3) A factory manufactures laptops. The weight of laptop follows normal distribution N (µ, σ²) independently. For quality control, an inspector sampled 4 laptops randomly and measured their weights. The measured weights are as follows in kilograms: 3.280, 1.503, 3.322, 1.502 You may assume that the smoothness condition holds. (a) Find an exact 90% confidence interval of µ. (b) Find an exact 90% confidence interval of σ². (c) The factory aims to control the average weight of the manufactured laptop to be 1kg. Based on the weights of 4 sampled laptops, test whether the factory has successfully controlled the average weight at significance level of 5%. State the null hypothesis, the alternative hypothesis, the rejection region, and the test result. (d) Find the efficiency of the sample variance S2 assume µ is known. = n³¹¹²²±1 (Xi - X)². You may n- i=1
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- To compare the dry braking distances from 30 to 0 miles per hour for two makes of automobiles, a safety engineer conducts braking tests for 35 models of Make A and 35 models of Make B. The mean braking distance for Make A is 43 feet. Assume the population standard deviation is 4.6 feet. The mean braking distance for Make B is 47 feet. Assume the population standard deviation is 4.2 feet. At α=0.10, can the engineer support the claim that the mean braking distances are different for the two makes of automobiles? Assume the samples are random and independent, and the populations are normally distributed. Complete parts (a) through (e).To compare the dry braking distances from 30 to 0 miles per hour for two makes of automobiles, a safety engineer conducts braking tests for 35 models of Make A and 35 models of Make B. The mean braking distance for Make A is 43 feet. Assume the population standard deviation is 4.6 feet. The mean braking distance for Make B is 46 feet. Assume the population standard deviation is 4.5 feet. At α=0.10, can the engineer support the claim that the mean braking distances are different for the two makes of automobiles? Assume the samples are random and independent, and the populations are normally distributed. The critical value(s) is/are Find the standardized test statistic z for μ1−μ2.To compare the dry braking distances from 30 to 0 miles per hour for two makes of automobiles, a safety engineer conducts braking tests for 35 models of Make A and 35 models of Make B. The mean braking distance for Make A is 42 feet. Assume the population standard deviation is 4.5 feet. The mean braking distance for Make B is 46 feet. Assume the population standard deviation is 4.4 feet. At α=0.10, can the engineer support the claim that the mean braking distances are different for the two makes of automobiles? Assume the samples are random and independent, and the populations are normally distributed. Complete parts (a) through (e).
- To compare the dry braking distances from 30 to 0 miles per hour for two makes of automobiles, a safety engineer conducts braking tests for 35 models of Make A and 35 models of Make B. The mean braking distance for Make A is 40 feet. Assume the population standard deviation is 4.8 feet. The mean braking distance for Make B is 43 feet. Assume the population standard deviation is 4.5 feet. At α=0.10, can the engineer support the claim that the mean braking distances are different for the two makes of automobiles? Assume the samples are random and independent, and the populations are normally distributed. Complete parts (a) through (e).To compare the dry braking distances from 30 to 0 miles per hour for two makes of automobiles, a safety engineer conducts braking tests for 35 models of Make A and 35 models of Make B. The mean braking distance for Make A is 44 feet. Assume the population standard deviation is 4.9 feet. The mean braking distance for Make B is 46 feet. Assume the population standard deviation is 4.7 feet. At α=0.10, can the engineer support the claim that the mean braking distances are different for the two makes of automobiles? Assume the samples are random and independent, and the populations are normally distributed. Complete parts (a) through (e).To compare the dry braking distances from 30 to 0 miles per hour for two makes of automobiles, a safety engineer conducts braking tests for 35 models of Make A and 35 models of Make B. The mean braking distance for Make A is 41 feet. Assume the population standard deviation is 4.6 feet.The mean braking distance for Make B is 42 feet. Assume the population standard deviation is 4.4 feet. At α=0.10, can the engineer support the claim that the mean braking distances are different for the two makes of automobiles? Assume the samples are random and independent, and the populations are normally distributed. Complete parts (a) through (e). (a) Identify the claim and state Ho and Ha. What is the claim? A.The mean braking distance is different for the two makes of automobiles. This is the correct answer. B.The mean braking distance is the same for the two makes of automobiles. C.The mean braking distance is less for Make A automobiles than Make B automobiles. Your answer is…
- The annual rainfall in a certain region is modeled using the normal distribution shown below. The mean of the distribution is 36.5 cm and the standard deviation is 5.2 cm. In the figure, V is a number along the axis and is under the highest part of the curve. And, U and W are numbers along the axis that are each the same distance away from V. Use the empirical rule to choose the best value for the percentage of the area under the curve that is shaded, and find the values of U, V, and W. Percentage of total area shaded: (Choose one) ▼ 200 35 | 55 25 30 40 45 50 ( cm) Submit Continue |Privacy Center © 2022 McGraw Hill LLC. All Rights Reserved. Terms of Use DIl S0 FB F7 esc F3To compare the dry braking distances from 30 to 0 miles per hour for two makes of automobiles, a safety engineer conducts braking tests for 35 models of Make A and 35 models of Make B. The mean braking distance for Make A is 43 feet. Assume the population standard deviation is 4.7 feet. The mean braking distance for Make B is 44feet. Assume the population standard deviation is 4.5 feet. At α=0.10, can the engineer support the claim that the mean braking distances are different for the two makes of automobiles? Assume the samples are random and independent, and the populations are normally distributed. Complete parts (a) through (e). a) identify the claim and state Ho and Ha b) find the critical values and identify the rejection regions c) Find the standardized test statistic z d) Decide whether to reject or fail to reject the null hypothesis. e) Interpret the decision in the context of the original claim.Calcium is essential to tree growth. In 1990, the concentration of calcium in precipitation in a certain area was 0.11milligrams per liter (mg/L).A random sample of 10 precipitation dates in 2018 results in the following data table. Complete parts (a) through (c) below. (B) With 98% confidence, the mean concentration of calcium in precipitation in this area in 2018 is between
- A production line produces glass panels with side length having a uniform distribution with mean 5 (fect). Nicola wants to buy 10 glass panels with side length at least 3 feet. What is the expected number of flass panels does he have to look at to find the 10 panels he needs? Note: answer is numerical, no unknown constants. Justify all your stops.The annual rainfall in a certain region is modeled using the normal distribution shown below. The mean of the distribution is 36.5 cm and the standard deviation is 5.2 cm. In the figure, V is a number along the axis and is under the highest part of the curve. And, Uand Ware numbers along the axis that are each the same distance away from Use the empirical rule to choose the best value for the percentage of the area under the curve that is shaded, and find the values of U, V, and W Percentage of total area shaded: (Choose one) 68% 95% 99.7% 20 25 30 U 35 I 40 45 50 55 ( cm)A drug to reduce fever was given to a random saple of 8 patients who suffered from high fever. The tempertaure (in celsius) of each patient was measured just before the drug was administered and also measured four hours after the drug was administered. The data is presented in table below. Suppose we want to test at the 1% level of significance whether the drug is effective in lowering fever. Let μ1 denote the mean tempertaure of patients before taking the drug and let μ2 denote the mean temperature of patient four hours after taking the drug . Also let μD=μ1 - μ2. ASSUMING ALL assumption of this test are satisfied Patient 1 2 3 4 5 6 7 8 Before 39.2 38.5 39.7 40.2 38.6 40.4 39.8 39.9 After 40 37.9 39.8 37 38.9 39.1 40.2 37.8 a) what is the null and alternative hypothesis and level of signiifcance for this hypothesis test? b) What is the distribution of the test statisitic for this hypothesis test? c) What is the decision rule for this hypothesis test?