Publications by Vinayak Kamath
R_BrdgWrkshp_Week1_Quiz_HTML
1. Create a vector that contains 20 numbers: data.set <- c(5:13, 10:15, 3:6, 20) data.set ## [1] 5 6 7 8 9 10 11 12 13 10 11 12 13 14 15 3 4 5 6 20 length(data.set) ## [1] 20 2. Convert the vector from 1 into a character vector: character.data.set <- as.character(data.set) is.character(character.data.set) ## [1] TRUE 3. Convert the ...
1441 sym R (3570 sym/38 pcs)
R_BrdgWrkshp_Week1_Quiz_02_RMD
1. Write two functions. These functions should take a numeric vector as input, and determine the mean and the median from the values in the vector. Please do not use the built-in mean() or median() functions. You should also provide some code that initializes your numeric vector and calls your two functions!: -> Function to do the actual calculat...
459 sym R (826 sym/13 pcs)
Data607-Week01 Assignment - Basic Loading and Transformation
The 2020 Endorsement Primary 1 Overview: Which Democratic candidates are receiving the most support from prominent members of their party? Joe Biden vs. Elizabeth Warren vs. Bernie Sanders. The endorsements are used by candidates to influence voters and other party elites; it can be seen as a way to get the popular or more acceptable. The endo...
2248 sym R (6466 sym/10 pcs) 3 img
Data606 - Lab 2 - Introduction to data
Some define Statistics as the field that focuses on turning information into knowledge. The first step in that process is to summarize and describe the raw information - the data. In this lab, you will gain insight into public health by generating simple graphical and numerical summaries of a data set collected by the Centers for Disease Control ...
16164 sym R (474984 sym/111 pcs) 13 img 1 tbl
Data607-Week05-Assignment Tidying and Transforming Data
Create a .CSV file (or optionally, a MySQL database!) that includes all of the information above. You’re encouraged to use a “wide” structure similar to how the information appears above, so that you can practice tidying and transformations as described below. CSV File created using Microsft Excel. File can found in Git Repository. Read th...
712 sym R (3720 sym/10 pcs) 3 img 6 tbl
Data606 - Lab 5a - Foundations for statistical inference - Sampling distributions
In this lab, we investigate the ways in which the statistics from a random sample of data can serve as point estimates for population parameters. We’re interested in formulating a sampling distribution of our estimate in order to learn about the properties of the estimate, such as its distribution. The data We consider real estate data from th...
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Data606-HomeWork-Chapter 6 - Inference for Categorical Data
2010 Healthcare Law. (6.48, p. 248) On June 28, 2012 the U.S. Supreme Court upheld the much debated 2010 healthcare law, declaring it constitutional. A Gallup poll released the day after this decision indicates that 46% of 1,012 Americans agree with this decision. At a 95% confidence level, this sample has a 3% margin of error. Based on this inf...
7155 sym R (2253 sym/16 pcs)
Data606 - Lab 8 - Introduction to linear regression
Batter up The movie Moneyball focuses on the “quest for the secret of success in baseball”. It follows a low-budget team, the Oakland Athletics, who believed that underused statistics, such as a player’s ability to get on base, betterpredict the ability to score runs than typical statistics like home runs, RBIs (runs batted in), and batting...
11078 sym R (10529 sym/69 pcs) 19 img
Data607 - MajorAssignment Project4 - Document Classification
Document Classification Introduction It can be useful to be able to classify new “test” documents using already classified “training” documents. A common example is using a corpus of labeled spam and ham (non-spam) e-mails to predict whether or not a new document is spam. For this project, we start with a spam/ham dataset, then predict t...
1865 sym R (5391 sym/15 pcs) 4 img
Data605-Week1-HomeWork2-kamath
Problem set 1 . #defining the sample matrix: A <- matrix(c(3, 2, 2, -1, 5, 0, 4, 1, 6), 3, 3) A ## [,1] [,2] [,3] ## [1,] 3 -1 4 ## [2,] 2 5 1 ## [3,] 2 0 6 A1 <- t(A) A1 ## [,1] [,2] [,3] ## [1,] 3 2 2 ## [2,] -1 5 0 ## [3,] 4 1 6 #Checking by comparing the Matrix side by s...
218 sym R (2803 sym/36 pcs) 3 img