Assignment 6 STA319 Spring 2026

1 introduction

This is the space where I’ll describe the data set and my team and the year. So on and so forth we go. Below are tables 1 and 2 that describe the overall summary and stats for the overall season as well as my team. (rephrase in own words).

2 Tables/Season Summary

summary %>%
  kbl(caption = "team score summary", digits = 2) %>%
  kable_classic(full_width = F)

team score summary

team_name	mean_score
Aces	85.52
Dream	76.93
Fever	84.50
Liberty	84.98
Lynx	82.36
Mercury	81.93
Mystics	79.30
Sky	77.40
Sparks	78.40
Storm	82.67
Sun	80.36
Wings	84.20

my_team %>%
  kbl(caption = "Wings Win Summary", digits = 2) %>%
  kable_classic(full_width = F)

Wings Win Summary

team_score	field_goal_pct	total_rebounds	three_point_field_goal_pct	steals	team_name	team_winner
84	42.9	33	45.0	7	Wings	FALSE
109	56.8	33	45.8	7	Wings	FALSE
81	38.5	42	22.2	9	Wings	FALSE
67	36.6	33	22.2	4	Wings	FALSE
91	47.2	28	25.0	3	Wings	FALSE
77	40.5	34	20.0	6	Wings	FALSE
96	49.3	33	40.0	5	Wings	FALSE
86	43.1	44	22.2	3	Wings	FALSE
93	39.2	34	41.9	8	Wings	FALSE
94	48.2	38	38.9	10	Wings	TRUE
93	48.0	40	28.6	4	Wings	TRUE
113	52.6	34	36.4	9	Wings	TRUE
71	43.5	38	15.4	4	Wings	FALSE
74	37.9	28	42.9	7	Wings	FALSE
91	43.9	36	41.7	12	Wings	FALSE
101	51.3	29	45.0	10	Wings	TRUE
81	43.7	33	31.3	6	Wings	FALSE
84	50.0	26	36.8	5	Wings	FALSE
85	46.1	37	30.0	6	Wings	FALSE
85	44.9	27	33.3	16	Wings	TRUE
96	49.3	34	29.4	6	Wings	FALSE
71	41.9	31	23.5	11	Wings	FALSE
76	45.7	32	33.3	12	Wings	FALSE
94	48.7	36	60.0	9	Wings	TRUE
84	41.2	41	39.3	6	Wings	FALSE
69	39.4	36	20.8	6	Wings	FALSE
72	35.4	35	34.6	9	Wings	FALSE
78	47.9	34	41.2	5	Wings	FALSE
67	37.7	29	6.3	3	Wings	FALSE
84	47.7	28	46.2	6	Wings	FALSE
90	47.4	36	26.1	9	Wings	FALSE
72	39.7	35	11.8	7	Wings	FALSE
81	37.8	40	20.0	5	Wings	FALSE
76	46.2	34	36.4	12	Wings	FALSE
72	51.6	30	37.5	3	Wings	FALSE
84	42.1	44	16.7	5	Wings	TRUE
107	52.0	38	50.0	10	Wings	TRUE
78	42.9	35	12.5	6	Wings	FALSE
74	34.8	40	38.9	7	Wings	FALSE
87	45.1	42	33.3	7	Wings	TRUE

3 Histograms

below are my histograms and here is a great and wonderful discussion, through a few sentences, on all they have to offer. looking at winning vs losing and now here they are so special and great, right?

#i refuse to have a 6-7 joke in my coding. you cannot make me
p <- data %>%
  ggplot(aes(x=team_score, fill = team_winner)) +
    geom_histogram(color = "#e9ecef", alpha=0.6, position = 'identity') +
    scale_fill_manual(values=c("#002b5c", "#c4d600")) 

boxplot(team_score ~ result, data = df_hist, notch = TRUE, 
        col = c("#002b5c", "#c4d600"),
        main = "Points score by game result",
        xlab = "Result", ylab = "Points")

## Warning in (function (z, notch = FALSE, width = NULL, varwidth = FALSE, : some
## notches went outside hinges ('box'): maybe set notch=FALSE

4 Models

5 Interaction Models

Call: lm(formula = team_score ~ field_goal_pct + total_rebounds + three_point_field_goal_pct + field_goal_pct * total_rebounds + field_goal_pct * three_point_field_goal_pct + total_rebounds * three_point_field_goal_pct, data = my_team)

Residuals: Min 1Q Median 3Q Max -20.0621 -2.8317 0.2787 3.8935 16.3294

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 22.297634 95.796054 0.233 0.817 field_goal_pct 0.800250 2.399886 0.333 0.741 total_rebounds -0.237118 2.587190 -0.092 0.928 three_point_field_goal_pct -0.034367 1.249697 -0.028 0.978 field_goal_pct:total_rebounds 0.016969 0.063750 0.266 0.792 field_goal_pct:three_point_field_goal_pct 0.001972 0.023458 0.084 0.934 total_rebounds:three_point_field_goal_pct 0.006036 0.027642 0.218 0.829

Residual standard error: 7.467 on 33 degrees of freedom Multiple R-squared: 0.6414, Adjusted R-squared: 0.5762 F-statistic: 9.836 on 6 and 33 DF, p-value: 3.182e-06

\(~\)

After after running all models and reducing models, my final model is team_score= -12.3047153 + 1.4462277 * field_goal_pct + 0.6871829 * total_rebounds + 0.2593569 * three_point_field_goal_pct.

This model significantly predicts team score, F(3, 36) = 21.2378708, p<.0001, 0.6088799.

See table below for results:

	Dependent variable:
	team_score
field_goal_pct	1.446***
	(0.253)
total_rebounds	0.687**
	(0.255)
three_point_field_goal_pct	0.259**
	(0.112)
Constant	-12.305
	(14.869)
Observations	40
R2	0.639
Adjusted R2	0.609
Residual Std. Error	7.173 (df = 36)
F Statistic	21.238*** (df = 3; 36)
Note:	p<0.1; p<0.05; p<0.01

6 Residuals

#residual plots of a variety of times
ols_plot_resid_fit(model4)

ols_plot_resid_hist(model4)

ols_plot_resid_stud(model4)

ols_plot_resid_lev(model4, threshold = 3)

ols_plot_cooksd_chart(model4)

7 Prediction

I built this model to predict my team’s points for a game in which they achieve the median value for each variable. The predicted team score is 83.908595, with 95% confidence interval (81.5603447, 86.2568453).