Problem 1: Determine the least squares data fit for the following data. Also, find the root-mean-square-error of your least square fit.Xiyi-1.4660.3-0.0620.60.4920.90.8221.21.0681.51.9441.82.5832.13.2392.44.1482.74.46435.185

Given: Sr. No. x y 1 0 -1.466 2 0.3 -0.062 3 0.6 0.492 4 0.9 0.822 5 1.2 1.068 6 1.5…

Answered: Problem 1: Determine the least squares…

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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(b)
Nabisco claims that Chip Ahoy Cookies have an x ̅=28.67 per cookie with a SD=2.67. Assuming this information is true, what is the population parameter for the standard deviation?
The output table below represents the results of the estimation of household expenditures (Y) and income (X) in thousand dollars. Considering the results, answer the following questions. Dependent Variable: Y Method: Least Squares Date: 01/07/16 Time: 11:22 Sample: 2000 2015 Included observations: 16 Variable Coefficient Std. Error t-Statistic Prob. C -0.241942 2.452237 -0.098662 0.9228 X 0.363176 0.013890 26.14674 0.0000 R-squared 0.979933 Mean dependent var 55.43750 Adjusted R-squared 0.978499 S.D. dependent var 33.17221 S.E. of regression 4.864079 Akaike info criterion 6.118100 Sum squared resid 331.2297 Schwarz criterion 6.214674 Log likelihood -46.94480 Hannan-Quinn criter. 6.123046 F-statistic 683.6522 Durbin-Watson stat 0.632113…
In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.
2.1. Give one word for the following statements or scenarios: 2.1.1. The data collected by the researcher from the Department of Education were the 2020 matric results for all South African high schools in disadvantaged areas.. 2.1.2. This analysis can be performed using either the method of moving averages, or by fitting a straight line using the method of least squares from regression analysis.
Add to MyRegistry 9 Mail - sgorham@a.. Like father, like son: In 1906, the statistician Karl Pearson measured the heights of 1078 pairs of fathers and sons. The following table presents a sample of 6 pairs, with height measured in inches, simulated from the distribution specified by Pearson. Compute the least-squares regression line for predicting son's height (v) from father's height (x). Round the slope and y-intercept values to at least four decimal places. Father's Son's height height 73.6 74.9 66.7 68.8 70.1 73.3 72.3 71.9 73.6 76.5 69.3 71.4 Send data to Excel Regression line equation: Save For Later Submit Assignment Check Answer © 2021 McGraw Hill LLC. AlI Rights Reserved. Terms of Use Privacy Center
A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, y. She randomly selects 11 children from her practice, measures their heights and head circumferences, and obtains the accompanying data. Complete parts (a) through (g) below. Click the icon to view the children's data. Data table (a) Find the least-squares regression line treating height y =x+ (O (Round the slope to three decimal places and round the d Height (inches), x Head Circumference (inches), y O. 28 17.6 24.5 17.1 25.75 17.1 25.75 17.5 24.25 17.0 27.75 17.7 26.5 17.3 27.25 17.6 26.5 17.3 26.5 17.5 27.75 17.6 Print Done Help me solve this View an example Get more help - Media - Clear all Check answer
3.
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. Thecorrelation coefficient is 0.944 and the least squares regression line is y = 0.199x + 11.982. Complete parts (a) and (b)below.Height, x27.5 25.5 26.25 25.25 27.5 26.25 26 27.25 27.25 27 27.25 ФHead Circumference, # 17.5 17.0 17.2 17.0 17.5 17.3 17.2 17.4 17.3 17.3 17.4(a) Compute the coefficient of determination, R?R?.% (Round to one decimal place as needed.)(b) Interpret the coefficient of determination and comment on the adequacy of the linear model.Approximately % of the variation inis explained by the least-squares regression model.According to the residual plot, the linear model appears to be (Round to one decimal place as needed.)
A researcher records data on 7 adult pairs' heights (in inches) to compare the physical characteristics of brothers and sisters. Brother Sister 71 69 68 64 6 65 67 63 70 65 71 62 66 62 Mean 68.4285 64.2857 SD 2.2253 2.4299 r=0.4050 What would the least-squares regression equation be for predicting the brother's height from the sister's? A. brother's height = 0.037+44.58 * sister's height B. brother's height = 44.58 + 0.371* sister's height C. brother's height = 20.71 + 0.029 * sister's height D. brother's height = 3.28- 40.68 * sister's height If the sister's height is the same as the mean (64.2857 inches), what would the brother's predicted height be A. 68.4285 (the same as the mean as well) B. 62.1234 C. 70.8990 D. None of the above. Which of the following would be correct? A. The pair of means, (68.4285, 64.2857), lies on the linear regression line. B. The effectiveness of the linear regression model is about 16%, C. The effectiveness of the linear regression model is 10096. D. Both…
x 5.7 4.1 6.2 4.4 6.5 5.8 4.9 y 1.9 4.8 0.8 3.9 1.2 1.7 3.0 (a) Computethecoefficientofdetermination. (b) Howmuchofthevariationintheoutcomevariableisexplainedbytheleast-squares regression line?
Use the following information to answer questions 6 and 7. In a study, nine tires of a particular brand were driven on a track under identical conditions. Each tire was driven a particular controlled distance (measured in thousands of miles) and the tread depth was measured after the drive. Tread depth is measured in "mils." Here, 1 mil is 0.001 inch. The least-squares regression line was computed and added to a scatterplot of these data. On the plot, one data point is marked with an "X." The equation of the least-squares regression line is: Tread depth = 360.64 - 11.39 Miles The data value marked with "X" in the provided scatterplot has Tread Depth (Mils) 60 80 150 0 5 O a negative value for the residual. O a positive value for the residual. O a zero value for the residual. O a zero value for the correlation. 10 15 Miles (x 1000) 20 25 30

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Problem 1: Determine the least squares data fit for the following data. Also, find the root- mean-square-error of your least square fit. Xi yi -1.466 0.3 -0.062 0.6 0.492 0.9 0.822 1.2 1.068 1.5 1.944 1.8 2.583 2.1 3.239 2.4 4.148 2.7 4.464 3 5.185