primaryobjects

#
# [2016-03-28] Challenge #260 [Easy] Garage Door Opener
# https://www.reddit.com/r/dailyprogrammer/comments/4cb7eh/20160328_challenge_260_easy_garage_door_opener/
# Demo at http://www.r-fiddle.org/#/fiddle?id=83RtwcPP&version=1
#
# Kory Becker 4/3/2016
# http://primaryobjects.com
#

# Valid states and commands.

primaryobjects

#
# [2016-03-21] Challenge #259 [Easy] Clarence the Slow Typist
# https://www.reddit.com/r/dailyprogrammer/comments/4bc3el/20160321_challenge_259_easy_clarence_the_slow/
# Demo at http://www.r-fiddle.org/#/fiddle?id=JwY7VC2k&version=1
#
# Kory Becker 4/3/2016
# http://primaryobjects.com
#

keypad <- data.frame(c(1, 4, 7, 10),

primaryobjects

#
# [2016-04-04] Challenge #261 [Easy] verifying 3x3 magic squares
# https://www.reddit.com/r/dailyprogrammer/comments/4dccix/20160404_challenge_261_easy_verifying_3x3_magic/
# Demo at http://www.r-fiddle.org/#/fiddle?id=AgVxiDgU&version=1
#
# Kory Becker 4/4/2016
# http://primaryobjects.com
#

magicSquare <- function(input) {

primaryobjects

CommonManager = {
    wordToNumber: function(word, arr) {
        var dict = {
            'first': 1,
            'second': 2,
            'third': 3,
            'fourth': 4,
            'fifth': 5,
            'sixth': 6,
            'seventh': 7,

primaryobjects

---
title: "Single Variable Linear Regression R^2"
output: html_document
---

The following figure shows three data points and the best fit line:
y = 3x + 2.

The x-coordinate, or "x", is our independent variable and the y-coordinate, or "y", is our dependent variable.

primaryobjects

NBA <- read.csv('nba_train.csv')

# Seems around 42 wins, the team has a good chance of making it to the playoffs.
table(NBA$W, NBA$Playoffs)

NBA$PTSdiff <- NBA$PTS - NBA$oppPTS
WinsReg <- lm(W ~ PTSdiff, data = NBA)

PointsReg <- lm(PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + TOV + STL + BLK, data=NBA)
SSE <- sum(PointsReg$residuals^2)

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data <- read.csv('climate_change.csv')
train <- subset(data, Year <= 2006)
test <- subset(data, Year > 2006)

fit <- lm(Temp ~ MEI + CO2 + CH4 + N2O + CFC.11 + CFC.12 + TSI + Aerosols, data=train)

fit2 <- lm(Temp ~ MEI + TSI + Aerosols + N2O, data=train)

# Auto-create model (note, step does not address the collinearity of the variables, so some features removed in fit2 are present in this model).
fit3 <- step(fit)

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train <- read.csv('pisa2009train.csv')
test <- read.csv('pisa2009test.csv')

tapply(train$readingScore, train$male, mean)

# Which columns have an NA value?
which(unlist(lapply(train, function(x) any(is.na(x)))))

# Remove missing values.
train <- na.omit(train)

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train <- read.csv('flutrain.csv')
test <- read.csv('flutest.csv')

train[order(train$ILI),]
train[order(train$Queries),]

hist(train$ILI)

plot(log(train$ILI), train$Queries)

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---
title: "Logistic Regression Intro"
output: html_document
---

Suppose the coefficients of a logistic regression model with two independent variables are as follows:
B0 = -1.5
B1 = 3
B2 = -0.5