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The image contains a problem related to linear regression and confidence intervals. The problem provides data for "Book Value (X)" and "Audited Value (Y)" of ten inventory items and asks the following:

1. Fit a linear regression model \( Y = \alpha + \beta X \) for the provided data.
2. Obtain a 95% confidence interval for the slope parameter (\(\beta\)).
3. Find the predicted mean value of \( Y \) for \( X = 100 \) with a 95% confidence interval.
4. Find the book value \( X \) for which the 95% confidence interval for \( Y \), the predicted minimum length, occurs.
5. Calculate the coefficient of determination ( \( R^2 \) ) and interpret it.

Given the dataset provided in the image, we can proceed to answer each question by applying linear regression techniques. Would you like me to compute the regression model and confidence intervals using the data provided?

Auditors are often required to compare the audited (or current) value of an inventory item with the book (or listed) value. If a company is keeping its inventory and books up to date, there should be a strong linear relationship between the audited and book values. An accountant intends to fit a linear regression model. He sampled ten inventory items and obtained the audited and book values shown in the table.

Learn how to fit a linear regression model to compare audited and book values of inventory items. This problem involves computing a 95% confidence interval for the slope parameter, predicting values for specific book values, and determining the coefficient of determination (R^2) with interpretation. Suitable for undergraduate students studying statistics or business analytics.

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