Publications by Arvind Sharma
correlation_covariance
Correlation and covariance are both measures of the relationship between two variables in statistics. Covariance: 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). Formula: For two variabl...
2165 sym
Resume_Experiment_RCT
1 Set Up # Clear the workspace rm(list = ls()) # Clear environment gc() # Clear unused memory ## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb) ## Ncells 525780 28.1 1167594 62.4 NA 669268 35.8 ## Vcells 967837 7.4 8388608 64.0 32768 1840504 14.1 cat("\f") # Clear the console The LST pack...
2280 sym R (11932 sym/89 pcs) 4 img
HW4
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 ...
21151 sym Python (24848 sym/210 pcs) 17 img
MLE
1 OLS is a special case of MLE Ordinary Least Squares (OLS) can be viewed as a special case of Maximum Likelihood Estimation (MLE) under certain assumptions. The relationship between OLS and MLE is particularly evident in the context of the simple linear regression model. In a simple linear regression model with a normally distributed error term, t...
7836 sym R (1911 sym/14 pcs) 1 img
solution_LLN_CLT_Theory_Discussion
The Law of Large Numbers (LLN) and the Central Limit Theorem (CLT) are fundamental concepts in probability and statistics. 1 Law of Large Numbers (LLN): Definition: The Law of Large Numbers states that as the sample size increases, the sample mean converges in probability to the population mean. In simpler terms, as you take more observations from...
5589 sym
Count_Model_Implementation
1 Data We use the data set warpbreaks. This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn. remove(list=ls()) ?warpbreaks df <- warpbreaks head(df) ## breaks wool tension ## 1 26 A L ## 2 30 A L ## 3 54 A L ## 4 25 A L ## 5 70 A ...
3344 sym R (5899 sym/25 pcs) 4 img
Satterthwaite_formula
1 Null/Alt \[ n_1=28 \] \[ n_2=22 \] I will use function mean. I am given - n1 <- 28 n2 <- 22 x1_bar <- 1025 x2_bar <- 910 s1 <- 150 s2 <- 125 2 Method 1: test stat vs critical value I need to compute my test statistic and compare it with my critical value 2.1 Test Statistic se <- sqrt( (s1^2 / n1) + (s2^2 / n2) ) se ## [1] 38.90757 test_stat...
435 sym
Discussion_Solution: Normal_to_Standard_Normal
Lets first create the normally distributed data with mean 108 and sd of 7.2 # Clear the workspace rm(list = ls()) # Clear environment gc() # Clear unused memory ## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb) ## Ncells 526179 28.2 1168724 62.5 NA 669288 35.8 ## Vcells 965134 7.4 8388608 64.0 16384...
329 sym R (1756 sym/10 pcs) 3 img
ggplot2_intro
Read this help file for R Markdown. 1 Set Up Clear all environments. Installing and loading all the libraries. Make sure you have the libraries installed. 2 Import Data Now, I will import my data. Make sure you comment out or exclude or do not use View(train) command. df <- read.csv("~/Library/CloudStorage/Dropbox/WCAS/Summer/Data Analysis/share/...
3030 sym Python (15683 sym/65 pcs) 12 img
merging_datasets
1 Import # Consumers consumers <- read.csv("~/Library/CloudStorage/Dropbox/WCAS/Data Analysis/data_MA/multiple_table_data/Restaurant_Ratings/consumers.csv") consumer_preferences <- read.csv("~/Library/CloudStorage/Dropbox/WCAS/Data Analysis/data_MA/multiple_table_data/Restaurant_Ratings/consumer_preferences.csv") # Restaurants restaurants <- rea...
421 sym R (7617 sym/34 pcs) 9 img