Category: R

The course is regression analysis, and we use R as programming languages. Please follow the following “dataset Project” PDF (Already finished the intro part) to write a dataset project Analysis report 1. The link for the dataset is: https://www.kaggle.com/datasets/mirichoi0218/insur…
And please follow the research question and hypothesis in “Final Project Intro 1” pdf and The picture attached to write R code (such as scatter plot, multicolinearity analysis, etc).

The course is regression analysis, and we use R as programming languages. Please follow the following “dataset Project” PDF (Already finished the intro part) to write a dataset project Analysis report 1. The link for the dataset is: https://www.kaggle.com/datasets/mirichoi0218/insur…
And please follow the research question and hypothesis in “Final Project Intro 1” pdf and The picture attached to write R code (such as scatter plot, multicolinearity analysis, etc).

Important: Please knit you RMD file to HTML for submission. An unknitted RMD file will result in 0 point.
The knitted html file can be found in the same folder or directory as your rmd file with the same file name but “html” file extension.

All the instructions and information you need are in the
attached files. (Plz, read the files i attached carefully)
You are required To conduct a sensitivity and uncertainty analysis using the Green-Ampt code for the Green-Ampt Equation with First-Order Sensitivity Analysis (FOUA) for 11 Texture classes for the assigned soil horizon, follow these steps:
Step 1: Obtain Rainfall Data
Use TP 40 (https://www.weather.gov/gyx/TP40s.htm) to obtain 100-year 3-hour rainfall values for your assigned city.
Use the values from the two nearest contours to get upper and lower limits. Use the mean of these values as the baseline. Countors are (3.5 , 4) inches
Step 2: Gather Soil Data (from table-2)
Refer to Rawls et al., (1983) for soil data. Use mean values as baseline.
Use upper and lower limits (one-standard deviation) when available for sensitivity coefficients.
Adjust field capacity values to be lower than porosity.
Step 3: Implement First-Order Sensitivity Analysis (FOUA)
Perform FOUA for Green-Ampt Equation parameters using the provided code.
For each parameter, calculate sensitivity coefficients (Sa and Sr) using equations (1) and (2) in the appendix.
Calculate sensitivity coefficients one at a time, perturbing each input while keeping others at baseline.
Note that Sa and Sr can be positive or negative, indicating the direction of the sensitivity.
Step 4: Uncertainty Analysis
Use FOUA to quantify uncertainty in the model output.
Assume input parameters follow a normal distribution with mean and standard deviation.
Use equations (3), (4), and (5) in the appendix to calculate the mean and standard deviation of the model output.
Step 5: Data Presentation
Present all data neatly in tables, including rainfall values, soil parameters, sensitivity coefficients, and uncertainty analysis results.
Use figures to visually show sensitivity of parameters, and include graphs supporting your findings.
Step 6: Discuss Results
Discuss your results, emphasizing sensitive parameters and their impact on the Green-Ampt model.
Analyze how uncertainties in input parameters propagate through the model and affect the output.
Support your discussion with graphs and tables.
Step 7: List Assumptions
Clearly list all assumptions made during the analysis, such as the normal distribution assumption for input parameters.

Car Sales. Consider the data on used cars (mlba::ToyotaCorolla ) with 1436 records and details on 38 variables, including Price, Age, KM, HP, and other specifications. The goal is to predict the price of a used Toyota Corolla based on its specifications.
Use predictors Age_08_04, KM, Fuel_Type, HP, Automatic, Doors, Quarterly_Tax, Mfr_Guarantee, Guarantee_Period, Airco, Automatic_airco, CD_Player, Powered_Windows, Sport_Model, and Tow_Bar.
To ensure everyone gets the same results, use the following code to convert categorical predictors to dummies, create training and holdout data sets, and normalize the training set and holdout set. Note the holdout set is normalized by using the training set.
# load the data and preprocess
toyota.df <- mlba::ToyotaCorolla toyota.df <- mlba::ToyotaCorolla %>%
mutate(
Fuel_Type_CNG = ifelse(Fuel_Type == “CNG”, 1, 0),
Fuel_Type_Diesel = ifelse(Fuel_Type == “Diesel”, 1, 0)
)

# partition
set.seed(1)
idx <- createDataPartition(toyota.df$Price, p=0.6, list=FALSE) train.df <- toyota.df[idx, ] holdout.df <- toyota.df[-idx, ] #Normalize the dataset. Use the training set to determine the normalization. normalizer <- preProcess(train.df, method="range") train.norm.df <- predict(normalizer, train.df) holdout.norm.df <- predict(normalizer, holdout.df) Fit a neural network model to the data. Use a single hidden layer with two nodes. Record the RMS error for the training data and the holdout data. Repeat the process, changing the number of hidden layers and nodes to single layer with 5 nodes, and two layers, 5 nodes in each layer. What happens to the RMS error for the training data as the number of layers and nodes increases? What happens to the RMS error for the holdout data? Comment on the appropriate number of layers and nodes for this application.

Requirements
This lab assignment has two parts, each asking you to define a function in R that solves
a specific business problem. Each part is worth 5 points with a total of 10 points. The detailed
requirements are listed below.
Part 1: Inventory replenishment
In your RStudio interface, open a new R Script window. (Check Lesson 1.2.4 for
instructions on running R scripts.) Define an R function named unit() that calculates the
number of weekly units that Mary Fresh needs for a particular product to replenish her inventory
with the following three arguments:
• sales: the annual sales revenue of the product (required)
• unit_cost: the per-unit cost of the product (required)
• profit: the profit margin (optional with the default value of 0.05)
The number of units is calculated by: 𝑢𝑛𝑖𝑡 = 𝑠𝑎𝑙𝑒𝑠 ∗ (1 − 𝑝𝑟𝑜𝑓𝑖𝑡)/(𝑢𝑛𝑖𝑡_𝑐𝑜𝑠𝑡 ∗ 52).
Please make sure your function rounds any decimal answers to the nearest whole number.
Execute your codes to define the function. Write an R statement in your script window to test it
using sales = 10000 and unit_cost = 15.5. Your function should return 12.
Save your script file as Lab6FirstNameLastName.R to your computer (or to your U:
drive if you are using the Foster Remote Lab).
Part 2: Ad revenue
In the same R Script window, define a new function named revenue() that calculates
the annual revenue that a YouTuber makes with the following five arguments:
• videos: the number of videos the YouTuber publishes in a year (required)
• views: the average number of views for each video (required)
2
• conversion: the conversion rate, i.e., the percentage of views that generate
impressions (optional with the default value of 1)
• CPM: YouTube’s CPM (optional with the default value of 8.50)
• commission: YouTube’s commission rate (optional with the default value of 0.45)
Where 𝑟𝑒𝑣𝑒𝑛𝑢𝑒 = 𝑣𝑖𝑑𝑒𝑜𝑠 ∗ 𝑣𝑖𝑒𝑤𝑠 ∗ 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 ∗ (𝐶𝑃𝑀/1000) ∗ (1 − 𝑐𝑜𝑚𝑚𝑖𝑠𝑠𝑖𝑜𝑛).
Please round the return result to the nearest hundredth.
Execute your code to define the function. Write an R statement in your script window to
test it using videos = 48, views = 21300. Your function should return 4779.72. Save your work.
Your file should include both the functions and the test statements.

you are required to Conduct a sensitivity and uncertainty analysis using the Green-Ampt code provided to you to identify sensitive model parameters for the Green-Ampt Equation using First-Order Sensitivity Analysis (FOUA) for 11 Texture classes for the assigned soil horizon.
All the instructions and information you need are in the attached files. (Plz, read the files I attached carefully)

Require to write a statistical report showing on choosing a classification method for predicting the status, as well as goodness of fit of that classifier.

Hi, I started a project on supervised and unsupervised learning but I can’t proceed. (ROC=NAN , packages don’t load).
I need some help on the implementattion of the models and I need you to comment every step and put a final solution on the predictions.Steps to do: implement Logistic, regression, decision, tree, random forest, lasso, SVM, PCA, Kmeans, KNN and others., explain every step and explain the graph, put a final comment with the explanation of the final prediction. At the end knit the codes in html or pdf and send me everything. There are a lot of examples on kaggle, you can follow them, please don’t copy and paste. Thank you https://www.kaggle.com/datasets/pavansubhasht/ibm-…

Hi, I started a project on supervised and unsupervised learning but I can’t proceed. (ROC=NAN , packages don’t load).
I need some help on the implementattion of the models and I need you to comment every step and put a final solution on the predictions.Steps to do: implement Logistic, regression, decision, tree, random forest, lasso, SVM, PCA, Kmeans, KNN and others., explain every step and explain the graph, put a final comment with the explanation of the final prediction. At the end knit the codes in html or pdf and send me everything. There are a lot of examples on kaggle, you can follow them, please don’t copy and paste. Thank you https://www.kaggle.com/datasets/pavansubhasht/ibm-…