9. Answer the questions based on what is provided in the given box. Consider a statisticalsignificance level of 0.05 for this problem.>> myData = readtable ('peruvian.txt', 'Delimiter',' ');>> mdl = fitlm (myData, 'Systol-Age+Weight+Height+Pulse+Diastol')mdl =Linear regression model:Systol 1 + Age + Weight + Height + Pulse + DiastolEstimated Coefficients:EstimateSEtStatpValue(Intercept)33.394Age-0.3155564.2760.312890.519540.60814-1.00850.32328Weight0.945670.400982.35840.026838Height0.0124160.0423030.293510.77165Pulse0.115940.238160.486820.6308Diastol0.24360.257330.946660.35325Number of observations: 30, Error degrees of freedom: 24Root Mean Squared Error: 11R-squared: 0.423, Adjusted R-Squared: 0.303F-statistic vs. constant model: 3.52, p-valuea) Describe the null hypothesis for the F-test?= 0.0158b) Is there a relationship between the predictors and the response? Justify your answer.

Here, we are given a regression model output with Systol as the response or dependent variable, and…

Answered: 9. Answer the questions based on what…

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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A consumer advocacy group recorded several variables on 140 models of cars.The resulting information was used to produce two models for predicting miles per gallon in the city (mpg_city), one based on the engine displacement (in cubic inches) and a second one based the power of the engine (in horsepower).Model 1: mpg vs engine displacementThe regression equation ismpg_city=33.8 - 0.063*displacementS = 3.26269R-squared = 66.7%Model 2: mpg vs horsepowerThe regression equation ismpg_city=32.2 - 0.0577*horsepowerS = 4.06386R-squared = 49.7% The displacement variable is better because it has a higher R-square (66.7%).The variable horsepower is better because it has a higher residual standard error (S=4.06386). The variable horsepower is better because it has a higher residual standard error (S=4.06386) and a lower R-square (49.7%).The displacement variable is better because it has a lower estimate for the residual standard error (S=3.26269).The displacement variable is better because it…
Suppose that you run a correlation and find the correlation coefficient is 0.421 and the regression equation is ˆy=2.2x+6.96y^=2.2x+6.96. The center of your x-data was 5.2 and the center of your y-data was 18.6.If the critical value is .396, use the appropriate method to predict the yy value when xx is 6.8
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Find the estimated y intercept . Round your answer to three decimal places. age 35 41 52 56 66 bone density 358 350 348 332 321
11. What is the standard error of the slope of the regression line (SEb)? Please put the answer in bold.
) Is there evidence of a nonlinear relationship between birth weight andmother’s weight gain?
Suppose that you run a correlation and find the correlation coefficient is 0.338 and the regression equation is ˆy=−12.7+4.3xy^=-12.7+4.3x.The mean for the xx data values was 7, and the mean for the y data values was 17.A T Test for the slope of the regression line is performed, and the p-value is greater than the level of significance of 0.05. Use the appropriate method to predict the y value when x is 4.4.
If the absolute value of regression coefficient of a predictor in SLR is more than that of predictor, when a second predictor is added, what attribute describes the relationship between the original variable and the added variable Answer in few lines .not very big answer needed
The attached results are for a multiple regression study of smartphone addiction (SSA-SV) proneness in relation to 1) Gender 2)Age 3) Anxiety (GAD-7). I just want to clarify that age is statistically significant with p = 0.006; is this correct? What's throwing me off is the notation "< .001" on the top line under the p category. I was under the impression that the p-value would need to be less than 0.001.
The regression line for a given dataset is found to be y^ =76−7.51xx. There is a pair of values x =7 and y =42 in this dataset taken from a specific individual. Compute the residual (error) of the predicted value y^ for this individual. Give your answer to two decimal places
Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is1.8cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead_Width_(cm) Weight_(kg)7.2 1287.4 1679.8 2619.5 2218.7 2118.3 201 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) Part 2 The best predicted weight for an overhead width of 1.8 cm, based on the regression equation, is enter your response here kg. (Round to one decimal place as needed.) Part 3 Can the prediction be correct? If not, what is wrong? A. The prediction cannot be correct…
Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"† describes a study to investigate the effect of x = frying time (in seconds) and y = acrylamide concentration (in micrograms per kg) in French fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. FryingTime AcrylamideConcentration 150 155 240 115 240 190 270 180 300 140 300 265 (a) Construct a scatterplot of these data. A scatterplot has 6 points.The horizontal axis is labeled "x" and ranges from 100 to 350.The vertical axis is labeled "y" and ranges from 50 to 350.The points are plotted from left to right in a horizontal direction starting from the lower left side of the diagram.Along the horizontal axis, there is 1 point at 150, 2 points…

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