5.3 The data shown below present the average number of surviving bacteria in a cannedfood product and the minutes of exposure to 300° F heat.Number of BacteriaMinutes of Exposure17511082953824715506497318289171016111112c. Identify an appropriate transformed model for these data. Fit this model to the data andconduct the usual tests of model adequacy.

Given data: Number of Bacteria Minutes of Exposure 175 1 108 2 95 3 82 4 71 5 50 6…

Answered: 5.3 The data shown below present the…

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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An unknown metal has been found and the following experimental results have been tabulated in the table below. The table contains the grams of the unknown metal and the volume in milliliters of water displacement. Find a linear model that expresses volume as a function of the mass. grams Volume in ml 20 222.3 21.5 246.2 23 258.2 24.5 277.8 26 291.9 27.5 318.1 29 322.3 A) Write the linear regression equation for the data in the chart.Volume = �+ where x is the grams of the unknown metal. Round your answers to 3 decimal places B) If the mass of an unknown metal is 18, using your un-rounded regression equation find its predicted volume. Round your answer to 1 decimal place.
Given are five observations for two variables, x and y. xi 5 9 13 17 20 yi 50 54 46 23 11 The estimated regression equation for these data is ŷ=0.6+2.6x a. Compute SSE, SST, and SST (to 2 decimals) and show work. SSE=________ SST=_________ SSR=_________ b.Compute the coefficient of determination r^2. Comment on the goodness of fit. The least squares line provided a good fit; ________% of the variability in y has been explained by the estimated regression equation (to 3 decimal). Show work. c. Compute the sample correlation coefficient. Enter negative value as negative number. Show work. ___________________
Use regression to find a quartic model for the given data set. Round the regression parameters to three decimal places. 1 3 1 2 y = 15 10 5 X Y 15 Plot the data along with the quartic model. y 2 4 H 44 4 62 7 1 6 8 3 10 9 8 10 15 10 15 y 101 2 4 6 8 10 x
X- 36.000 9-1.456 Step 3. After compleling the table, present following computations and the interpretation. Calculate the correlation coetticient Ir). using the working formula: o The sign or integer before the value just interprets the value as either directly or inversely proportional. Just consider the rounded-off value il it is equal to I or 0. o If the computedr>0, it means that there is a perfect correlation between the variables. Therefore, there will be a need to continue solving for the slope and y intercept, No further calculations will be made it r
5:32 PM Tue Mar 1 + 94% A webassign.net A statistical program is recommended. A sales manager collected the following data on x=- years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 83 + 4x. Years of Experience Annual Sales ($1,000s) Salesperson 1 80 3. 97 4 102 4 4. 107 6 103 6. 8 116 7 10 119 8 10 123 11 127 10 13 136 (a) Compute the residuals. Years of Annual Sales Experience ($1,000s) Residuals 80 87 3. 97 95 4 102 99 4 107 99 6. 103 107 8 116 115 10 119 123 10 123 123 11 127 127 13 136 135 Construct a residual plot. 16 16 12 12 8- 8 4 4 -4 -4 -8- -8- -12 -12 -16 -16 2 4 6 8 10 12 14 2. 4 6 8 10 12 14 Years of Experience Years of Exmerience Residuals Residuals
The following table shows the hot dogs bought from a street vendor over the course of eight days ("Demand"). Also shown is the temperature for each day in degrees Celsius. Temperature (°C) 19 Demand 45 10 30 23 34 17 42 a. Calculate the SST. SST = (Round to two decimal places as needed.) 6 19 19 O 13 16 22 42 32 A linear regression on the data gives the equation below. Complete parts a through d below. Predicted Demand = 15.58+1.15(Temperature) (...)
An unknown metal has been found and the following experimental results have been tabulated in the table below. The table contains the grams of the unknown metal and the volume in milliliters of water displacement. Find a linear model that expresses mass as a function of the volume. grams Volume in ml 17 136.1 18.5 146.7 20 160.1 21.5 168.8 23 182.4 24.5 190.4 26 206.1 A) Write the linear regression equation for the data in the chart. Volume = where x is the grams of the unknown metal. Round your answers to 3 decimal places B) If the mass of an unknown metal is 15, using your un-rounded regression equation find its predicted volume. Round your answer to 1 decimal place. mL Submit Question Ce 24 &
Tar 25 27 20 24 20 20 21 24 CO 18 16 16 16 16 16 14 17 Nicotine 1.5 1.7 1.1 1.6 1.1 1.0 1.2 1.4 Multiple Regression with Cigarettes Use the sample data given in Exercise 1 “Cigarette Tar and Nicotine.” a. Find the multiple regression equation with the response (y) variable of amount of nicotine and predictor (x) variables of amounts of tar and carbon monoxide. b. Identify the value of the multiple coefficient of determination R2, the adjusted R2, and the P-value representing the overall significance of the multiple regression equation.c. Use a 0.05 significance level and determine whether the regression equation can be used to predict the amount of nicotine given the amounts of tar and carbon monoxide. d. The Raleigh brand king size cigarette is not included in the table, and it has 23 mg of tar and 15 mg of carbon…
Question 8 please
I need to find the two models in part b. Please give final answer
Item9 eBook Item 9 The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 9.601 3.153 3.045 0.010 x1 0.221 0.110 2.009 0.000 x2 − 1.168 0.577 − 2.024 0.028 x3 − 0.106 0.073 − 1.452 0.114 x4 0.631 0.361 1.748 0.001 x5 − 0.043 0.029 − 1.483 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 1,844.31 368.9 9.37 0.000 Residual Error 56 2,205.62 39.39 Total 61 4,049.93 x1 is the number of architects employed by the company. x2 is the number of engineers employed by the company. x3 is the number of years involved with health care projects. x4 is the number of states in which the firm operates. x5 is the percent of the firm’s work that is health…
QUESTION 1 Mr Stat was hired to do some research for the local potion master. He ran a Minitab analysis and found the regression equation for the age increase in years based on the quantity in milliliters, of Ageing Potion one consumed. Age Increase = 0.045*Quantity %3D a) What is the explanatory variable? Quantity b) What is the response variable? Age Increase Suppose the quantities in the st anged from 150ml to 740ml. If unable to predict, answer with n/a. c) How many years would someone's age increase if they drank 200ml? d) How many years would someone's age increase if they drank 100ml? n/a

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