Identify which of the following scatterplots contain possible outliers or observations influential for the least squaresregression line. Select all that apply90203080-15.70-3 60-1010-50-520,000 40,000 60.00 s0,00 100.000 120.000101525354030Number of incorrect answersDistance driven (mi)Age (years)OutlierInfluential observationOutlier60-4555-50-45-22-35-20-25-40-18-15-5.5 6.0 6.5 7.0 7.5 8.0Hours slept6570752.05 2.10 2.15 2.20 2.25 2.30 2.35 2.405.0Daily high temperature (*F)Price of gasoline (S/gal)No outliers or influential observationsNo outliers or influential observationsOutlierInfluential observationAnswer BankNo outliers or influential observationsInfluential observationOutlierDaily low temperature ("F)Test scoreDuration of commute (min)Years of full-time employmentCoffee consumed (oz)Number of oil changes

Outlier is an extreme values in the data set. Influential observation is the observation whose…

Answered: gression line. Select all that apply…

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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under which conditions measuremnt error is problematic in terms of bias and inconsistency of the ordinary least squares(OLS) estimator.
The shape of the mean-variance frontier that results from the combination of a riskless and arisky asset is…..:a. Is U-shaped, tilted 90 degrees clockwise.b. Is a straight line passing from the mean-variance points of the two assets.c. Is a hyperbola.d. Consists of two straight lines, each connecting one of the two assets to a risk-freeportfolio
Test the hypothesis that the average weekly allowance of female and male ACC 215 students the same, at 5%l evel of significance. (Step by step solution, manually, without using excel) Weekly Allowance of Students (Php) Male: 1500 2000 2500 2500 2000 1750 2000 1800 Female:1500 2000 1750 1500 2000 2500 3000 3500 Ho: Ha: α = Decision Rule: Computation: Decision: Conclusion:
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1)Remove the four potential outliers of 0, 0, 8, and 20, and then obtain a new histogram without the outliers. Does the data appear to be normally distributed now? 2)Assuming that the four potential outliers of 0, 0, 8, and 20 are not recording errors, repeat the hypothesis test from part (c) (again setting up the hypothesis test and using either the critical value or p-value approach), and compare your results with that obtained in (c). Did you make a different conclusion? 3)Imagine you know have to make a recommendation/conclusion to the company that hired you: Assuming that the four potential outlies are not recording errors, and looking at the two results above, would you recommend using the first test with the outliers or the second test with the outliers removed? There is no right or wrong answer here, I am interested in what you think and your reasoning.
Texas Instruments produces computer chips in production runs of 1 million at a time. It has found that the fraction of defective modules can be very different in different production runs. These differences are caused by small variations in the set-up of each production run. Managers have observed that defective rates are roughly triangular, with a lower bound of 0%, and an upper bound of 50%. Defects more likely to be near 10% than any other single value in their range. (a) Before we test any modules, let’s understand the distribution of de- fects. Simulate at least 2,000 draws from the triangular distribution and answer the following questions. 1 What is the probability of a defect rate less than 0.25 in this production run? 2 For what number M would you say that the defective rate is equally likely to be above or below M? 3 What is the probability that we will find exactly two defective modules when we test 10 modules from this production run? (b) Now suppose that we have taken a…
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The article “Characterization of Highway Runoff in Austin, Texas, Area” (J. of Envir. Engr., 1998: 131–137) gave a scatterplot, along with the least squares line, of x = rainfall volume (m3) and y = runoff volume (m3) for a particular location. The accompanying values were read from the plot. x <‐ c(5, 12, 14, 17, 23, 30, 40, 47, 55, 67, 72, 81, 96, 112, 127) y <‐ c(4, 10, 13, 15, 15, 25, 27, 46, 38, 46, 53, 70, 82, 99, 100) Test at level 0.05 whether there is a useful linear relationship between rainfall and runoff,and then calculate a 95% confidence interval for the true average change in runoff volumeassociated with one unit (one m3) increase in rainfall volume.

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