This is a Corporate Information Security Risk Matrix File that you might need:
https://docs.google.com/spreadsheets/d/1_U5hq5extn9ke_bLzJ08e-M086-yW2aE/edit?usp=sharing&ouid=101979596434486218548&rtpof=true&sd=true
Does not need to be very detailed.
There are three links at bottom of the project overview pdf file you can use as templates. If you cannot click on them please let me know.
Section 1: One-Way ANOVA
You will need to address the header at the top of the data files using the skip command or something similar
This is the carbon dioxide emissions for each state by fuel type. This is emission and energy usage data provided by the US government. HINT: choose either the totals column or one of the categories you are interested in. Remember, you have a categorical variable (states) and a continuous variable for this model. Make sure you are removing the header and the last row of data from the data frame (the last row is a row total).
Do a QQ plot or formal test for normality to determine if the data are normally distributed. Include the graphs in a word document to submit with your R file. If the data is seriously not-normal, determine the appropriate transformation and transform your data.
What are your null and research hypotheses?
Section 1: One-Way ANOVA
You will need to address the header at the top of the data files using the skip command or something similar
This is the carbon dioxide emissions for each state by fuel type. This is emission and energy usage data provided by the US government. HINT: choose either the totals column or one of the categories you are interested in. Remember, you have a categorical variable (states) and a continuous variable for this model. Make sure you are removing the header and the last row of data from the data frame (the last row is a row total).
Do a QQ plot or formal test for normality to determine if the data are normally distributed. Include the graphs in a word document to submit with your R file. If the data is seriously not-normal, determine the appropriate transformation and transform your data.
What are your null and research hypotheses?
The data analysis report is specifically for the client and should address the challenges faced by the
client. Write a summary of your overall findings and recommendations to the executives at the bank.
Think of this section as your closing remarks of a presentation, where you summarize your key
findings, model performance, and make recommendations to improve loan processes at the bank.This
needs to be provided in a word document once you knit the document.
Please refer to the textbook and answer questions
http://nangkhieutin.com/wp-content/uploads/2018/08/Starting-Out-With-Java.pdf
No plagiarism as well thanks!
We are going to use some of our knowledge of Pandas to write a simple filter Python script. I am not providing a template. You will need to write a script that contains the “__main__” section along with a single required function. You may also want to include a parse_args function which can be copied and modified from previous assignments.
Data: attached
Instruction:
Download the iris dataset and use pandas to load in the dataset into a pandas dataframe. Write a function named filter that will take an argument, species, which is the species of the iris plant. It will use this species name to filter the dataframe so that it will contain only observations pertaining to the species passed in. It should then use the describe method of dataframes to determine the descriptive statistics of each column. Your function should print this value to the terminal but it should also write the output to a text file. Name this file output.txt.
Your script should also take in user output via command line arguments. The first argument will be the path to your file. The second argument should be the species. You might run your script like this for example:
python3 hw3.py iris.csv “Iris-setosa”
Running your script as in the example should create a file named output.txt in the current working directory that may not have been there already. This file will contain the descriptive statistics for the observations of “Iris-setosa”.
