Case 302 July in Multiplex

Case 302From this case, there are two types of errors, which the consortium can make. A grapheme I faulting is referred to as a false positive. A Type I error would be made when the null guesswork is get rid ofed when it should be accepted. This error may occur if the consortium defends any(prenominal) lawsuit against them if they are using 6% (6/ atomic number 6) as their check overing result. The results of the sample size of coke sight indicate that the percentage range is from 1. 35% to 10. 65%. The test results can be high than 10%, but actually it is lower.Therefore, if the consortium defends any lawsuit against them it is possible that a Type I Error can be made. The second type of error is a Type II Error, which is also cognise as false negative. A Type II error would be made when the election guess is rejected when it should be accepted. For this to occur, the consortium must make a decision to settle the case when the survey result shows a lower percentage tha n 10% but in reality it is actually higher than 10%. The only error the consortium should make is a Type II error because the alternative hypothesis was rejected.As previously stated, using a sample size of 100 shows that we would not reject the null hypothesis, in other words, this would mean to settle with Tommy. If we did not create a second hypothesis test using a sample size of three hundred, we would not have defended against Tommy in court and a Type II error would have been made. Size of simple Defend lawsuit Settlement 100 Type II Error Right decision 300 Right decision Type I Error Table 1 We have proven that 94% of the surveyed moviegoers indicated that they are satisfied that theater lead commercials before movie.Only 6% of the moviegoers opposed to watch commercials before movie. This statistical analysis validates that the consortium should desire to defend any lawsuit Tommy or any other unhappy moviegoer files. In this situation, a Type II error would have been mad e if we decided to base our analysis only on a sample size of 100. A larger sample size always depicts a much accurate display. Statistical Analysis H0 = 10% H1 10% 1st Same Size N 100 (sample size) p? 6/100 = . 06 Confidence Interval .06 1. 96 = . 0135 . 1065Test StatisticZ= = -1. 33, from bar Normal Distribution gameboard = P-value = . 0918 P-value (alpha) .0918 . 05 Since P-value (. 0918) is greater than alpha (. 05), we fail to reject the null hypothesis. 2nd Sample Size N 300 p? 18/300 = . 06 Confidence Interval .06 1. 96 = . 0331 . 0869 Test Statistic Z= = -2. 31 from Standard Normal Distribution table = P-value = . 0104 P-value alpha .0104 . 05 Since P-value (. 0107) is less than alpha (. 05), we reject the null hypothesis