Lunch break: In a recent survey of 675 working Americans ages \( 25-34 \), the average weekly amount spent on lunch as \( \$ 43.34 \) with standard deviation \( \$ 2.63 \). The weekly amounts are approximately bell-shaped. Part: \( 0 / 3 \) Part 1 of 3 (a) Estimate the percentage of amounts that were less than \( \$ 40.71 \). Round the answer to one decimal place. Approximately

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?

The strongest finding from the data was that the top 10 finishes occurred as the top predictor of winnings among the drivers. The p-value of 0.0068 provides a significant relationship and its relatively high, \( t \)-statistics of 2.91 , compared to other variables. Poles, Wins, and Top 5 finishes were expected to give us more predictive power, but showering the low \( t \)-stats, suggesting weak or no true relationship to the winnings. Based on these findings while wins and poles are important for achieving overall success in NASCAR, they do not appear to directly drive winnings as strongly as top 10 finishes. To support this, the regression model demonstrated the coefficient for the top 10 finishes. The top 10 finish is associated with an increase of \( \$ 112,822 \) in winnings. The substantial contribution compared to the negligible coefficients for poles or wins, reinforces the importance of consistency over singular achievements.I 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 economics. Do you agree with their conclusions? Why or why not? -

A newsletter publisher believes that over \( 71 \% \) of their readers own a Rolls Royce. For marketing purposes, a potential advertiser wants to confirm this claim. After performing a test at the 0.02 level of significance, the advertiser decides to reject the null hypothesis. What is the conclusion regarding the publisher's claim? Answer 2 Points O There is sufficient evidence at the 0.02 level of significance that the percentage is over \( 71 \% \). - There is not sufficient evidence at the 0.02 level of significance to say that the percentage is over \( 71 \% \). Shortcuts

A manufacturer must test that his bolts are 1.00 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 64 randomly selected bolts off the assembly line, he calculates the sample mean to be 1.05 cm . He knows that the population standard deviation is 0.18 cm . Assuming a level of significance of 0.05 , 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 sufficient evidence at a 0.05 level of significance that the mean length of the bolts is different from 1.00 cm and the machines need to be recalibrated. We reject the null hypothesis and conclude that there is insufficient evidence at a 0.05 level of significance that the mean length of the bolts is different from 1.00 cm and the machines need to be recalibrated. We fail to reject the null hypothesis and conclude that there is sufficient evidence at a 0.05 level of significance that the mean length of the bolts is different from 1.00 cm and the machines need to be recalibrated. We fail to reject the null hypothesis and conclude that there is insufficient evidence at a 0.05 level of significance that the mean length of the bolts is different from 1.00 cm and the machines need to be recalibrated.

A newsletter publisher believes that less than \( 58 \% \) of their readers own a Rolls Royce. For marketing purposes, a potential advertiser wants to confirm this claim. After performing a test at the 0.05 level of significance, the advertiser decides to reject the null hypothesis. What is the conclusion regarding the publisher's claim? Answer 2 Points O There is sufficient evidence at the 0.05 level of significance that the percentage is less than \( 58 \% \). There is not sufficient evidence at the 0.05 level of significance to say that the percentage is less than \( 58 \% \).