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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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.
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