esThe accompanying data file contains 40 observations on the response variable y along with the predictor variables x1 and x2. Use theholdout method to compare the predictability of the linear model with the exponential model using the first 30 observations for trainingand the remaining 10 observations for validation.Click here for the Excel Data Filea-1. Use the training set to estimate Models 1 and 2.Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.Predictor VariableConstantX1X2Model 1 (Linear) Model 2 (Exponential)a-2. Calculate the RMSE of the two models in the validation set.Note: Do not round intermediate calculations and round final answers to 2 decimal places.RMSEModel 1 (Linear)Model 2 (Exponential) 1Home Insert Draw Page LayoutH11Paste23456700 ax1533.86 20104.84 1564.8920159.61 1643.06134.27138 736.56 159 64.892010 10.642011 76.901812 20.001113 80.901114 224.17 1215 45.751616 8.131717 319.97 1318 48.611919 564.67 1220 111.87 1121 152.39 1322 13.341823 28.801524 37.561325 105.62 1726 44.051827 451.65 1728 10.341829 32.701230 19.21AY1431 14.021532 2.451633 2.482034 50.341735 29.311736 20.001637 196.28 1738 943.12 1339 7.251040 89.731541 32.9112424344BCalibri (Body)I UBx230202321161330232220131619251730252725241422152621282113121612152120122930122518fxC▼ 11DEFormulasA- A-Exercise_7.57+Select destination and press ENTER or choose PasteFGData ReviewHabI▼E+ViewJAcrobatWrap Text ▾→ Merge & Center ▾KCh8_Q40_V14_Data_File (5)LGeneralM% >N+.0 .00.00 ➡.0O#Conditional FormatFormatting as TablePQRCellStylesSInsert ▾Delete ▾Format ▾TUΣV▼A.Q.Sort & Find &Filter SelectVOQ Search SheetWAICreate and ShareAdobe PDFX+ Share ✔YN+ 100%AA

a-1: Linear Regression: Excel Procedure: Enter X1, X2 and Y data in Excel Go to Data Click on Data…

Answered: The accompanying data file contains 40…

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 data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 760 1119 1125 850 965 785 D Temperature (°F) 70.5 90.8 90.8 74.7 77.1 69.9 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)
Refer to the Baseball 2018 data, which reports information on the 2018 Major League Baseball season. Let attendance be the dependent variable and total team salary be the independent variable. Determine the regression equation and answer the following questions. Click here for the Excel Data Filea-1. Draw a scatter diagram.1. On the graph below, use the point tool to plot the point corresponding to the Attendance and its team salary (Salary 1).2. Repeat the process for the remainder of the sample Salary 2, Salary 3, … ).3. To enter exact coordinates, double-click on the point and enter the exact coordinates of x and y. a-2. From the diagram, does there seem to be a direct relationship between the two variables?multiple choice 1 Yes No b. What is the expected attendance for a team with a salary of $100.0 million? (Round your answer to 4 decimal places.) c. If the owners pay an additional $30 million, how many more people could they expect to attend? (Round your answer to 3…
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
You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ý = 0.406 + 1.3385X, + 2X2 The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ý = 1.9 – 3X + 12X2 + 4Xg + 8x, This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = B₂= B3 =B4 = 0 O Ho: One or more of the parameters is not equal to zero. H₂: B3 =B4 = 0 O Ho: B3 = P4 = 0 H₂: None of the parameters are equal to zero. ⒸH₁: B3 =B₁ = 0 H: One or more of the parameters is not equal to zero. O Ho: B₁ = P₂ = B3 =B4 = 0 H: One or more of the parameters is not equal to zero. ✔ Find…
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…
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. Xx Y 11.8 14.1 11.5 9.8 7.1 6.2 6.5 x = thousands of automatic weapons y = murders per 100,000 residents 8.6 7 3.3 2.5 2.5 2.6 6 This data can be modeled by the equation y = 0.85x +4.07. Use this equation to answer the following; Answer decimal places. = A) How many murders per 100,000 residents can be expected in a state with 1.2 thousand automatic weapons? Answer = decimal places. Round to 3 0.9 4.7 B) How many murders per 100,000 residents can be expected in a state with 8.5 thousand automatic weapons? Round to 3
I need in one hour pls help thankyou
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 =
A financial website reported the beta value for a certain company was 0.86. Betas for individual stocks are determined by simple linear regression. The dependent variable is the total return for the stock, and the independent variable is the total return for the stock market, such as the return of a market index. The slope of this regression equation is referred to as the stock's beta. Many financial analysts prefer to measure the risk of a stock by computing the stock's beta value. Suppose the following data show the monthly percentage returns for the market index and the company for a recent year. Month Market Index% Return Company% Return August -3 4 September 8 7 October 0 1 November -2 1 December -5 0 January 0 0 February 7 7 March 0 -2 April 2 0 May -5 -1 a. Develop the least squares estimated regression equation. (Let x = Market Index % Return (as a %), and let y = Company % Return (as a %). Round your numerical values to four decimal places.)
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 924 1150 840 1166 1087 930 Temperature (°F) 77.5 84.6 74.1 91 79.6 79.7 What is the regression equation? What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? What is wrong with this predicted value? Choose the correct answer below. A. It is unrealistically high. The value 3000 is far outside of the range of observed values. B. The first variable should have been the dependent variable. C. It is only an approximation. An unrounded value would be considered accurate. D. Nothing is wrong with this value. It can be…

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