An experiment is conducted to understand how the yield of a bacteria culture is affected by the amount of an additive (g) and temperature ( oC). The following multiple quadratic regression model was developed where x1 indicates the additive amount and x2 indicates the temperature. REGRESSION MODEL: Y ~ 1 + X1+ X2+ X1*X2 + X1^2 + X2^2 ESTIMATED COEFFICIENTS: ESTIMATE SE TSTAT PVALUE _________ _________ _______ __________ (INTERCEPT) -1194.7 194.48 -6.1432 0.00010929 X1 414.99 51.696 8.0274 1.1427E-05 X2 9.5278 2.2312 4.2702 0.0016365 X1:X2 -1.0003 0.1867 -5.3578 0.00032001 X1^2 -46.042 7.7794 -5.9184 0.00014735 X2^2 -0.021687 0.0070014 -3.0976 0.011297 Using all the terms of the regression model given above, estimate the bacteria yield when the amount of additive is 2.3 g and the temperature is 140 oC.. a) 201.3145 b) There is not enough data to estimate the bacteria yield c) 102.9450 d) 817.2489 e) 1135.168
Regression model:
From above given output, regression equation is:
x1= additive=2.3
x2=temperature= 140
Answer: C
An experiment is conducted to understand how the yield of a bacteria culture is affected by...
please show all steps no excel. i do not understand how to do
the regression shit
Questions 27-30: A prediction model for the selling price (in thousands) of a house is to be developed. It's believed that the selling price is influenced by the assessed values (X1. Assessed, in thousands) of the house, the amount of time it took house to sell (X2. Time, in months) and whether the house is a new house or an old house (K3, Ne...
1. One Price Realty Company wants to develop a model to estimate the value of houses in its inventory The office manager has decided to develop a multiple regression model to help explain the variation in house values. (25 points) The office manager has chosen the following variables to develop the model: X1 square feet X2- age in years x3- dummy variable for house style (1 if ranch, 0 if not) X4-2d dummy variable for house style (I if split...
PLEASE SHOW ALL EXCEL FORMULAS USED FOR EACH CALCULATIONS. A
STEP BY STEP WALKTHROUGH OF HOW TO DO THE PROBLEM. Thank you so
much for your help!
Hours Feet Elevator Elevator
code
24.00 545 Yes 1
13.50 400 Yes 1
26.25 562 No 0
25.00 540 No 0
9.00 220 Yes 1
20.00 344 Yes 1
22.00 569 Yes 1
11.25 340 Yes 1
50.00 900 Yes 1
12.00 285 Yes 1
38.75 865 Yes 1
40.00 831 Yes 1...
Reserve Problems Chapter 11 Section 2 Problem 1 The department of health studied the number of patients who need liver transplantation. The following data are the Liver Transplantation Waiting List (LTWL), where y is the size (in number of patients) and x is the corresponding year: 1278 1761 2917 3923 4983 6914 7655 7596 77378524 8376 8639 8946 x 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Round your intermediate answers to four decimal places...
please help me answer the all question ..thanks a lot
2. Berikut adalah keputusan penganggaran persamaan regresi untuk mengkaji penentu tekanan darah sistolik. Below is the regression output showing the results of an examination into the determinants of systolic blood pressure SUMMARY OUTPUT Regression Statistics Multiple R R Square 0.9907 0.9815 4.5467 Standard Error Observations 20 ANOVA df MS Regression Residual Total 3 17560.0464 5853.3488 330.7536 20.6721 17890.8 16 19 Coefficients Standard Error tStat 12.9037 0.5581 Intercept Xt 7.2017 2.4077...