grantbrown

plotNormal = function(lb, ub, mu = 0, sigma = 1)
{
    # Generate X an Y pairs for normal curve
    X = seq(mu - 4*sigma, mu + 4*sigma, 0.1)
    Y = dnorm(X, mean = mu, sd = sigma)
    # Plot the normal curve line, lablel axes and plot
    plot(X,Y, type = "l", main = "Normal PDF", xlab = "x", ylab = "Density(x)")
    # Calculate visible bounds (no use plotting tails that are off the screen)
    lb2 = min(max(lb, mu-4*sigma), min(ub, mu+4*sigma))
    ub2 = max(max(lb, mu-4*sigma), min(ub, mu+4*sigma))

grantbrown

BMI FatPct
27.2 34.6
23.7 39.5
24.9 41.3
32.2 33.3
36.8 43
20 32.3
29.5 31.5
29.3 36.5
44.6 41.3

grantbrown

# T Distribution Illustration 
TimeDelay = 0.5
plotT = function(df, add = FALSE)
{
    f = function(x)
    {
        return(dt(x, df))
    }
    curve(f, xlim = c(-6,6), ylim = c(0, 0.5), add = add, main = "T Distribution vs. Standard Normal",ylab = "Density")

grantbrown

# Functions to make SLR Confidence and Prediction Band Plots.
# Grant Brown, June 2013
# 171-162



# Internal function used by ConfidenceBandPlot and PredictionBandPlot
BasicRegressionPlot = function(X,Y, main = "", xlab = "",ylab = "", plt = TRUE, matchScale = FALSE)
{

grantbrown

grantbrown

# To illustrate the use of confidence bands, let's just make up 
# a data example. First, to ensure that everyone's simulated data
# looks the same in case you want to work together, let's tell R
# to use the same starting point for random number generation. 
# (don't worry about how this works)

set.seed(12345)

grantbrown

# Homework 3 R code template 

# Before doing anything else, get a copy of HeartDiseaseDiabetes.csv
# This file is available on the github site, and an excel version is 
# available on icon. Remember that to use the excel version, you need 
# to open it in excel or libre office and save a copy as a csv. 


# Once you have obtained a copy of the data set in csv format, take 
# note of where you've saved it. We need to tell R to use that folder 

grantbrown

IQ DI
73 38
82 43
84 36
88 34
89 30
91 23
98 26
98 32
101 19

grantbrown

grantbrown

# Change my working directory to wherever the data file is stored
setwd("/home/grantbrown/dev/171-162-Materials/Section4/Homework5/")

CancerData = read.csv("Homework5.csv", head = TRUE)

# Perform the regression:
MyMLR = lm(AllCancerMortalityCrude ~ GraduationRate + HospitalBeds + PctFarmCorn, data = CancerData)

# Take a look at the regression summary: