An experiment to study the relationship between x = time spent exercising (min) and y = amount of oxygen consumed during the exercise periodresulted in the following summary statistics.n = 20 Σχ= 50 Σy = 16,704 ΣχΣy = 14,194,228 Σxy = 44,195(a) Estimate the slope of the population regression line.x² = 150Estimate y intercept of the population regression line.(b) One sample observation on oxygen usage was 747 for a 2 min exercise period. What amount of oxygen consumption is predicted for thisexercise period, and what is the corresponding residual?ŷ =residual(c) Calculate a 99% confidence interval for the average change in oxygen consumption associated with a 1 min increase in exercise time. (Usetechnology to calculate the critical value. Round your answers to three decimal places.)

Answered: An experiment to study the relationship…

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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In a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.79 -0.4 -0.79 0.4
Which of the following tools is not appropriate for studying the relationship between two numerica variables? Correlation coefficient Scatter plot Historam Regression
e) Perform the F Test making sure to state the null and alternative hypothesis.
The amounts of 6 restaurant bills and the corresponding amounts of the tips are given in the below. Assume that bill amount is the explanatory variable and tip amount the response variable. Bill 32.98 49.72 52.44 64.30 97.34 88.01 Tip 4.50 5.28 7.00 7.70 16.00 10.00 (a) Find the correlation: r= (c) The regression equation is y^= . (d) If the amount of the bill is $90, the best prediction for the amount of the tip is $ .Note: Enter your answer as a number xx.xx (e) According to the regression equation, for every $5 increase in the bill, the tip should INCREASE or DECREASE) by $ .
6
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 80 mm Hg. Use a significance level of 0.05. Right Arm 100 99 93 77 77 Q Left Arm 174 168 148 148 146 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is ŷ=+x. (Round to one decimal place as needed.) mm Hg. Given that the systolic blood pressure in the right arm is 80 mm Hg, the best predicted systolic blood pressure in the left arm is (Round to one decimal place as needed.) Data table Critical Values of the Pearson Correlation Coefficient r α = 0.05 α = 0.01 0.950 0.990 0.959 0.878 0.811 0.917 0.754 0.875 0.707 0.834 0.666 0.798 0.632 0.765 0.602 0.735 0.576 0.708 0.553 0.684 0.532 0.661 0.514 0.641 0.497 0.623 0.482…
Do the following plots show 1. Constant variability 2. Nearly normal residuals 3. Independent observations for SLR (conditions for linear regression)
Consider the following information about variable X: Mean = 4, standard deviation = 2. Consider the following information about variable Y: Mean = 5, standard deviation = 3. If there is a correlation of r = 0.52 between variable X and variable Y, what is the intercept for a simple linear regression equation predicting Y on the basis of X? Please provide your answer as a raw score (not a z score) with a minimum of two decimal places.
i)Test individually whether the slope coefficients are significant at 10% significance level. ii)Test the overall significance of the estimated regression at 1% significance level.
Given the table below, Let y, x1, x2, and x3 be the time of concentration (t, in hr), watershed length (L in ft), watershed slope (S in %), and runoff curve number (CN): Evaluate the linear multiple regression model with two or three predictor variables. Compute the multiple correlation coefficient, the standard error of estimate, and the standard deviation of the variable y. Discuss the reliability of the model
Given the Analysis of Variance (ANOVA) for the simple linear regression as below: Table Q5: ANOVA Source df MS F Regression 1 252378 E Error A 5482 D Total 24 в (a) Calculate A, B, C, D and E. (b) At a = 0.05 test whether the regression model are fit.
For the data below, the Pearson correlation coefficient between y and x^2 isA. 0.913B. 0.985C. 0.990D. 0.873

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