A manufacturer must test that his bolts are 2.00 cm long when they come off the assembly line. He must recalibrate his machines if the bolts are too long or too short. After sampling 169 randomly selected bolts off the assembly fine, he calculates the sample mean to be 2.05 cm . He knows that the population standard deviation is 0.29 cm . Assuming a level of significance of 0.01 , is there sufficient evidence to show that the manufacturer needs to recalibrate the machines? Step 3 of 3 : Draw a conclusion and interpret the decision. Answer 2 Points We reject the null hypothesis and conclude that there is insufficient evidence at a 0.01 level of significance that the mean length of the bolts is different from 2.00 cm and the machines need to be recalibrated. We fail to reject the null hypothesis and conclude that there is insufficient evidence at a 0.01 level of significance that the mean length of the bolts is 2.00 cm and the machines need to be recalibrated. We fail to reject the null hypothesis and conclude that there is sufficient evidence at a 0.01 level of significance that the mean length of the bolts is different from 2.00 cm and the machines need to be recalibrated. We reject the null hypothesis and conclude that there is sufficient evidence at a 0.01 level of significance that the mean length of the bolts is different from 2.00 cm and the machines need to be recalibrated.

What is a scatter plot?

61. Mode is the value with maximum frequency in the data. 62. Fermions are classified as Leptons and Baryons. 63. A muxture in which the composition is not uniform throughout is called heterogeneous mixture 64. The arithmetic mean of first ' \( n \) ' natural numbers is \( \frac{n}{2} \) ' 65. Symmetric encryption is slower than asymmetric encryption. 66. \( |7|=0 \)

Some surprising trends emerged in reviewing the data to predict NASCAR drivers' earnings. It might seem natural to assume that winning races is the most significant factor in earnings. Still, the analysis showed that consistent performance-finishing in the Top 10 -is the best predictor. The top 10 finishes explained \( 80.6 \% \) of the variation in winnings, making them the most reliable indicator. |The combined regression model, which used all four factors (Poles, Wins, Top 5 finishes, and Top 10 finishes), explained \( 82 \% \) of the variation in winnings. Of these, the Top 10 finishes had the most significant impact, with each additional finish adding approximately \( \$ 117,100 \) to a driver's earnings. Surprisingly, poles and wins didn't show much significance compared to placement consistency. Based on the managerial analysis report, teams should focus on achieving steady, high-level performance to secure consistent Top 10 finishes. Some key steps include prioritizing vehicle reliability, improving pit stop efficiency, and fine-tuning race strategies. While winning races is still exciting and valuable, focusing solely on wins or poles may not maximize earnings. Future studies might help to include other factors, like sponsorship deals or drivers' average placements across races, for a more complete picture of what drives financial success in NASCAR. Review your peer's post. In a minimum of 100 words each, respond in a substantive manner, and provide information that they may have missed or may not have considered . regarding the inferences that can be drawn from the application of Multiple Regression in business and econiomics. Do you agree with their conclusions? Why or why not?

15. Suppose data collected for heights (in cms) of 390 cows were tabulated in a frequency distribution and the following results were obtained. fi: \( 6,25,48,72,116,60,38,22,3 \) CMI \( =112, \mathrm{CM} 2=117 \) where CMi ith class mark Determine: a) Class interval size (class width), Class limits, Class boundaries and class marks b) The less than cumulative frequency distribution c) The class intervals having the highest frequency d) Above which height do we find \( 50 \% \) of the cows? c) Below which height do we get \( 25 \% \) of the cows? Draw a) histogram b) a frequency polygon c) C. a less than ogive for the above data

What is a scatter plot?