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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 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

MATLAB: An Introduction with Applications
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 constructed. Study the plots and answer the questions that follows. 80 50 11 b. 3 RATIO X3 Standardized Residual 0.5 0.75 0.8 0.8 0.75 100 120 140 160 180 METHOD X4 Predicted Rotational Rotational Non-rotational Rotational Non-rotational Diagnose each of the possible problems that are displayed in the plots. If applicable also mention a formal way to test that it is indeed a problem. Also, when a problem is diagnosed, provide a possible remedial measure to remedy the problem. 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.
Transcribed Image Text: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 constructed. Study the plots and answer the questions that follows. 80 50 11 b. 3 RATIO X3 Standardized Residual 0.5 0.75 0.8 0.8 0.75 100 120 140 160 180 METHOD X4 Predicted Rotational Rotational Non-rotational Rotational Non-rotational Diagnose each of the possible problems that are displayed in the plots. If applicable also mention a formal way to test that it is indeed a problem. Also, when a problem is diagnosed, provide a possible remedial measure to remedy the problem. 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.
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