The code you provided is almost correct for performing a chi-squared goodness-of-fit test to assess the adequacy of the fit of the two-component normal mixture model with common variances to the n = 75 data points. However, there are a few errors that need to be corrected. Here is the corrected code:

library(mclust)
# 读取 CSV 文件中的数据
csv_data = read.csv("A1_2023a_V2.csv")
# 提取列数据为向量
column_vector = csv_data$x
# 将列数据转换为矩阵（以一列形式）
column_matrix = matrix(column_vector, ncol = 1)

# Fit the model
model = Mclust(column_matrix, G=2, modelNames = "E")
summary(model)

# Calculate observed and expected frequencies
observed = table(model$classification)
expected = model$parameters$pro * length(column_vector)

# Perform chi-squared test
chi_square = sum((observed - expected)^2 / expected)
df = length(observed) - 1 - model$parameters$df
p_value = 1 - pchisq(chi_square, df)

# Print results
print(paste("Chi-Squared Statistic:", chi_square))
print(paste("Degrees of Freedom:", df))
print(paste("P-Value:", p_value))

This code first loads the necessary library and reads the data from the CSV file. It then fits a two-component normal mixture model with common variances to the data using the Mclust function. The observed frequencies are calculated by tabulating the classifications from the model, and the expected frequencies are calculated by multiplying the proportions from the model by the total number of data points. The chi-squared statistic is then calculated and the degrees of freedom are calculated by subtracting 1 and the number of parameters estimated in the model from the number of observed frequencies. Finally, the p-value is calculated and the results are printed.