Publications by Keeno Glanville
DATA608-HW2
Register for API keys Within this assignment I will be utilizing data from the Bureau of Labor Statistics Public Data API and the Federal Reserve Board. In order to obtain the data I require I will sign up for API keys from both of these organizations. https://fred.stlouisfed.org/docs/api/api_key.html https://data.bls.gov/registrationEngine/ API...
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DATA621 HW2
1 raw <- read_csv('https://raw.githubusercontent.com/kglan/MSDS/main/DATA621/HW2/classification-output-data.csv', col_names = TRUE) ## Rows: 181 Columns: 11 ## ── Column specification ─────────────────────────────────────────────────────�...
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Data621 HW2
1. Dataset raw <- read_csv('https://raw.githubusercontent.com/kglan/MSDS/main/DATA621/HW2/classification-output-data.csv', col_names = TRUE) ## Rows: 181 Columns: 11 ## ── Column specification ──────────────────────────────────────────────────�...
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DATA621-HW1
DATA EXPLORATION Within this data set there are 2276 observations of 16 variables. The main focal point of this data is that we want to predict the target wins that a team will have over a given parameters. To first attack the data set there was some basic cleaning to remove the unnecessary naming within the columns. We then did some exploratio...
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DATA605 Final Part 1
Using R, generate a random variable X that has 10,000 random Gamma pdf values. A Gamma pdf is completely describe by n (a size parameter) and lambda (λ , a shape parameter). Choose any n greater 3 and an expected value (λ) between 2 and 10 (you choose) n <- 5 # Size parameter lambda <- 4 # Shape X <- rgamma(10000, shape = n, rate = lambda...
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DATA605 Assignment10 Redo
Smith is in jail and has 1 dollar; he can get out on bail if he has 8 dollars. A guard agrees to make a series of bets with him. If Smith bets A dollars, he wins A dollars with probability .4 and loses A dollars with probability .6. Find the probability that he wins 8 dollars before losing all of his money if (a) he bets 1 dollar each time (tim...
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Data605 Taylor Series
Load Package library(pracma) ## Warning: package 'pracma' was built under R version 4.2.2 f(x) = 1/(1-x) f1 <- function(x) { 1 / (1 - x) } a1 <- 0 n1 <- 5 taylor(f1,a1,n1) ## [1] 1.000293 1.000029 1.000003 1.000000 1.000000 1.000000 f(x) = e^x f1 <- function(x) { exp(x) } a1 <- 0 n1 <- 5 taylor(f1,a1,n1) ## [1] 0.008334245 0.041666573 0.166...
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DATA 605 Discussion 13
4.2 Excercise 3 h <- 1 dVdt <- 5 1cm r <- 1 cat(dVdt/(2* pi* r), "cm/s") ## 0.7957747 cm/s 10cm r <- 10 cat(dVdt/(2* pi* r), "cm/s") ## 0.07957747 cm/s 100cm r <- 100 cat(dVdt/(2* pi* r), "cm/s") ## 0.007957747 cm/s...
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DATA605 - discussion Week 14
8.8 Excercise 21-24 In Exercises 21 – 24, write out the first 5 terms of the Binomial series with the givenk-value. 21.k=½ 22.k= -1/2 23.k=1/3 24.k=4 Binomial Series : (1+x)^k = 1 + kx + (k(k-1)/2!) x^2 + (k(k-1)(k-2)/3!) x^3 + (k(k-1)(k-2)(k-3)/4!) x^4 + … x <-2 #Binomial k = 1/2 # Find the first 5 terms of the Binomial series for k = 1/2 ...
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Regression Analysis Life Expectancy
plot(x =df$TotExp , y= df$LifeExp) 1 model <- lm(LifeExp ~ TotExp, data=df) summary(model) ## ## Call: ## lm(formula = LifeExp ~ TotExp, data = df) ## ## Residuals: ## Min 1Q Median 3Q Max ## -24.764 -4.778 3.154 7.116 13.292 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (...
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