**What Are the Elements That the Production Manager Should Consider in Determining His Company’s Ability to Produce Chips That Meet Specifications?**

**custom essay**

specifically for you

specifically for you

for only $16.05

**$11/page**

To identify the company’s ability to produce the chips of the designated size, the production manager should consider the data such as the p-value and the confidence coefficient. The former will help identify the veracity of the null hypothesis; in other words, in the instances that presuppose a 0.95 p-value, the possibility for the production of the chips of the required value can be deemed as very high. Herein the significance of the p-value lies. Along with the above-mentioned element, the mean value should be considered. It is essential to identify the average number of software items so that the paradigm of their production could be located. Last but not least, the value itself should be located; in other words, the length of the items produced needs to be calculated so that the premises for calculating the mean could be created (Black, 2011).

**Do the Chips Produced Meet the Desired Specifications?**

Seeing that the confidence interval for the hardware produced by the company equals 95% for the specified value, it can be assumed that 95% of the goods produced by the firm meet the specified parameters of 9 to 1.1 cm as a mean value. On the one hand, one might argue that the designated length does not comply with the standards set by the company. Indeed, the maximum value listed above (i.e., 1.1 cm) is 0.1 cm lower than the required one (i.e., 1.2 cm). On the other hand, one must bear in mind that the data provided is labeled as the mean value; in other words, the range in question ([9; 1.1], that is) is the average of the length of the tools produced. Hence, approximately 50% of the software created by the organization reaches the average of 1.1. Seeing that the mean is defined as the average of the lengths combined, it will be reasonable to assume that at least 50% of the products of the average of 1.1 cm are 1.2 cm and longer. In other words, 25% of the software tools produced are at least 1.2 cm long, which aligns with the instructions provided by the organization. Therefore, it can be assumed that the products manufactured by the staff meet the standards established by the firm (Anderson, Sweeney, & Williams, 2014).

**What Reasons Should the Production Manager Provide to the Vice-President to Justify That the Production Team is Meeting Specifications?**

As it has been stressed above, the emphasis should lie on the fact that the average value of the chips’ lengths was calculated in order to analyze the data. In other words, the actual length of each chip may exceed the mean value provided in the course of the calculations. Herein the need to continue the current strategy in chips production lies; as soon as the production process increases so that the average length could be increased, the number of items that exceed the required 1.2 will increase, hence, affecting the sales to a considerable extent (Jani, 2014). In other words, the manager should provide the above-mentioned information regarding the p-value for the products of the designated length. Particularly, the fact that the p-value for the chips, the average length of which ranges from 9 to 1.1 cm, reaches 95% deserves to be mentioned as the key argument. In addition, a strong emphasis should be placed on the fact that the specified information concerns the average length.

**How Will This Decision Impact the Chip Manufacturer’s Sales and Net Profit?**

**100% original paper**

on any topic

on any topic

done in as little as

**3 hours**

It is assumed that the decision taken by the company will prevent the chip manufacturer’s sales from shrinking. If unnecessary changes are introduced to the production process, the quality of the product and, therefore, customers’ satisfaction rates, will drop. Hence, it is important that the right decision should be made.

## Reference List

Anderson, D., Sweeney, D., & Williams, T. (2014). *Statistics for business & economics, revised*. Boston, Massachusetts: Cengage Learning.

Black, K. (2011). *Business statistics: For contemporary decision making*. New York City, New York: John Wiley & Sons.

Jani, P. N. (2014). *Business statistics: Theory and applications*. New Delhi: PHI Learning Pvt. Ltd.