Determine the simple linear regression model for the rental and interpret the fittedmodel. Provide justifications to the conclusion you reach based on Figure 1. Determineand interpret the coefficient of determination, ².Ben and Helen are considering to rent a unit of a condominium. They are trying todecide between two condominium units, one with 1000 square metres for a monthlyrental of RM1500 and the other with 1200 square metres for RM1800. Whichcondominium unit would you recommend to them? An agent from a property rental company would like to predict the monthly rental forcondominium based on the size as defined in square metres. A sample of 25condominiums was selected. Data is generated and the summary of outputs are shownin Table 1 and Figure 1.ANOVAdfSSMSF59.914SignificanceF7.52E-08RegressionResidual1 2268776.545 226877723 870949.4547 37867.37Total243139726StandardError161.00427t StatP-valueCoefficients177.12081.06511.100InterceptSize0.2820.137607.740 7.52E-08Table 1Size Residual Plot5004003002001000-100-200◆ 1000-300-400-500Residuals500SizeFigure 1+50020002500

Answered: Determine the simple linear regression…

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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Tutorial 8: Multiple Regression The table below reports the results of the regression analysis on the relationships between salary data for n=14 randomly sampled systems analysts from a large population of such individuals and their years of experience (Exper) and years of postsecondary education (Educ): Variable Coefficients Standard Error I-stat p-value Intercept EXPER 2690 204.6 45511 16.92 0.000 825.7 4.04 0.002 EDUC 1603.7 536.3 2.99 0.012 s= 2156 R-sq = 0.672 R-sq (Adj) = 0.613 ANOVA Source DF SS MS F-stat P 0.002 Regression Residual Total 2 104862168 52431084 11.28 11 51149976 4649998 13 156012144 Prediction Interval 3 Salary 4 5 Predicted value 56467 6. 7 Prediction Interval (95%) 8 Lower limit 51548 9 Upper limit 61386 10 Interval Estimate of Expected Value (95%) Lower limit 11 12 55180 13 Upper limit 57755
Please all the 3 of these sub-questions. Make sure answer is rounded up to 4 decimals
The table shows the numbers of new-vehicle sales (in thousands) in the United States for Company A and Company B for 10 years. The equation of the regression line is y = 0.991x + 1,222.81. Complete parts (a) and (b) below. D New-vehicle sales (Company A), x New-vehicle sales (Company B), y 4,149 3,923 3,566 3,400 3,266 3,076 2,868 2,485 1,952 2,066 4,912 4,871 4,827 4,721 4,672 4,474 4,684 3,822 2,956 2,754 (a) Find the coefficient of determination and interpret the result. 12²=0 (Round to three decimal places as needed.) 1
Find the multiple regression equation with weight as the response variable and the dummy variable of sex and the variable of age as the explanatory variables.
Choose the correct answer of each number. 1. An analyst hired by the multinational company to study the sales data of its more than 75 stores worldwide. Used regression analysis to predict $ sales (y) by using $ advertising (x1) and $ salary of sales representatives (x2) across all the branches. You obtained the following regression function: y = 7800 + 8.5x1-1.6x2. If the advertising budgets of one of the branches of the corporation is now $45,000 (which is 10% more than before) and the salary of sales representatives is now $8,500 (which is 20% less than before), then the predicted sales before in that branch is a. $417,750 b. $341,250 c. $376,700 d. $335,6502. An analyst hired by the multinational company to study the sales data of its more than 75 stores worldwide. Used regression analysis to predict $ sales (y) by using $ advertising (x1) and $ salary of sales representatives (x2) across all the branches. You obtained the following regression function: y = 7800 + 8.5x1-1.6x2. If…
The table shows the total square footage (in billions) of retailing space at shopping centers and their sales (in billions of dollars) for 10 years. The equation of the regression line is y = 516.388x - 1699.161. Complete parts a and b. Total Square Footage, X Sales, y 4.9 5.1 5.2 5.4 5.6 5.7 ********** (a) Find the coefficient of determination and interpret the result. 5.9 5.8 (Round to three decimal places as needed.) How can the coefficient of determination be interpreted? 5.9 883.3 937.6 986.3 1059.5 1107.2 1200.9 1281.1 1340.8 1438.2 1536. 6.2 OA. The coefficient of determination is the fraction of the variation in sales that can be explained by the variation in total square footage. The remaining fraction of the variation is unexplained and is due to other factors or to sampling error. OB. The coefficient of determination is the fraction of the variation in sales that is unexplained and is due to other factors or sampling error. The remaining fraction of the variation is…
The managing director of a consulting group has the accompanying monthly data on total overhead costs and professional labor hours to bill to clients. Complete parts a through c Click the icon to view the monthly data. a. Develop a simple linear regression model between billable hours and overhead costs. Overhead Costs = 247733.3 +(43.2000) x Billable Hours (Round the constant to one decimal place as needed. Round the coefficient to four decimal places as needed. Do not include the $ symbol in your answers.) b. Interpret the coefficients of your regression model. Specifically, what does the fixed component of the model mean to the consulting firm? Interpret the fixed term, bo. if appropriate. Choose the correct answer below. OA. The value of by is the predicted overhead costs for 0 billable hours. OB. For each increase of 1 unit in overhead costs, the predicted billable hours are estimated to increase by bo OC. It is not appropriate to interpret by. because its value is the predicted…
please use this situation: A small theater company has a linear regression model to estimate y = the concession stand sales in dollars, based on knowing x = the number of people in attendance. The regression equation is: = 6.72x + 11.50 and the correlation coefficient was r = 0.781. The data set saw the number of people in attendance ranging from a minimum of 18 people to a maximum of 170 people. 1) How reliable would it be to make a prediction for the concession sales amount if there were 500 people in attendance? Explain.
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY - 3.17 +0.823HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2700 lb, it has an engine displacement of 1.6 L, and its highway fuel consumption is 35 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.). Regression Table I R² Adjusted R2 WT/DISP WT/HWY Predictor (x) Variables P-Value WT/DISP/HWY 0.000 0.942 0.000 0.748 0.000 0.942 0.000…
H. Find the slope and y-intercept of each regression line. Then, determine if the correlation between the variables is positive or negative. (12 points) Regression Line Equation 1. Y = 1.751X – 21.722 Slope y-intercept Kind of Correlation 2. Y = -221.943X+65.434 3. Y = -82.455X –314.986 4. Y = 0.997X + 0.168
The volume (in cubic feet) of a black cherry tree can be modeled by the equation y = - 50.8 + 0.3x, +4.5x,, where x, is the tree's height (in feet) and x, is the tree's diameter (in inches). Use the multiple regression equation to predict the y-values for the values of the independent variables. x, = 70, x, = 8.6 The predicted volume is cubic feet. (Round to one decimal place as needed.)
Solve for the y-intercept and the slope. Form the equation of the regression line that describes the relationship between the two variables. Predict the profit of the fashion boutique business when the store measures 3,800 square feet. Explain the result.

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