(a) Use least-squares regression to fit a linear polynomial to the data given in the table below:2461012141618411.41 412.54 413.29 414.25 414.17 415.39 415.71 416.80 416.68Calculate the standard deviation for the regression line. Then, use the fitted linear formula toestimate the y-value at x 14.6. Round all your computations to 6 decimal points.(b) Repeat part (a) but with using the general least-squares estimation approach (i.e., the oneimplementing the matrix-vector form). Comment on the vector of residuals.Hint: You do not need to develop a program for this problem. Solve it manually and submit.

Given information: x y 2 411.41 4 412.54 6 413.29 8 414.25 10 414.17 12 415.39 14…

Answered: (a) Use least-squares regression to fit…

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 study was conducted to determine the relationship between starting salaries (RM thousands) for recent statistics graduates and their grade point averages in the major course. A linear regression model was fitted to the data and the estimates regression function was obtained. Part of the computer output for the above analysis is given below: ANOVA Model Sum of df Mean F Sig. Squares Square Regression 147.28 .000 Error 734.9 40.828 Total 6748.2 Coefficients Unstandardized Coefficients Model Sig. Std. B Error Constant GPA -8.42 3.007 3.395 0.2477 -2.48 12.14 0.011 0.000 (a) Complete the ANOVA table (blue boxes). (b) Write down the estimated regression function. Interpret the estimated parameters. (c) Test whether there is a linear association between salaries and grade point average. Use a = 0.05. (d) Determine the coefficient of determination for the model and interpret its meaning.
Determine if the statement is true or false.
Use the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are in dollars.) What is the best predicted subway fare when pizza costs $3.98 per slice? Year Pizza Cost Subway Fare CPI 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 0.152 0.353 1.000 1.251 1.753 2.003 2.254 2.300 2.747 2.997 0.151 0.346 1.000 1.347 1.497 2.004 2.253 2.551 2.747 2.750 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 The regression equation is y=+x. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.) The best predicted subway fare when pizza costs $3.98 per slice is $ (Round to the nearest cent as needed.)
Compute the least-squares regression equation for the given data set. Use a TI-84 calculator. Round the slope and y-intercept to at least four decimal places. 43 37 15 19 24 37 18 y 72 67 25 29 42 65 32 Send data to Excel Regression line equation: y = 1.7777x + 1.5838
The following data represent the commute time (in minutes) x and a score on a well-being survey y. The equation of the least-squares regression line is y = - 0.0423x + 69.1961 and the standard error of the estimate is 0.5262. Complete parts (a) through (e) below. 15 25 35 45 60 82 115 D 69.0 68.3 66.8 66.2 66.5 64.2 y 67.4 (a) Predict the mean well-being index composite score of all individuals whose commute time is 30 minutes. y = (Round to two decimal places as needed.) (b) Construct a 90% confidence interval for the mean well-being index composite score of all individuals whose commute time is 30 minutes. Lower Bound (Round to two decimal places as needed.) Upper Bound |(Round to two decimal places as needed.) (c) Predict the well-being index composite score of Jane, whose commute time is 30 minutes. y =(Round to two decimal places as needed.)
The dataset below contains synthetic records of human heights and weights of 18 years old children. HEIGHT WEIGHT (Feet) (in kg) 5.48 51.36 5.96 62.04 5.78 69.56 5.69 64.70 5.65 65.59 5.73 56.05 5.82 64.31 5.83 62.03 а. Find the equation of the regression that model the relationship between the weight of mail and number of order using LINEAR EQUATION, EXPONENTIAL EQUATION, SIMPLE POWER EQUATION, and HYPERBOLIC| EQUATION. b. Compute for the correlation coefficient of each equation using PEARSON PRODUCT MOMENT CORRELATION COEFFICIENT (PPMCC).
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
Use the p-value criterion to find the best model for predicting the number of points scored per game by football teams using the accompanying National Football League Data. Does the model make logical sense? E Click the icon to view the National Football League Data. Determine the best multiple regression model. Let X, represent Rushing Yards, let X, represent Passing Yards, let X3 represent Penalties, let X4 represent Interceptions, and let Xg represent Fumbles. Enter the terms of the equation so that the Xy-values are in ascending numeral order by base. Select the correct choice below and fill in the answer boxes within your choice. (Type an integer or decimal rounded to three decimal places as needed.) Points/Game =+ OX 01+Og+O1 A. OB Points/Game = O C. Points/Game = OD. Points/Game = + Dx O E. Points/Game = Data table for the national football league Points/ Game Rushing Yards/ Game Passing Yards/ Penalties Interceptions Fumbles O Game 25.2 90.1 259.2 140 18 4 16.2 95.2 208.5 108…
Compute the least-squares regression equation for the given data set. Round the slope and yFintercept to at least four decimal places. 4 y 7. 3. Send data to Excel Regression line equation: y =|
Use the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are in dollars.) What is the best predicted subway fare when pizza costs $3.91 per slice? Year 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 Pizza Cost 0.153 0.347 0.998 1.247 1.751 1.996 2.246 2.298 2.751 2.997 Subway Fare 0.149 0.347 1.001 1.347 1.500 1.996 2.252 2.548 2.753 2.748 CPI 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 Question content area bottom Part 1 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.) Part 2 The best predicted subway fare when pizza costs $3.91per slice is $enter…
In the graph shown below consider the horizontal line to represent the average y value for the data set and the slant line to represent the linear regression equation. use only the graph of the data to do the following:

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