Hallar el intervalo de confianza de la variable accuracy

# Leer los datos
# archivo <- file.choose()
archivo <- '/Users/pctm/Documents/EDA/Dataset_IA_corte_II.xlsx'
data <- read_excel(archivo, col_names = TRUE)

# Crear la tabla y aplicar estilos
kable(data[1:20,], caption = "Algoritmos de inteligencia artificial utilizando frameworks de python") %>%
  kable_styling(full_width = TRUE) %>%
  scroll_box(width = "900px", height = "500px")

Algoritmos de inteligencia artificial utilizando frameworks de python

Algorithm	Framework	Problem_Type	Dataset_Type	Accuracy	Precision	Recall	F1_Score	Training_Time	Date
SVM	Scikit-learn	Regression	Time Series	0.6618051	0.6929447	NA	0.4426950	4.9785924	2023-03-08 11:26:21
K-Means	Keras	Clustering	Time Series	0.7443216	0.4900292	0.8766533	0.4414046	NA	2023-03-09 11:26:21
Neural Network	Keras	Clustering	Image	0.8852037	0.5948056	0.9685424	0.9644707	3.2825938	2023-03-10 11:26:21
SVM	Keras	Clustering	Text	0.8416477	0.8424142	0.8748388	0.7041523	4.0416289	2023-03-11 11:26:21
SVM	Scikit-learn	Regression	Tabular	0.7229514	0.6856109	0.3010956	0.6456472	3.6039908	2023-03-12 11:26:21
K-Means	PyTorch	Regression	Image	0.6368133	0.6255330	7.4548096	0.8865271	3.0064753	2023-03-13 11:26:21
Neural Network	PyTorch	Regression	Text	0.9985623	0.6366858	0.3357948	0.9014956	NA	2023-03-14 11:26:21
Neural Network	Scikit-learn	Regression	Image	0.7130907	0.6756681	0.4803251	0.5993146	2.3283453	2023-03-15 11:26:21
SVM	Keras	Regression	Time Series	NA	0.8710099	0.3416673	0.8161708	3.4064529	2023-03-16 11:26:21
Random Forest	Keras	Regression	Text	0.5818119	0.9352508	NA	0.8626737	3.4199049	2023-03-17 11:26:21
SVM	PyTorch	Regression	Image	0.8974048	9.7320081	0.7806129	0.7927904	1.9283008	2023-03-18 11:26:21
SVM	Keras	Clustering	Image	0.8468411	0.8721420	0.3801413	0.4909570	4.7142907	2023-03-19 11:26:21
SVM	TensorFlow	Clustering	Tabular	0.6103848	0.5892441	0.5686872	0.9255299	0.9200495	2023-03-20 11:26:21
SVM	PyTorch	Clustering	Image	0.5411905	0.8128808	0.6193656	0.7234567	2.5517613	2023-03-21 11:26:21
K-Means	Keras	Clustering	Text	0.8402497	0.6625619	0.5583371	0.5694835	3.4853315	2023-03-22 11:26:21
Neural Network	PyTorch	Regression	Text	NA	0.5528024	0.3847175	0.6551369	3.5159654	2023-03-23 11:26:21
K-Means	TensorFlow	Classification	Tabular	0.6366298	0.9045229	0.5932635	0.4225427	3.2783309	2023-03-24 11:26:21
K-Means	PyTorch	Regression	Text	0.9754318	0.4230558	0.8258246	0.4767201	1.4489122	2023-03-25 11:26:21
K-Means	PyTorch	Classification	Time Series	0.5755289	0.9410572	0.3497054	0.8593281	0.8654122	2023-03-26 11:26:21
SVM	PyTorch	Clustering	Text	0.7161674	0.6768865	0.3561260	0.4000070	3.2161076	2023-03-27 11:26:21

t.test(data$Accuracy, conf.level = 0.95)

## 
##  One Sample t-test
## 
## data:  data$Accuracy
## t = 21.246, df = 520, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
##  0.7966857 0.9590306
## sample estimates:
## mean of x 
## 0.8778581

#SIN FUNCION
media_muestra <- mean(data$Accuracy, na.rm = TRUE)
media_muestra

## [1] 0.8778581

desv_est <- sd(data$Accuracy, na.rm = TRUE)
desv_est

## [1] 0.9431208

n <- sum(!is.na(data$Accuracy))
n

## [1] 521

nivel_confianza <- 0.955
gl <- n - 1

t_critico <- qt(1 - (1 - nivel_confianza) / 2, gl)
t_critico

## [1] 2.009503

error_estandar <- desv_est / sqrt(n)
error_estandar

## [1] 0.04131887

limite_inferior <- media_muestra - t_critico * error_estandar
limite_superior <- media_muestra + t_critico * error_estandar

limite_inferior

## [1] 0.7948277

limite_superior

## [1] 0.9608885