2. A researcher feels that global warming may be reducing average rainfall in Perlis for the past 10 years. The researcher is interested in making predictions for future rainfall. The 10 years data of the rainfall in Perlis is shown in Table 4. (a) Without using any software, construct a simple linear regression model for the above data. (b) Predict the rainfall for the year 2019 and 2020.

2. A researcher feels that global warming may be reducing average rainfall in Perlis for the past 10 years. The researcher is interested in making predictions for future rainfall. The 10 years data of the rainfall in Perlis is shown in Table 4. (a) Without using any software, construct a simple linear regression model for the above data. (b) Predict the rainfall for the year 2019 and 2020.

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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Suppose you are interested in estimating the impact of different inputs (temperature, rainfall, fertilizer) on agricultural crop yields. Your unit of analysis is farm-level. In writing the structural regression equation, you want to control for the organic carbon soil content (soil quality, essentially). However, you cannot find data on soil quality when you go to estimate the equation. Write out your theoretical regression equation and the estimated regression equation. What happens as a result of not having data on soil quality? Specifically, show how you determine whether your estimates are upper or lower bounds of the true estimates of the impact of fertilizer. Theoretical: Y = Bo + B₁T + B₂R+B3F + B4S + ε Y = Bo + B₁T + B₂R+B3F Estimated:
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1. The number of pounds of steam used per month by a chemical plant is thought to be related to the average ambient temperature (in degF) for that month. The past year’s usage and temperature are shown in the following table: (a) Assuming that a simple linear regression model is appropriate, fit the regression model relating steam usage (y) to the average temperature (x). (b) What is the estimate of expected steam usage when the average temperature is 55F?
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None
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Linear regression is a highly effective data analysis method that accurately estimates the value of unknown data using related and known data. This is achieved using a linear equation that accurately models the quantitative relationship between the dependent (unknown) and independent (known) variables. In other words, by looking at how one variable affects another, this method can accurately forecast the value of the dependent variable. It involves choosing the variable to forecast and using another variable to make informed predictions about its value. In experiments, the independent variable is the cause, and its value remains constant while other variables have been modified. On the other hand, the dependent variable is the effect, and changes influence its value in the independent variable. As I began my business, I struggled with determining the appropriate pricing for my products. It was important that the prices were reasonable while allowing for a profit. The pricing had to…
In Step 2: Construct an estimated simple linear regression model how did you come up with the column X*X ?