Question 3Bluereef real estate agent wants to form a relationship between the prices of houses, how manybedrooms, House size in sq ft and Lot Size in sq ft.The data pertaining to 100 houses were processed using MINITAB and the following is an extractof the output obtained:The regression equation is Price = ß + Bedroom + yHouse Size + ¿Lot SizePredictorCoefSE CoefTP**Constant3771823062.660.33Bedrooms74.3*-4.36House SizeLot SizeS= 25023SourceDFRegression 3ResidualErrorTotalR-Sq-56.0%14177699452.9817.0296 6010904605399SSMS76501718347 255005727826261358960.7420.164-0.26 0.798R-Sq (adj) -54.6%FP****a) Write out the regression equation.b) Fill in the missing values *, **, *** and ****.c) Use the p-value approach to determine if & is significant at the 5% significance level

Introduction The linear regression shows the relationship b/w independent variables and dependent…

Question 3 Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = ß + Bedroom + yHouse Size + ALot Size Predictor Coef SE Coef T P Constant 37718 2.66 Bedrooms 2306 0.33 74.3 -4.36 House Size Lot Size S= 25023 Source DF SS Regression 3 76501718347 Residual Error Total R-Sq-56.0% 96 99 60109046053 14177 6994 52.98 17.02 0.742 0.164 -0.26 0.798 R-Sq (adj) -54.6% MS 25500572782 626135896 P a) Write out the regression equation. b) Fill in the missing values *, **, and c) Use the p-value approach to determine if & is significant at the 5% significance level

Question 3 Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = ß + Bedroom + yHouse Size + ALot Size Predictor Coef SE Coef T P Constant 37718 2.66 Bedrooms 2306 0.33 74.3 -4.36 House Size Lot Size S= 25023 Source DF SS Regression 3 76501718347 Residual Error Total R-Sq-56.0% 96 99 60109046053 14177 6994 52.98 17.02 0.742 0.164 -0.26 0.798 R-Sq (adj) -54.6% MS 25500572782 626135896 P a) Write out the regression equation. b) Fill in the missing values *, **, and c) Use the p-value approach to determine if & is significant at the 5% significance level

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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Listed below are the numbers of commuters and the number of parking spaces at different Metro-North railroad stations. Use technology (calculator) to help you answer the following, and round to the 3 decimal places where rounding is necessary. . a. Find the linear regression line y = a + bx. b. Are the variables positively or negatively related? c. Find and interpret r2. Make sure to include what it means specific to this data set. d. Use your regression line to make a prediction for the number of parking spaces for a station with 900 e. Identify and interpret the slope of the linear model.
State the regression equation and use it to predict taxes for a house with lot size 10K.
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Which of the following is the most appropriate equation to model the data? ŷ = –10.89x + 0.85 ŷ = –0.85x + 10.89 ŷ = 10.89(0.85)x ŷ = 0.85(10.89)x
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.3 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.3 253.2 258.7 278.7 257.8 274.2 262.2 Height 1784.8 1771.0 1675.6 1645.9 1858.7 1710.1 1789.2 1737.4 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 two decimal places as needed.) The best predicted height of a male with a foot length of 273.3 mm is enter your response here mm. (Round to the nearest integer as needed.)
The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance. Copy-paste the data and run the analysis using MS Excel. starting salary GPA Years of experience Civil Service Ratings 15000 80.1 1 79.5 15000 81.2 1 78.0 15500 81.3 2 79.0 16000 82.4 3 80.0 16200 83.4 3 85.0 17500 87.9 4 89.9 18000 90.3 5 89.1 16300 84.2 3 84.1 17000 87.0 4 89.0 17900 88.1 5 89.2 What is the computed R square of the resulting multiple linear regression and its interpretation?
The following data represent the number of flash drives sold per day at a localcomputer shop and their prices.Price Units Sold34 336 432 635 530 938 240 1a. Develop the estimated regression equation that could be used to predict thequantity sold given the price. Interpret the slope.b. Did the estimated regression equation provide a good fit? Explain.c. Compute the sample correlation coefficient between the price and the number offlash drives sold. Use a= 0.01 to test the relationship between price and units sold.d. How many units can be sold per day if the price of flash drive is set to $28.
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7 Height 1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.7 mm is (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O B. The result is exactly the same as the actual height of 1776 mm. O C. The result is very different from the actual height of 1776 mm. O D. The result does not make sense given the context of the data. C mm.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.6 8.5 7. 3.5 2.6 2.3 2.4 0.4 13.8 11 10 6.3 6.1 5.9 4.1 I = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.85T + 3.97. Use this equation to answer the following; A) How many murders per 100,000 residents can be expected in a state with 1.2 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 2.2 thousand automatic weapons? Answer = Round to 3 decimal places. Question Help: O Message instructor Submit Question to search 1O
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the fight 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 90 mm Hg. Use a significance level of 0.05. Right Arm Left Arm % 102 101 94 80 79 177 172 143 143 143 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y=+x. (Round to one decimal place as needed.). Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed) M H N H & C Copyright ©2022 Pearson Education Inc. All rights reserved. | Terms of Use | Privacy Policy | Permissions | Contact Us | a 33 M 0 8 K Vi 1. fio 11 O More (2) { 87°F Next [ insert prt sc 7:46 8/5/2 backspace
A linear relationship between EmployeeSalary (Dependent) and degree(independent) has the following equation : Salary = 400+0.2 (Degree). SST= 736, SSR= 385. Calculate and interpret the coefficient of determination (r2) : Select one: O a. 0.48 , 47.69 percent of the variability in employee salary can be explained by the simple linear regression equation Ob. 0.52,52.31 percent of the variability in employee salary can be explained by the simple linear regression equation Oc. 0.48, 47.69 percent of the variability in the degree earned can be explained by the simple linear regression equation F Od. 0.52, 52.31 percent of the variability in the degree earned can be explained by the simple linear regression equation Next page JUN 2 12 étv W Ps Lr
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.8 8.2 6.9 3.6 2.5 2.8 2.6 0.4 14 11.3 10.2 7.4 6.5 6.3 5.9 4.5 x thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equationý = 0.85x + 4.12. Use this equation to answer the following; %D A) How many murders per 100,000 residents can be expected in a state with 7.3 thousand automatic weapons? Answer = Nearest whole value. B) How many murders per 100,000 residents can be expected in a state with 1 thousand automatic weapons? Nearest whole value Answer =