The systolic blood pressure of individuals is thought to be related to both age and weight. Let the systolic blood pressure, age, and weight be represented by the variables x1, x2, and x3, respectively. Suppose that Minitab was used to generate the following descriptive statistics, correlations, and regression analysis for a random sample of 15 individuals. Descriptive StatisticsVariableNMeanMedianTrMeanStDevSE Meanx115158.78159.68158.783.4590.893110x21556.8557.8556.851.4450.373097x315176.84176.04176.844.0181.037443VariableMinimumMaximumQ1Q3x1126170136.347167.113x2438147.83977.990x3120234141.672224.477Correlations (Pearson) x1x2x20.889 x30.8500.639Regression Analysis The regression equation is x1 = 0.808 + 1.262x2 + 0.815x3 PredictorCoefStDevTPConstant0.8080.4941.640.064x21.2620.6172.050.032x30.8150.7531.080.150S = 0.470R-sq = 91.1 %R-sq(adj) = 93.1 % What percentage of variation in x1 can be explained by the corresponding variation in x2? Round your answer to the nearest tenth.answer choices:91.1%93.1%8.9%72.3%79.0%

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Description: The systolic blood pressure of individuals is thought to be related to both age and weight. Let the systolic blood pressure, age, and weight be represented by the variables x1, x2, and x3, respectively. Suppose that Minitab was used to generate the f

From the given output, the correlation coefficient between x1 and x2 is 0.889.

Answered: The systolic blood pressure of…

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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The systolic blood pressure of individuals is thought to be related to both age and weight. Let the systolic blood pressure, age, and weight be represented by the variables x₁, x₂, and x₃, respectively. Suppose that Minitab was used to generate the following descriptive statistics, correlations, and regression analysis for a random sample of 15 individuals.

Descriptive Statistics
Variable	N	Mean	Median	TrMean	StDev	SE Mean
x₁	15	158.78	159.68	158.78	3.459	0.893110
x₂	15	56.85	57.85	56.85	1.445	0.373097
x₃	15	176.84	176.04	176.84	4.018	1.037443

Variable	Minimum	Maximum	Q1	Q3
x₁	126	170	136.347	167.113
x₂	43	81	47.839	77.990
x₃	120	234	141.672	224.477

Correlations (Pearson)
	x₁	x₂
x₂	0.889
x₃	0.850	0.639

Regression Analysis

The regression equation is

x₁ = 0.808 + 1.262x₂ + 0.815x₃

Predictor	Coef	StDev	T	P
Constant	0.808	0.494	1.64	0.064
x₂	1.262	0.617	2.05	0.032
x₃	0.815	0.753	1.08	0.150
S = 0.470	R-sq = 91.1 %	R-sq(adj) = 93.1 %

What percentage of variation in x1 can be explained by the corresponding variation in x2? Round your answer to the nearest tenth.

answer choices:

91.1%

93.1%

8.9%

72.3%

79.0%

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