library(tidyverse)
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library(ggplot2)
library(dbplyr)
## 
## Attaching package: 'dbplyr'
## 
## The following objects are masked from 'package:dplyr':
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##     ident, sql
library(ggthemes)
library(ggrepel)
library(ggrepel)
library(broom)
library(lindia)
week3_dataset <- read.csv("baseball.csv",header = TRUE)
model <- glm(Playoffs ~ W, data = week3_dataset,
             family = binomial(link = 'logit'))
model$coefficients
## (Intercept)           W 
##  -34.206104    0.372348
sigmoid <- \(x) 1 / (1 + exp(-(-34.206 + 0.372 * x)))

week3_dataset %>%
  ggplot(mapping = aes(x = W, y = Playoffs)) +
  geom_jitter(width = 0, height = 0.1, shape = 'O', size = 3) +
  geom_function(fun = sigmoid, color = 'blue', linewidth = 1)+
  labs(title = "Modeling a Binary Response with Playsoff teams and Wins") +
  scale_y_continuous(breaks = c(0, 1)) +
  theme_minimal()

0 = -34.206 + 0.372* W

w = 34.206/0.372
w
## [1] 91.95161

w = 91.95 This means that when a team wins 91 games, then then have a 50/50 chance of making the playoffs