First Assignment

 Hello Dr Friedman, 

Im writing this as per the blog post section of the last assignment. Thanks for waiting for me to get off work from a very early shift to be able to accept this post as part of my assignment. I look forward to hearing from you throughout the semester

Comments

Post a Comment

Popular posts from this blog

R Package: pfStat

Image
 Final Project: pfStat, a personal finance mini-suite in R GitHub URL: https://github.com/tahousex/pfStat What is pfStat? This package is entitled pfStat, and is a small package with a few functions dedicated to helping a user calculate different personal finance metrics. The functions include the ability to derive Returns on Interest, Tax Rates based on Income, Taxable Income and Home Equity, and the purpose behind choosing these kinds of metrics was to emphasize the importance of knowing where one puts their wallet. It goes without being entirely sanctimonious that the dollar does not go as far as it once did, and knowing where one stands amongst their own finances and parameters in the financial world will assist them in making more calculated decisions.  The Functions: The taxrate() income receives input from the user income and state tax percentage represented as a decimal percentage (0-1)   and gives the user output of a dollar amount of the taxes owed based on the input The taxa
Read more

Module 7 Assignment

Module 7 Assignment  R Code: # Module 7 Assignment #1.1 #First define the data set required, I/O values x <- c(16, 17, 13, 18, 12, 14, 19, 11, 11, 10) y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48) #Then we must create the relationship (correlation) cor(x,y) #This value 0.728 that is in the console tells us a few things #This relationship is correlated and is increasing linearly #I may choose to plot these findings using plot() plot(x,y) #See the lower righthand window for a composite plot for this data, as the regression trednline would positively increase through the points #1.2 #This settion of code will get the value for the coefficients of the linear regression for this data Regression_model=lm(y~x) Regression_model$coefficients #2.1 #This problem will go over the aquisition of similar values with a different data set discharge <- c(3.600,1.800,3.333,2.283,4.533,2.883) waiting <- c(79,54,74,62,85,55) plot(discharge,waiting) #2.1 Regression_model_1=lm(waiting~discharge)
Read more

The Tampa Feasibility Report featuring R based Visualizations

Image
  Visualizations for Professional Writing's Final Deliverable These visualizations were taken from FDOT data for our local district on the subject of transit usage, and crash data that was munged into relative ratios that had a regression performed on these variables within.  Pie Chart The above figure is a pie chart using data collected that estimates the higher end limits of hourly numbers of passengers that could utilize each method of public transportation during peak hours Transit Regression This figure here shows the monthly totals for crashes, fatalities and serious injuries from accidents recorded within FDOT data and the regression line method was used against the 'free_y' inputs which allowed there to be a 95 percent confidence regression on the other two variables from the one in question showing certain trends within and between variables. Bus versus Pubic Transport with 95 percent regression line fitted The same style of regression was sued here but this time i
Read more