Sela(d) Find the predicted percentage ŷ of successful field goals for a player with x = 65% successful free throws. (Round your answer to two decimal places.)(e) Find a 90% confidence interval for y when x = 65. (Round your answers to one decimal place.)lower limitupper limit(f) Use a 5% level of significance to test the claim that B > 0. (Round your answers to two decimal places.)tcritical tConclusionO Reject the null hypothesis, there is sufficient evidence that B > 0.Reject the null hypothesis, there is insufficient evidence that B > 0.Fail to reject the null hypothesis, there is insufficient evidence that B > 0.O Fail to reject the null hypothesis, there is sufficient evidence that B > 0.(g) Find a 90% confidence interval for ß. (Round your answers to three decimal places.)lower limitupper limitInterpret its meaning.O For every percentage increase in successful free throws, the percentage of successful field goals increases by an amount that falls within the confidence interval.For every percentage increase in successful free throws, the percentage of successful field goals decreases by an amount that falls within the confidence interval.For every percentage increase in successful free throws, the percentage of successful field goals decreases by an amount that falls outside the confidence interval.O For every percentage increase in successful free throws, the percentage of successful field goals increases by an amount that falls outside the confidence interval. Let x be a random variable that represents the percentage of successful free throws a professional basketball player makes in a season. Let y be a random variable that represents the percentage of successful field goals a professionalbasketball player makes in a season. A random sample of n =6 professional basketball players gave the following information.676475867373444148514451(a) Verify that Ex438, Ey = 279,Ex232264, Ey = 13059, Exy = 20493, and r = 0.800.II%3D%3D%3D%3DΣχEyEx?Ey2Σχy(b) Use a 5% level of significance to test the claim that p > 0. (Round your answers to two decimal places.)critical tConclusionO Reject the null hypothesis, there is sufficient evidence that p > 0.Reject the null hypothesis, thereinsufficient evidence that p > 0.Fail to reject the null hypothesis, there is insufficient evidence that p > 0.Fail to reject the null hypothesis, there is sufficient evidence that p > 0.(c) Verify that S 2.7729, a 14.783, b 0.4345, and x = 73.000.Seab65% successful free throws. (Round your answer to two decimal places.)(d) Find the predicted percentage ŷ of successful field goals for a player with x =%(e) Find a 90% confidence interval for y when x = 65. (Round your answers to one decimal place.)lower limit%%upper limit(f) Use a 5% level of significance to test the claim that B > 0. (Round your answers to two decimal places.)critical tConclusionO Reject the null hypothesis, there is sufficient evidence that B > 0.Reject the null hypothesis, there is insufficient evidence that B > 0.Fail to reject the null hypothesis, there is insufficient evidence that ß > 0.MacBook Pro

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Let a be a random variable representing the percentage of protein content for early bloom alfalfa hay. The average percentage protein content of such early bloom alfalfa should be u = 17.2%. A farmer's co-op is thinking of buying a large amount of baled hay but suspects that the hay is from a later summer cutting with lower protein content. A small amount of hay was removed from each bale of a random sample of 50 bales. The average protein content from the samples was determined by a local agricultural college to be -15.8% with a sample standard deviation of S = 5.3%- At a = .05, does this hay have lower average protein content than the early bloom alfalfa?
In the airline business, "on-time" flight arrival is important for connecting flights and general customer satisfaction. Is there a difference between summer and winter average on-time flight arrivals? Let x1 be a random variable that represents percentage of on-time arrivals at major airports in the summer. Let x2 be a random variable that represents percentage of on-time arrivals at major airports in the winter. A random sample of n1 = 16 major airports showed that x1 = 74.9%, with s1 = 5.3%. A random sample of n2 = 18 major airports showed that x2 = 70.2%, with s2 = 8.6%. Note: For degrees of freedom d.f. not in the Student's t table, use the closest d.f. that is smaller. In some situations, this choice of d.f. may increase the P-value a small amount and thereby produce a slightly more "conservative" answer. (a) Does this information indicate a difference (either way) in the population mean percentage of on-time arrivals for summer compared to winter? Use α = 0.05. (i) What is the…
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Magnetic surveying is one technique used by archaeologists to determine anomalies arising from variations in magnetic susceptibility. Unusual changes in magnetic susceptibility might (or might not) indicate an important archaeological discovery. Let x be a random variable that represents a magnetic susceptibility (MS) reading for a randomly chosen site at an archaeological research location. A random sample of 120 sites gave the readings shown in the table below. Magnetic Susceptibility Readings,centimeter-gram-second ✕ 10−6 (cmg ✕ 10−6) Comment MagneticSusceptibility Number ofReadings EstimatedProbability "cool" 0 ≤ x < 10 30 30/120 = 0.25 "neutral" 10 ≤ x < 20 54 54/120 = 0.45 "warm" 20 ≤ x < 30 24 24/120 = 0.20 "very interesting" 30 ≤ x < 40 6 6/120 = 0.05 "hot spot" 40 ≤ x 6 6/120 = 0.05 Suppose a "hot spot" is a site with a reading of 40 or higher. (c) What is the probability that you will need more than five readings to find the first…
Magnetic surveying is one technique used by archaeologists to determine anomalies arising from variations in magnetic susceptibility. Unusual changes in magnetic susceptibility might (or might not) indicate an important archaeological discovery. Let x be a random variable that represents a magnetic susceptibility (MS) reading for a randomly chosen site at an archaeological research location. A random sample of 120 sites gave the readings shown in the table below. Magnetic Susceptibility Readings,centimeter-gram-second ✕ 10−6 (cmg ✕ 10−6) Comment MagneticSusceptibility Number ofReadings EstimatedProbability "cool" 0 ≤ x < 10 36 36/120 = 0.30 "neutral" 10 ≤ x < 20 48 48/120 = 0.40 "warm" 20 ≤ x < 30 12 12/120 = 0.10 "very interesting" 30 ≤ x < 40 18 18/120 = 0.15 "hot spot" 40 ≤ x 6 6/120 = 0.05 Do the probabilities shown in the table add up to 1? Do the probabilities need to total to 1 a. Yes, they do add to 1. The probabilities must total to…
A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company (X1) and how he/she scored on a business aptitude test (X2). A random sample of 8 employees provides the following (refer Table 4): Employees y X1 X2 1 100 9 6 2 90 4 10 3 80 8 9 4 70 5 4 5 60 5 8 6 50 7 5 7 40 2 4 8 30 2 2 (a) Run a regression analysis based on the data in Table 4 using Mic. Office EXCEL. Based on theoutput, propose a regression equation that can be used to predict paralegal GPA.(b) Interpret the coefficient value of each independent variable in the constructedmodel(c) Is each independent variable sharing a significant linear relationship with the dependent variable?Verify at 0.05 level of significance. Make your conclusion based on the p-value in the EXCEL output.
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A pathologist has been studying the frequency of bacterial colonies within the field of a microscope using samples of throat cultures from healthy adults. Long-term history indicates that there is an average of 2.80 bacteria colonies per field. Let r be a random variable that represents the number of bacteria colonies per field. Let O represent the number of observed bacteria colonies per field for throat cultures from healthy adults. A random sample of 100 healthy adults gave the following information. 2 3 4 5 or more 13 18 28 16 17 8 (a) The pathologist wants to use a Poisson distribution to represent the probability of r, the number of bacteria colonies per field. The Poisson distribution is given below. P(r) = r! Here 1 = 2.80 is the average number of bacteria colonies per field. Compute P(r) for r = 0, 1, 2, 3, 4, and 5 or more. (Round your answers to three decimal places.) P(0) = P(1) = Р(2) Р(3) 3 P(4) = %3D P(5 or more) = (b) Compute the expected number of colonies E = 100P(r)…

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