Question

The forestfires dataset contains meteorological information and the area burned for 517 forest fires that occurred in montesinho natural park in portugal. the columns of interest are ffmc, dmc, dc, isi, temp, rh, wind, and rain. read in the file . create a new data frame x from the columns ffmc, dmc, dc, isi, temp, rh, wind, and rain, in that order. calculate the correlation matrix for the data in x. scale the data. use sklearn's pca function to perform four-component factor analysis on the scaled data. print the factors and the explained variance.

What is the closest predetermined manufacturing overhead rate based on machine-hours?
A) $4.87 per machine-hour
B) $5.78 per machine-hour
C) $4.87 per forming department machine-hour
D) $5.78 per finishing department machine-hour

Answer 1

Read and manipulate the forestfires dataset by creating a data frame, calculating the correlation matrix, scaling the data, performing PCA, and printing the factors and explained variance. Additionally, the unrelated cost calculation is indicative of cost accounting methods.

Step-by-step explanation:

To address the student's question, it is essential to first read in the forestfires dataset which contains various meteorological variables and information about the area burned in forest fires. From this dataset, a new data frame X should be created with the specified columns (ffmc, dmc, dc, isi, temp, rh, wind, and rain) in the mentioned order. After creating X, the next step is to calculate the correlation matrix for these columns to understand the relationships between them. Following this, the data should be scaled to ensure equal importance during analysis. Lastly, a four-component factor analysis is performed on the scaled data using the PCA function from sklearn, and the results including the factors and the explained variance need to be printed.The later part of the question seems to be about calculating a predetermined manufacturing overhead rate based on machine-hours, which appears unrelated to the earlier steps involving the dataset.

The provided example calculations indicate a difference in cost between two jobs resulting from a difference in required machine hours. This calculation is not directly related to the dataset analysis but rather seems to pertain to cost accounting or cost analysis.To solve this problem, we need to follow the given steps:Read in the forestfires dataset and create a new data frame 'x' with the specified columns.Calculate the correlation matrix for the data in 'x'.Scale the data in 'x'.Use sklearn's PCA function to perform four-component factor analysis on the scaled data.Print the factors and the explained variance.The closest predetermined manufacturing overhead rate based on machine-hours is not related to the forestfires dataset. So, we cannot determine the manufacturing overhead rate based on the given information.