Most Australian youth-sports leagues separate athletes by birthdate, and the cutoff date is January 1st. Thus, those children born in January and February have some physical advantages in youth sports over those born in November and December. A recent studysuggests that those physical advantages enjoyed early in life impact the likelihood of a child becoming a professional athlete. The table gives the number of Australian-born 2009 Australian Football League players born in different months of the year, as well as the proportion of births expected if the birthdates of the athletes matched the distribution of all births nationally. Is there evidence that the distribution of birthdates of AFL athletes is not the same as the distribution of birthdates nationally?   Birthdates nationally in Australia and for Australian football players   Proportion Actual for Months Nationally AFL players Jan-Mar 0.248 196 Apr-June 0.251 162 Jul-Sep 0.254 137 Oct-Dec 0.247 122

Is it defense or offense that wins football games? The accompanying data file includes a team’s winning record (Win in %), the average number of yards gained, and the average number of yards allowed during a recent NFL season. Team Win Yards_Gained Yards_Allowed Arizona Cardinals 46.9 366.8 305.2 Atlanta Falcons 68.8 415.8 371.2 Baltimore Ravens 50.0 347.7 322.1 Buffalo Bills 43.8 354.1 357.0 Carolina Panthers 37.5 343.7 359.8 Chicago Bears 18.8 356.5 346.8 Cincinnati Bengals 40.6 356.9 350.8 Cleveland Browns 6.2 311.0 392.4 Dallas Cowboys 81.2 376.7 343.9 Denver Broncos 56.2 323.1 316.1 Detroit Lions 56.2 338.8 354.8 Green Bay Packers 62.5 368.8 363.9 Houston Texans 56.2 314.7 301.3 Indianapolis Colts 50.0 364.4 382.9 Jacksonville Jaguars 18.8 334.9 321.7 Kansas City Chiefs 75.0 343.0 368.5 Miami Dolphins 25.0 262.7 337.0 Minnesota Vikings 62.5 332.8 382.6 New England Patriots 50.0 315.1 314.9 New Orleans Saints 87.5 386.2 326.4 New York…

A psychologist at a private mental health facility was asked to determine whether there was a clear difference in the length of stay for patients with different categories of diagnosis. Looking at the last seven patients in each of the three major categories, the results (in terms of weeks of stay) were as follows. Using the data below, test whether the length of stay for patients vary based on category of diagnosis.  Diagnosis Category  Affective Disorders  Cognitive Disorders  Drug-related Conditions  7 12 8 6 8 10 5 9 12 6 11 10 9 11 9 5 10 11 6 9 12 The above scenario requires a one-way Anova, with a bar graph. Question - I need to write an explanation for the output given. I am to include a statistical notation and explanation as to whether the results are significant or not significant.  Examples of Explanation: Non-significant:A One-Way ANOVA was conducted to examine whether a preceding situation (watching a…

Education influences attitude and lifestyle. Differences in education are a big factor in the "generation gap." Is the younger generation really better educated? Large surveys of people age 65 and older were taken in n1 = 38 U.S. cities. The sample mean for these cities showed that x1 = 15.2% of the older adults had attended college. Large surveys of young adults (age 25 - 34) were taken in n2 = 37 U.S. cities. The sample mean for these cities showed that x2 = 18.1% of the young adults had attended college. From previous studies, it is known that ?1 = 6.4% and ?2 = 4.8%. Does this information indicate that the population mean percentage of young adults who attended college is higher? Use ? = 0.05. What is the value of the sample test statistic? (Test the difference ?1 − ?2. Round your answer to two decimal places.)=__(c) Find (or estimate) the P-value. (Round your answer to four decimal places.)=__