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Hey, I need this hw in 24 hours.I am a business major.So,it is not going to be in engineer level hw.

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..ooo Turkcell ? 12:50 ieu.blackboard.com Assignment 2 - Exploratory. Analytics %92 For this assignment you may use the data set from the previous assignment or choose a new one. In doing all of the following operations, record the R commands as an R notebook and submit the ".Rmd" file along with the data set file. 1. Explore a single variable: Exploratory analysis of a single variable answers the question "how is the distribution of this variable?". Choose a variable from your data set and draw its histogram, preferably using ggplot2 library. 2. Many statistical methods rely on the assumption that a variable is normally distributed. Use the shapiro.test function to test normality of the variable you have visualized in question 1. If you want you can check out an example at https://www.sheffield.ac.uk/polopoly_fs/1.5 karadimitriou-normalR.pdf 3. Explore two variables: This exploratory analysis answers the question "how the two variables are correlated?" Choose two variables from the data set, one of which is the variable you have used in questions 1&2. Draw a plot of the two variables. Then comment on the relation between the two variables, I.e do you think the two are strongly correlated? Positively or negatively correlated? D

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R | R Studio Assignment Description Image [Solution]
..ooo Turkcell ? 12:50 ieu.blackboard.com %92 4. Explore three variables: This analysis aims to analyse the question "how does the relation of variables A and B are regulated by the third variable C?". An example is the Diet variable regulating the relation between Weight and Time variables in the ChickWeight dataset. Choose a third variable from your data set and produce a visualization (e.g. 'facet' in ggplot2) that exposes the interaction of three variables. The third variable should be a rank or categorical variable. If there's no such variable in your data set then you may need to produce one, e.g. by assigning categories to ranges. For example if the third variable is "age" (not categorical), you can produce a new variable "age.range" which has string values such as "18-25", "25-35", etc. 5. Comme on the regulatory effect of the third variable in question 4. For example do you think all or some categories have a more clear/strong correlation than what you have seen in the drawing for question 3? 6. Think of a business question that can be posed onto your data set of choice. Comment (but not conduct) the exploratory analysis necessary to answer the question. Which variables and which relations should be explored? D

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