R Assignment 6

0.1 R Markdown

1 Introduction

This is the space where I’ll describe the data set and my team and the year…

Below are tables one and two that describe the overall summary for the 2025 season, as well as my team, NY Liberty. …

Table 1 – my team did make the playoffs, etc. … Table 2 –

#table for Team Summaries 2c
summary %>%
  kbl(digits = 2, caption = "Team Summaries") %>%
  kable_paper("hover", full_width = F, html_font = "Cambria") 

Team Summaries

team_name	mean_score	sd_score	mean_fgp	sd_fgp	mean_rebounds	sd_rebounds	mean_three	sd_three	mean_steals	sd_steals
Aces	85.52	9.56	45.27	5.82	33.78	5.88	35.27	7.08	6.80	2.67
Dream	76.93	10.59	41.28	6.78	35.95	4.41	30.83	9.32	7.14	2.82
Fever	84.50	10.17	45.56	5.38	35.10	5.49	35.00	8.99	5.88	2.29
Liberty	84.98	9.92	44.53	5.61	36.90	5.77	35.38	10.06	7.75	2.19
Lynx	82.36	11.39	45.21	6.34	33.15	5.06	37.80	9.43	8.36	3.17
Mercury	81.93	12.60	44.28	7.34	32.26	5.39	32.97	10.34	6.55	2.12
Mystics	79.30	8.69	43.36	4.82	31.85	4.66	36.64	8.69	7.28	2.24
Sky	77.40	9.62	42.44	5.22	36.60	5.57	31.74	11.62	7.00	3.30
Sparks	78.40	10.57	42.63	6.15	32.67	5.52	32.09	11.00	7.30	2.78
Storm	82.67	9.65	43.43	5.39	34.67	6.02	28.35	9.03	9.24	3.27
Sun	80.36	9.89	44.30	5.28	33.43	4.62	32.84	11.67	7.89	3.29
Wings	84.20	11.47	44.47	5.24	34.75	4.65	32.06	11.75	7.12	2.95

#table for My Team 2d
my_team_results %>%
  kbl(digits = 1, caption = "My Team Wins/Losses") %>%
  kable_paper("hover", full_width = F, html_font = "Cambria")

My Team Wins/Losses

team_winner	mean
FALSE	80.0
TRUE	86.5

2 Histograms

Histogram discussion… Here are side by side histograms for …

library(ggplot2)

p = df_hist %>% 
  ggplot(aes(x=team_score, fill= result)) + geom_histogram(color = "blue", alpha=0.6, position = 'identity') +
  scale_fill_manual(values=c( "purple", "lightblue")) 
  
boxplot(team_score ~ result, data = df_hist, 
        col = c('lightblue', 'lightgreen'),
        main = "Points scored by game result", 
        xlab = "Result", ylab = "Points")  

3 Models

\(~\)

After all models were run and model reduction was completed, my final model is team_score= 3.2455521+ 1.1788679 * field_goal_pct + 0.5183829 * total_rebounds + 0.285623 * three_point_field_goal_pct .

This model significantly predicts team score, f(3, 48) = 32.2409164, p,.00001, adjusted R^2= 0.6476021 .

	Dependent variable:
	team_score
field_goal_pct	1.179***
	(0.181)
total_rebounds	0.518***
	(0.151)
three_point_field_goal_pct	0.286***
	(0.101)
Constant	3.246
	(10.049)
Observations	52
R2	0.668
Adjusted R2	0.648
Residual Std. Error	5.887 (df = 48)
F Statistic	32.241*** (df = 3; 48)
Note:	p<0.1; p<0.05; p<0.01

hist(model3$residuals)

plot(model3$fitted.values, model3$residuals)

ols_plot_cooksd_bar(model3)

ols_plot_resid_lev(model3, threshold = 3)

I built this model to predict my team’s points for a game in which they archive the median value for each variable. The predicted team score is 84.6709881, with 95% confidence interval (83.0084105, 86.3335656)