Publications by Arvind Sharma
final_merge_sol
1 Set UP # Clear the workspace rm(list = ls()) # Clear environment gc() # Clear unused memory ## used (Mb) gc trigger (Mb) max used (Mb) ## Ncells 544289 29.1 1211880 64.8 686462 36.7 ## Vcells 994556 7.6 8388608 64.0 1875959 14.4 cat("\f") # Clear the console library("dplyr") ## ## Attaching package: 'dpl...
69 sym R (4820 sym/13 pcs)
Final_sol
Welcome to the Final! You must show your work and justify all of your responses. I cannot evaluate partial credit without evidence of understanding. This final is open book, open note, open Internet, and open computer. You may not communicate via any means with any intelligent being on this planet or another, and you may not communicate with lifefo...
9809 sym R (11160 sym/66 pcs) 8 img
HW5_solution
For Review, watch LINEAR REGRESSION IN R The attached .csv file has data pertaining to hospital expenditures (dependent variable). The column RVUs is a representation of standard outpatient workload. Using R, conduct correlation analysis (between the two variables) and interpret. Then fit a linear model with Expenditure as the dependent variable (...
13418 sym R (15353 sym/67 pcs) 18 img
HW_HypothesisTesting_solution
Homework 5, Data Analysis Resources : W5 folder in our Dropbox folder now contains some supplementary lecture readings re-emphasizing the core concepts of CLT, standard error, hypothesis testing, confidence interval and p-values. Please skim through them (~15 minutes) before attempting the assignment to refresh your memories. Please find the Open ...
23192 sym Python (25205 sym/213 pcs) 18 img
Gauss Markov Assumptions
gvlma function may be worth a look. 1 Gauss Markov Assumptions The Gauss-Markov assumptions, also known as the Gauss-Markov conditions or classical linear regression assumptions, are a set of key assumptions that form the basis for Ordinary Least Squares (OLS) regression. When these assumptions are met, OLS estimators are unbiased, efficient, and ...
7072 sym
covariance_variance
Correlation and covariance are both measures of the relationship between two variables in statistics. 1 Covariance: 1.1 Definition: Covariance is a measure of how much two variables change together. It indicates the direction of the linear relationship between two variables (whether they tend to increase or decrease together). 1.2 Formula: For t...
4256 sym 1 img
linear_regression_implementation
# Clear the workspace rm(list = ls()) # Clear environment gc() # Clear unused memory ## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb) ## Ncells 531014 28.4 1182544 63.2 NA 669277 35.8 ## Vcells 973384 7.5 8388608 64.0 16384 1840364 14.1 cat("\f") # Clear the console 1 Data 1.1 Export ...
232 sym R (10322 sym/29 pcs) 4 img
log_variable_for_fit_R2
# Clear the workspace rm(list = ls()) # Clear environment gc() # Clear unused memory ## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb) ## Ncells 531021 28.4 1182564 63.2 NA 669277 35.8 ## Vcells 974118 7.5 8388608 64.0 16384 1840364 14.1 cat("\f") # Clear the console if(!is.null(dev.l...
2795 sym R (5524 sym/23 pcs) 10 img
Box_Cox_transformation
1 Intro The Box-Cox transformation is a family of power transformations that are used to stabilize the variance and make a dataset more closely approximate a normal distribution. It is named after statisticians George Box and Sir David Cox who introduced it in a 1964 paper. https://www.jstor.org/stable/2984418?seq=4 Here, y is the original variabl...
5220 sym R (4537 sym/29 pcs) 10 img
logit_implementation
1 SetUp https://www.ucl.ac.uk/~uctqiax/PUBLG100/2015/week9/seminar9.html 1.1 Packages # install.packages("https://cran.r-project.org/src/contrib/Archive/Zelig/Zelig_4.2-1.tar.gz", # repos=NULL, # type="source") # install.packages("sandwich") # packageVersion("Zelig") # # install.packages("Zelig") # install.packa...
2700 sym R (10291 sym/61 pcs) 6 img 3 tbl