Publications by Data Whisperer
Week 14 _Sujit
Problem 14.3 This is an example of a nested design with two factors machine and spindle where the spindle is nested within the machine Factor A is machine and it has three levels (I=3) and Factor B is spindle and it has two levels (J=2) and the number of replicates are four(4) [K=4]. Machine (Factor A) has fixed effect and spindle Factor(B) ha...
1381 sym R (810 sym/4 pcs)
IE-5344 Week 1 suggested problem by R
Reading data x <- c(21,24,32,47,50,59,68,74,62,50,41,30) y <- c(185.79,214.47,288.03,424.84,454.68,539.03,621.55,675.06,562.03,452.93,369.95,273.98) Building model model <- lm(y~x) model ## ## Call: ## lm(formula = y ~ x) ## ## Coefficients: ## (Intercept) x ## -6.332 9.208 Hence our equation becomes \(\hat{Y...
941 sym R (1266 sym/11 pcs) 2 img
FA4
Getting data days <- c(91,105,106,108,88,91,58,82,81,65,61,48,61,43,33,36) ind <- c(16.7,17.1,18.2,18.1,17.2,18.2,16,17.2,18,17.2,16.9,17.1,18.2,17.3,17.5,16.6) Q1) Scatter plot of data plot(ind,days, main = "Scatter plot of Days to index ") Q2) Least square estimate of the parameter model <- lm(days~ind) model ## ## Call: ## lm(formula = ...
1217 sym R (959 sym/8 pcs) 6 img
Week 2 sug problem SDA
Data input x <- c(21,24,32,47,50,59,68,74,62,50,41,30) y <- c(185.79,214.47,288.03,424.84,454.68,539.03,621.55,675.06,562.03,452.93,369.95,273.98) Fitting our simple linear regression model model <- lm(y~x) model ## ## Call: ## lm(formula = y ~ x) ## ## Coefficients: ## (Intercept) x ## -6.332 9.208 Qb)Test fo...
460 sym R (1063 sym/7 pcs)
FA11
data <- read.csv("C:\\Users\\sjtha\\OneDrive\\Documents\\Academia\\Spring 2022\\SDA Dr.Matis\\Flipped Assignments\\FA11\\data-table-B21(2).csv") head(data) ## ï..i y x_1 x_2 x_3 x_4 ## 1 1 78.5 7 26 6 60 ## 2 2 74.3 1 29 15 52 ## 3 3 104.3 11 56 8 20 ## 4 4 87.6 11 31 8 47 ## 5 5 95.9 7 52 ...
2722 sym R (3972 sym/18 pcs)
Trial
Getting data y <- c(91,105,106,108,88,91,58,82,81,65,61,48,61,43,33,36) x1 <- c(16.7,17.1,18.2,18.1,17.2,18.2,16,17.2,18,17.2,16.9,17.1,18.2,17.3,17.5,16.6) x2 <- c(1:16) Getting least square of including two factor interaction with using lm function model1 <- lm(y~x1+x2+x1*x2) model1 ## ## Call: ## lm(formula = y ~ x1 + x2 + x1 * x2) ## ...
152 sym R (653 sym/5 pcs)
FA5
Getting Data Here , X = Minutes of Exposure Y = Number of Bacteria x <- c(1:12) y <- c(175,108,95,82,71,50,49,31,28,17,16,11) A) Fit a simple linear regression model to the data. What Is the value of R^2? model <- lm(y~x) summary(model) ## ## Call: ## lm(formula = y ~ x) ## ## Residuals: ## Min 1Q Median 3Q Max ##...
2346 sym R (1915 sym/18 pcs) 9 img
FA19
Getting data Let temperature be x1 Let Oil be x2 Let Time be x3 Let no of inedible kernel be y x1 <- c(7,5,7,7,6,6,5,6,5,6,5,7,6,6,6) x2 <- c(4,3,3,2,4,3,3,2,4,2,2,3,3,3,4) x3 <- c(90,105,105,90,105,90,75,105,90,75,90,75,90,90,75) y <- c(24,28,40,42,11,16,126,34,32,32,34,17,30,17,50) dat <- data.frame(x1,x2,x3,y) 1. Fit a first order mo...
1470 sym R (2387 sym/13 pcs)