4. В 1 классе 20 мальчика, во 2 - 17 , в 3 - 16, в 4 - 19 . Постройте круговую диаграмму количества мальчиков в начальной школе по классам.

How do you identify the relationship between two variables in a scatter plot?

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?

Frise 15.3 Find the lower quartile, \( Q_{1} \), the upper quartile, \( Q_{3} \), and data sets: Note: You should first rank the data. \( \begin{array}{ll}\text { (i) }\{2,5,7,3,3,2,8\} & \text { (ii) } \\ \text { (ii) }\{5,8,6,4,5,3,4,12\} & \text { (iv) }\{8,7,8,7,6,5,6,5,4,3,4,3\} \\ \text { Leaf }\end{array} \)

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 \( 58 \% \).

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.