Q4Deep leaming is a type of Machine Learning, inspired by the function and structure ofa human brain, where machines can learn by experience and acquire skills withoutany human involvement. Table Q4 shows the relation of data amount and theperformance of a deep learning technique capability to perform COVID-19 face maskidentification among crowd in Pasar Rabu, Taman Universiti, Parit Raja.Table Q4Independent variableDependent variableFace mask identificationSet of experimentTraining data size, xаccuracy (%), у1100302004033005040055500606.6007077007588008099008510100090101010Given > r; = 4600, = 635, > = 2860000i=1i=1i=11010= 43875, >r yi = 322000(a)Determine Sr, Syy, and Sry(b)Determine B. B.(c)Determine the estimated regression line equation.

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Answered: (a) Determine Sr. Syy. and Szy (b)…

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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Q12 a) Construct a regression model to determine the relationship between individual yearly income and the value of the car owned. b) Using the regression equation from (a) determine the value of the car if the yearly income is $40,000 c) Describe the relationship between yearly income and value of the car owned d) Identify the independent variable and dependent variable based on the regression model constructed in (a) e) Explain why it is incorrect to predict the value of the car if an individual income is $10,000
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The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x,) and newspaper advertising (x,). The estimated regression equation was ý = 82.3 + 2.29x, + 1.90x2. The computer solution, based on a sample of eight weeks, provided SST = 25.1 and SSR = 23.415. (a) Compute and interpret R? and R 2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is 653 x . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (b) When television advertising was the only independent variable, R2 = 0.653 and R,2 = 0.595. Do you prefer the multiple regression results? Explain. Multiple regression analysis (is preferred since both R2 and R.2 show an increased v v…
An individual wants to determine how much money a boy scout earns from selling popcorn (Y-variable) based on the length of time (in days) that they went out selling (X-variable). Using the following linear regression equation Ŷ = 5 + 20X: a. State the value of the intercept AND the value of the slope (2 points) b. Based on the given regression equation, what can you determine about the direction of the relationship between X and Y (i.e. is it positive or negative)? (2 points) c. If a boy scout sells popcorn for 4 days, how much money is he predicted to earn? (2 points)
The U.S. online grocery market is estimating sales worth approximately $29.7 billion by 2021. One of the biggest situational factors that influence the amount spent by a customer is the distance that that customer lives from its closest grocery store. Using the OLS method, the simple regression equation was estimated as: y = 40 + 3.5x. Find 1) the predicted amount a customer spends if they live 10 miles from the closest grocery store, as well as 2) the error amount. Note: the observed amount spent by a customer that lives 10 miles away is $85.50. a) $75.00, $10.50 Ob) $75.00, -$10.50 c) $73.00, $12.50 d) $73.00, -$12.50
The City of Bellmore’s police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 What is the IV? What is the DV? If the mileage increases by one mile, what is the predicted increase in maintenance costs? If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?
A shoe store developed the following estimated regression equation relating sales to inventory investment and advertising expenditures. ý = 35 + 13z1 + Bz where 1 = inventory investment ($1000s) I2 = advertising expenditures ($1000s) y=sales ($1000s) a. Predict the sales resulting from a $15,000 investment in inventory and an advertising budget of $10,000. b. Interprel b1 and ba in this estimated regression equation. b: Sales can be expected to Sclect your answer by $13 for every dollar increase in - Select your answer- vwhen - Select your answer is held constant. b: Sales can be expected to Sclect your answer by $6 for every dollar increase in Sclect your answer - when Select your answer is held constant. -Icon Key Exercise 15.04 Algo (Loast Square Mothod)
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An oceanographer measured the length, in meters, of a deepwater wave and its speed, in meters per second. The results are shown in the following table. (a) Find the equation of a linear regression line for the data where wave length is the independent variable, x, and speed is the dependent variable. (Round your numerical values to two decimal places.) y= ? (b) Using the equation from part (a), estimate the speed (in meters per second) of a wave that is 200 m long. (Round your answer to one decimal place.) ? m/s
The accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x=140 cm (b) x=172 cm (c) x=164 cm (d) x=158 cm Length, x Girth, y138 106168 130153 116144 105159 126158 119124 103137 103154 121148 111147 106145 110 The equation of the regression line is y=____x + ___
The table lists the average monthly cost to workers for family health insurance for various years. Year, x Average Monthly Cost to Workers for Family Health Insurance $298 a) Use a graphing calculator to fit a regression line to the data. b) Predict the average monthly cost to workers for family health insurance in 2020, and compare the value with $493.3, which is obtained using the points (1,340) and (4,386). c) Find the correlation coefficient for the regression line, and determine whether the line fits the data closely. 2009, 0 2010, 1 2011, 2 340 348 2012, 3 367 2013, 4 2014, 5 386 406 a) The linear equation of the regression line that best models the data is y =x+. (Round to the nearest hundredth as needed.) b) The average monthly cost to workers for family health insurance in 2020 is predicted to be $ (Round to the nearest cent as needed.) Compare the above obtained value with $493.3 This value is $ $493.3 c) The correlation coefficient is (Round to the nearest thousandth as…
Need help finishing the regression equation with the following: Y=Residents' Preference B=Size of sector budget, sector budget, and individual trust as Independent Variables. I'm using Jamovi software, but the need to create the equation with information above. Thanks,

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(a) Determine Sr. Syy. and Szy (b) Determine Bo, B. (c) Determine the estimated regression line equation.