Need help on part D!How does lateral acceleration—side forces experienced in turns that are largely under driver control—affect nausea as perceived by bus passengers? An article reported data on x = motion sickness dose (calculated in accordance with a British standard for evaluating similar motion at sea) and y = reported nausea (%). Relevant summary quantities aren = 17, xi = 223.9 , yi = 196 , xi2 = 3056.65 , xiyi = 2759.6 , yi2 = 2976 .Values of dose in the sample ranged from 6.0 to 17.6.(a) Assuming that the simple linear regression model is valid for relating these two variables (this is supported by the raw data), calculate and interpret an estimate of the slope parameter that conveys information about the precision and reliability of estimation. (Calculate a 95% CI. Round your answers to three decimal places.) , (b) Does it appear that there is a useful linear relationship between these two variables? Test appropriate hypotheses using α = 0.01.State the appropriate null and alternative hypotheses.H0: β1 = 0Ha: β1 < 0H0: β1 = 0Ha: β1 > 0 H0: β1 ≠ 0Ha: β1 = 0H0: β1 = 0Ha: β1 ≠ 0Calculate the test statistic and determine the P-value. (Round your test statistic to two decimal places and your P-value to three decimal places.)t = P-value = State the conclusion in the problem context.Fail to reject H0. There is a useful linear relationship.Fail to reject H0. There is not a useful linear relationship. Reject H0. There is a useful linear relationship.Reject H0. There is not a useful linear relationship.(c) Would it be sensible to use the simple linear regression model as a basis for predicting % nausea when dose = 5.0? Explain your reasoning.Yes, the regression model can be applied in this situation.No, a regression model is only useful for estimating values of nausea percentage when using dosages between 6.0 and 17.6, the range of values sampled. No, a regression model is only useful for estimating values of nausea percentage when using dosages greater than or equal to 6.0.No, a regression model is only useful for estimating values of nausea percentage when using dosages greater than or equal to 17.6.(d) When Minitab was used to fit the simple linear regression model to the raw data, the observation (6.0, 2.50) was flagged as possibly having a substantial impact on the fit. Eliminate this observation from the sample and recalculate the estimate of part (a). (Round your answers to three decimal places.) ,

(D) Given observation is (6.0, 2.50). Given that, ∑xi=223.9 ∑yi=196 ∑xi2=3056.65 ∑yi2=2976…

Answered: ow does lateral acceleration—side…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter7: Analytic Trigonometry

Section7.6: The Inverse Trigonometric Functions

Problem 94E

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Need help on part D!

How does lateral acceleration—side forces experienced in turns that are largely under driver control—affect nausea as perceived by bus passengers? An article reported data on x = motion sickness dose (calculated in accordance with a British standard for evaluating similar motion at sea) and y = reported nausea (%). Relevant summary quantities are

n = 17,

	x_i = 223.9

	y_i = 196

	x_i² = 3056.65

	x_iy_i = 2759.6

	y_i² = 2976

Values of dose in the sample ranged from 6.0 to 17.6.

(a) Assuming that the simple linear regression model is valid for relating these two variables (this is supported by the raw data), calculate and interpret an estimate of the slope parameter that conveys information about the precision and reliability of estimation. (Calculate a 95% CI. Round your answers to three decimal places.)

(b) Does it appear that there is a useful linear relationship between these two variables? Test appropriate hypotheses using

α = 0.01.

State the appropriate null and alternative hypotheses.

H₀: β₁ = 0
H_a: β₁ < 0H₀: β₁ = 0
H_a: β₁ > 0 H₀: β₁ ≠ 0
H_a: β₁ = 0H₀: β₁ = 0
H_a: β₁ ≠ 0

Calculate the test statistic and determine the P-value. (Round your test statistic to two decimal places and your P-value to three decimal places.)

t	=
P-value	=

State the conclusion in the problem context.

Fail to reject H₀. There is a useful linear relationship.Fail to reject H₀. There is not a useful linear relationship. Reject H₀. There is a useful linear relationship.Reject H₀. There is not a useful linear relationship.

(c) Would it be sensible to use the simple linear regression model as a basis for predicting % nausea when dose = 5.0? Explain your reasoning.

Yes, the regression model can be applied in this situation.No, a regression model is only useful for estimating values of nausea percentage when using dosages between 6.0 and 17.6, the range of values sampled. No, a regression model is only useful for estimating values of nausea percentage when using dosages greater than or equal to 6.0.No, a regression model is only useful for estimating values of nausea percentage when using dosages greater than or equal to 17.6.

(d) When Minitab was used to fit the simple linear regression model to the raw data, the observation (6.0, 2.50) was flagged as possibly having a substantial impact on the fit. Eliminate this observation from the sample and recalculate the estimate of part (a). (Round your answers to three decimal places.)

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