Slidify
Data meets presentation
Rafael Reséndiz, Proffesor of Educative Assesment
UABC, Xochicalco
Reproducible Pitch Presentation
1. Knowing "Data meets presentation"
Load files and datasets and and have fun with them
data(iris) # Load data
lm(iris) # Make a lineal regression
##
## Call:
## lm(formula = iris)
##
## Coefficients:
## (Intercept) Sepal.Width Petal.Length
## 2.1713 0.4959 0.8292
## Petal.Width Speciesversicolor Speciesvirginica
## -0.3152 -0.7236 -1.0235
2. Statistical inference and simulation
Setting the global options Example: The exponential distribution
lambda = 0.2 # lambda for all simulations
n = 20 # number of exponential random variables
nsims = 1:100 # number of simulated averages
set.seed(1)
means <- data.frame(x=sapply(nsims,function(x) {mean(rexp(n,lambda))})) # necessary means
head(means,2) # show the results
## x
## 1 5.446627
## 2 4.274117
str(means) # See a summary of the results
## 'data.frame': 100 obs. of 1 variable:
## $ x: num 5.45 4.27 5.17 6.76 4.12 ...
Statistical inference in the real life
fit <- lm(iris) # Work with real data
confint(fit) # Identify intercepts and others
## 2.5 % 97.5 %
## (Intercept) 1.6182321 2.72430044
## Sepal.Width 0.3257653 0.66601260
## Petal.Length 0.6937939 0.96469395
## Petal.Width -0.6140049 -0.01630542
## Speciesversicolor -1.1982739 -0.24885002
## Speciesvirginica -1.6831329 -0.36386273
summary(fit$residuals)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.794200 -0.218700 0.008987 0.000000 0.202500 0.731000
Data basic plot
Plot your results and identify your creativity
