The annual production of a commodity is given by as follows:Year2005200620072008200920102011Production70809095102110115(in tonnes)(i)(ii)Fit a straight line Trend by the method of least squares.Convert the annual trend equation into a monthly Trend equation

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Answered: The annual production of a commodity is…

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 income of farmers depends on various factors. To predict the income of the next year, a study was undertaken and data was gathered considering as many as possible factors that might influence the yearly income. Regression methods are used to create such a prediction function, that is, we want to predict the profit for the next year. The following were determined. X₁ = SIZE - farm size recorded x 1000 hectares X₂ = AGE - how long the farm has been in operation in years X3 = RATIO- the ratio of land size to field size recorded as 0.5, 0.75, 0.8 and 0.9 X4 = METHOD - rotational and non rotational method of planting Ŷ = INCOME the income per year recorded in x R 1 000 000.00 The partial dataset is as follows INCOME Y 1.3 2.4 3.2 1.5 2.1 a. C. Standardized Residual Sample Quantiles 15 20 1.0- 0.8- 0.6- 04- 02- 0.0- SIZE X₁ 25 5 8 2 1.2 1.5 Predicted Theoretical Quantiles 45 AGE X₂ 2 20 100 1.1. The analyst did some exploratory analysis and below are some of the residual plots he…
mex PLC has gathered following information on the sales of face mask from April 2020 to september 2020. Month Sales ($) April 17,000 May 18,000 June 19,500 July 22,000 August 21,000 September 23,000 You are required to3; a. Using linear trend equation forecast the sales of face masks for October 2020.
A doctor wanted to determine whether there is a relation between a male’s age and his HDL (so-called good cholesterol). He randomly selected 9 of his patients and determined their HDL cholesterol. The data is reported in the table below: a) Compute the regression equation for HDL as a function of age. b)Interpret the meaning of the regression parameters.
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It is believed that the electrical energy that a chemical plant consumes each month is related to the average environmental temperature, the number of days in the month, the average purity of the product, and the tons of product manufactured. Historical data is available for the previous year and is presented in the following table (seen in the image=. Perform simple linear regression analysis
(c) Th Data Table Total Length (cm) 138.0 135.0 130.0 120.5 149.0 141.0 141.0 150.0 166.0 151.5 129.5 150.0 Print Weight (kg) 110 60 90 60 85 105 95 85 155 140 105 110 Done 0 X Critical Values for the Correlation Coefficient 3 0.997 4 0.950 5 0.878 6 0.811 7 0.754 8 0.707 9 0.666 10 0.632 11 0.602 12 0.576 13 0.553 14 0.532 15 0.514 16 0.497 17 0.482 18 0.468 19 0.456 20 0.444 21 0.433 22 0.423 23 0.413 24 0.404 25 0.396 26 0.388 27 0.381 0371 X not practical to v parts (a) through
The following is a monthly trend equation : Y = 40 + 4X %3D Note: [origin Jan. 2012, X-unit = one month, Y-unit = monthly sales (in 000 rupees]. %3D Convert it into an annual trend equation.
The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature x1, the number of days in the month x2, the average product purity x3, and the tons of product produced x4. The past year's historical data are available and are presented in the following table. y 240 236 290 274 214 301 316 910 300 300 296 x1 25 31 45 60 00 65 99 72 14 X₂ 24 21 24 25 20 25 20 26 20 25 20 80 90 84 267 75 276 60 288 50 261 38 23 25 24 24 25 25 X3 91 90 88 00 87 91 94 2 87 DA 86 20 88 91 90 89 X₁ 100 95 110 88 94 99 97 96 110 105 100 98 Predict power consumption for a month in which x₁ =75°F, x₂ =24 days, x3 =90%, andx4 = 98 tons. Use 1 decimal place.
1.2. In order to prepare the dataset for modelling, it became clear that RATIO repeats with similar values. The analyst decided to regard them also as categorical variables. Amend the dataset fully in order to build a regression model. Use an ascending order in the coding structure. 1.3. Show the new amended data set and formulate the regression model that must be estimated. Apply all the changes done to the dataset in Question 1.2 and give the regression function. 1.4. The estimated response function is obtained using the coding structure of Question 1.2. Ŷ = 1.34 + 0.034X₁ + 0.049 X₂ -0.041 X3 + 0.029X4 +0.011 X5 +0.08X6. Explain the regression coefficients b₁, b, and b. 1.5. Find the fitted response function for the rotational METHOD of farming on a farm with RATIO of 0.8. 1.6. Refer to Question 1.5. The analyst has a suspicion that there is interaction between the size and the age of the farm if the method of farming is rotational and the field/land ratio is 0.8. Give the response…
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design a linear model for this data and jusify this mode(state why it is better to use this model than others)

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The annual production of a commodity is given by as follows: Year 2005 2006 2007 2008 2009 2010 2011 Production 70 80 90 95 102 110 115 (in tonnes) (i) (ii) Fit a straight line Trend by the method of least squares. Convert the annual trend equation into a monthly Trend equation