Problem:Using historical climate data (temperature, humidity, wind speed, etc.) from multiple regions,construct a large matrix representing daily weather variables for the past 10 years. Perform thefollowing:•Use Principal Component Analysis (PCA) to reduce the dimensionality of the dataset whileretaining 95% of the variance.Reconstruct the reduced dataset and calculate the error matrix.Analyze which regions have the most variation in weather patterns and explain why.Data Source: Public climate databases like NOAA or IMD.

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Answered: Problem: Using historical climate data…

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 31EQ

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Beachcomber Ltd in a local car dealership that sells used and new vehicles. The manager of thecompany wants to know how different variables affect the sales of his vehicles. A random sample ofyearly data was taken with the view to testing the model:SALES=?+?AGE+?MIL+?ENGWhere SALES= amount that a vehicle is sold for($000’s), AGE = age of the vehicle, MIL= the totalmileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data wasprocessed using MINITAB and the following is an extract of the output obtained:The regression equation is *****PredictorCoef StDev t-ratio p-valueConstant1.7586 0.2525 6.9648 0.0000AGE 0.2124 0.3175 * 0.5042MIL -0.75270.3586 -2.0991 **ENG 4.8124 0.6196 7.7664 0.0000Analysis of VarianceSource DF SS MS F pRegression3 413.1291 138.7097*** 0.00Error 50 457.7607 2.2888Total 53a) What is dependent and independent variables? b) Fully write out the regression equation c) Fill in the missing values ‘*’, ‘**’, ‘***’and ‘***’. d) Hence…
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