in R, enter the following: Type each variable into a vector. Use the data.frame( ) function to create a data frame from the vectors. ****Chapter 2--Reading Q4 by Jeremiah Akinola - Sunday, 9 February 2014, 6:47 PM Longitudinal: A case that is traced over time, each person being included more than ones in the data frame. Cross- sectional sample: A snapshot of the population that includes people of different ages each person is included only one. (Edited by Hang Jing Wang - original submission Saturday, 8 February 2014, 2:49 AM)
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