Assignment Task
Questions for R-Assignment
Each question should be in its own section (use ## in R Markdown). All your answers must show the code you used to solve them.
(1) Academic Integrity: Please type the following statement as the first question of your submission:
“I am aware that I can discuss the questions on this assignment with others. However, sharing my solutions with another person, or having someone share their solutions with me, in any way is considered cheating and will be penalized according to Trent’s Academic Integrity policies.”
(2) The Normal Distribution
In this section, we will explore plotting and normal distribution probability problems. Create chunks carefully as needed to ensure that the overall flow is clean and organized – points will be deducted for hard-to-follow solutions, even if they are correct.
(a) Using dnorm(), create a careful, annotated normal density figure like the ones you would use to solve a qnorm problem. Put two vertical lines (in red) on the figure at +2.1 and -1.3, and label them as z1 and -z0. Label your x and y axes carefully. TWO BONUS MARKS: use polygon() command to shade the left tail in a solid colour of your choice. Note: no assistance will be given for the use of polygon, it’s a bonus because it will require you to go above and beyond and figure this out on your own.
(b) Take the two probabilities from WeBWorK R Assignment 3, Question 1, and solve them. Ensure you state the questions (in your own words, or mathematically using LaTeX) before using chunks and R code to solve.
(c) Take the two inverse probability problems from WeBWorK R Assignment 3, Question 2, and solve them. Again, ensure you state the problems clearly before your solution, and this time, reference the figures.
(d) Using mfrow, plot both inverse probability problems from part (c) (above) side-by-side, in the same graphical way as part (a), but with problem-specific labels.
(e) Solve WeBWorK R Assignment 3, TWO BONUS MARKS:
plot the appropriate figure for this problem with two x axes: one at line 0, corresponding to the standard normal Z, and one at line 4, corresponding to the X axis dollar figures.
(3) Linear Models
In this question, you will explore linear models – that is, lines of best fit. This question uses the data set provided in WeBWorK R Assignment 3, Question 4.
(a) Download the .csv file provided to you in WeBWorK R Assignment 3, Question 4. Move it to your project directory (where you saved your Rmd file!).
Rename it if you wish – it’s a randomized string which you can rename to something like r 3 q3a.csv if you wish.
(b) The data consists of two variables: x and y. These should be considered to be unitless variables for purposes of statements and plots.
(c) Load the data into your R Markdown environment using read.csv, as demonstrated in Workshop #07 and #08, and store the loaded data as a sensibly named data.frame object. Check the structure of the loaded object to ensure it matches the csv file.
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