Academic Integrity Violation

• The final project is to be completed individually.
• You are not to work with others on the projects (you may ask for help from the instructors only).
• The work that you submit must be yours in its entirety.
• You are not to use the discussion board to discuss the project.
• You should set up an office hour appointment if you have questions or email your instructor (remember there is a 24 hour turn around time).
• Failure to adhere to these guidelines will result in an academic integrity violation.
• No late work will be accepted. If you have a documented emergency that prevents you from completing a project, please contact your instructor.

R: Final Project
This project is meant to assess your ability to program in R based on what we’ve done in the course. The
result should be a report with a narrative throughout, section headings, graphs outputted in appropriate
places, etc.
To be clear be sure to include markdown text describing what you are doing, even when not explicitly asked for! The audience you are writing for is someone that has taken an introductory statistics course
You will create a .Rmd file and HTML output that answers the questions below. Then you’ll upload the HTML file to Moodle on the assignment link.
Note: Your file should follow good programming practices as outlined in the top section of the course. All
code chunks should show in your HTML document.
The final document should be at maximum 5 pages. Recall you can change the size of graphs on your
outputted document using code chunk options (out.width = for instance).

Overall Goal
We will read in a data set, do some basic exploratory data analysis (EDA – numeric summaries and graphs),
data transformations, and then fit some linear regression models. It should be fun 🙂 You should use the
tidyverse to read in, manipulate, and graph the data set.
Data set
We’ll be using a data set containing information about fish toxicity available at the assignment link in Moodle.
The original data can be found on the UCI machine learning repository here.
This dataset was used to develop quantitative regression QSAR models to predict acute aquatic toxicity towards the fish Pimephales promelas (fathead minnow) on a set of 908 chemicals. LC50 data, which is the concentration that causes death in 50% of test fish over a test duration of 96 hours, was used as model response. The model comprised 6 molecular descriptors: MLOGP (molecular properties), CIC0 (information indices), GATS1i (2D autocorrelations), NdssC (atom-type counts), NdsCH ((atom-type counts), SM1_Dz (2D matrix-based descriptors). Report Components: Introduction, EDA, Multiple Linear Regression Models

First, you should have a small section that introduces the purpose of the report and the data set you’ll be working with. You should provide a link to the original data source, describe the variables (as far as you are able), and read in the data – displaying a small snippet of it in the output. You should also describe the R packages you’ll use.
• When you read in the data, note that there are no variable names included and the delimiter is non-standard. You should give appropriate column names prior to displaying the snippet of data.

Next, you should have a section where you explore the data. The variable we are most interested is the LC50 variable (our response variable). You’ll want to focus your EDA on this variable but feel free to display relationships between other variables as well.
• You should provide a ‘pairs’ style plot using the GGally package
• You should have numeric summaries of the LC50 variable (at different levels/combinations of other variables)
• You should have plots of the LC50 variable (again showing relationships with other variables). Be sure to modify elements such as size, fill, etc. in your plots in order to show relationships between multiple variables. Use facet_wrap() or facet_grid() at least once.
• You should have at least 4 plots and at least 3 different kinds of plots (histogram, scatter plot, box plot,
We want to investigate relationships between the variables in terms of their medians. For each variable, we’ll create a new binary variable. The new variable should be set to low if the original variable’s value is less than or equal to the median value of that variable, otherwise it should be set to high. – Write a function to accomplish this transformation using the ifelse() or if_else() function.
– Use the apply() function to apply this function to each column of the data frame – Turn the result into a data frame (if it isn’t one already) and save it as an R object Using the new binary variables, – You should create a two-way contingency table with corresponding side-byside/stacked bar plot visual using the binary LC50 variable and another binary variable of your choosing

Repeat the above two-way table and bar plot using another binary variable of your choosing (with LC50)
Multiple Linear Regression Models We want to be able to predict a value of LC50 using the other variables in the data set (use the original data frame, not the binary version). Fit four different linear regression models. – At least one model should include a polynomial term – At least one model should include an interaction term
For each model, display the summary() of the fit.
For a model of your choice, display the diagnostic plots and comment on the model fit and the normality
assumption (referring to the relevant graphs and the patterns you see there).
Lastly, let’s use two models of your choice (at least one of which is an MLR model) to predict the LC50 value!
Predict the LC50 value at the median setting of each predictor (explanatory) variable in your model.

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