hey,

This is an assignment with two parts I attached here.

The first parts (Correlation and Regression analysis) are problems and solve it in word document

and the second part (Python application) for python programming send it with (Notebook format)

1INTRODUCTION

This project investigates the autocorrelation of a random signal. You will use the techniques of simulation you have developed in previous projects, plus apply the built-in MATLAB function xcorr. Because this analysis is fairly complicated, I’ve created a detailed skeleton of a MATLAB script to guide you.

1.1 Due Date

1.2 Background

A random process, , is defined as a collection of time functions governed by one or more random variables. Specific random time functions within the collection are called sample functions of the random process. For each instant in time, is itself a random variable which can take on different values at each value of and across all the sample functions . Because the collection of all of the values, across the collection of sample functions represent the values of a random variable, it is meaningful to speak of the pdf of the random variable at a specific instant in time. Mathematically, this pdf may change at every instant in time. As with random variables, it is meaningful to speak of the joint pdf, , and, in fact of this joint pdf for any collection of times, ,namely, . A random process for whom joint probability density remains the same no matter what the value of , and no matter what the specific choice of the times, is called strict sense stationary. Knowing this full joint pdf is difficult; we usually assume that we know only the second order joint pdf, that is, for any valid choice of With this knowledge, we can define a process as Wide Sense Stationary (WSS) if the following two conditions hold. (1.1) X(t) X(t 0 ) t 0 x(t 0 ) x1(t 0 ), x2 (t 0 { ),… } x1(t), x2 { (t),…} f X (t 0 ) (x(t 0 )) f X (t 0 ) X (t 1 ) x(t 0 ), x(t 1 ( )) t 0 ,t 1,t 2 ,…t { N } f X (t 0 ) X (t 1 )…X (t N ) x(t 0 ), x(t 1),…, x(tN ( )) N f X (t 1 ) X (t 2 ) x(t 1), x(t 2 ( )) t 1 and t 2. E X(t 0 ⎡ ) ⎣ ⎤ ⎦ = C (a constant) for all t 0 RXX (t 1,t 2 ) = RXX (t 1,t 1 +τ ) = RXX (t 1 +τ ,t 2 ) = E X(t 1)X(t 2 ⎡ ) ⎣ ⎤ ⎦ = RXX (τ ) 2 The function is called the autocorrelation function. The second condition of equation (1.1), states that the autocorrelation function of a WSS process is a function of only the time difference and not of the specific times . This project provides some experience with the autocorrelation function. It is important to note that the expected value shown in equation (1.1) is not a time average. Remembering that at every instant the random process is a random variable with an associated pdf the expected value in equation (1.1) is given by (1.2) It is not a time integral at all, but a true expected value evaluated at one instant in time. Equation (1.1) says that for a WSS process, this expected value is a constant. Similarly, given the joint pdf and the two time instants, , the second order expected value (1.3) This, too, is not a time integral, but a second order statistical expected value. Finally, a very important special case of random processes do have the property that a very long term time average of the random process gives the same numeric value as the statistical expected value. In this case the time average, which we write is equal to the expected value, . Such random processes are called ergodic. By the nature of the random number generators used in MATLAB, random sequences generated in MATLAB (and most other programs, too) possess this property, unless they are specifically modified by the user. All of the examples we have used in previous projects are ergodic. Given the assumption of ergodicity, MATLAB uses a time averaging method to estimate the statistical autocorrelation process. The MATLAB function that does this is xcorr. Because the sequences are not (and cannot be) infinitely long, however, use of xcorr is only an approximation, and produces an autocorrelation waveform, , that is, itself a random process. Let’s see how this works! 2 PROJECT TASKS Perform the following tasks, document your results and submit them in written form in accordance with the instructions in Section 3, below. You may use this document as a format. RXX (t 1,t 2 ) = E X(t 1)X(t 2 ⎡ ) ⎣ ⎤ ⎦ t 2 − t 1 t 1 and t 2 t 0 X(t 0 ) E[X(t 0 )] = x(t 0 ) f X (t 0 ) (x(t 0 ))dx(t 0 ) −∞ ∞ ∫ t 1 and t 2 RXX (t 1,t 2 ) = x(t 1)x(t 2 ) f X (t 1 ) X (t 2 ) (x(t 1), x(t 2 ))dx(t 1)dx(t 2 ) = RXX (t 2 − t 1)

I’m working on a python exercise and need an explanation and answer to help me learn.

Please download chalrsebookclub dataset from the link – https://www3.nd.edu/~busiforc/problems/DataMining/dataminingdatasets.html (since i cannot attach the csv)

This dataset examines associations among transactions involving various types of books. The database includes 2000 transactions, and there are 11 different types of books. Please use jupyter notebook and python

a) Drop the following columns ‘Seq#’, ‘ID#’, ‘Gender’, ‘M’, ‘R’, ‘F’, ‘FirstPurch’, ‘Related Purchase’, ‘Mcode’, ‘Rcode’, ‘Fcode’, ‘Yes_Florence’, ‘No_Florence’. Plot the support count for each item in the dataset using barplot.

b) Find the frequent itemsets with the min_support =5%.

d) Define min_confidence= 0.5 and print the 25 rules with the highest lift alongside their corresponding support, confidence, and lift.

e) Filter rules by number of antecedents (maximum 2) and consequents (maximum 1) and print the rules with their support, confidence, and lift.

The project in JavaScript

Transportation project Project Idea The transportation company wants to outsource its cars and planes to a number of drivers or pilots. Therefore, the idea of the application is to help them customize the car, whether it is a car or a plane for the driver or a qualified pilot, so that the plane is assigned to the pilot and the cars are allocated to a driver.

Steps

1-Create a Class for the vehicles and define their properties such as (Name, Manufacturer, ID).

2-Creating the Class for the car so that it inherits from the Class the vehicles, with defining the characteristics of the vehicle (gas or electric vehicle type).

3- Create a Class for the Airplane so that it inherits from the Class of Vehicles, with the aircraft characteristics defined (aircraft type ..).

4- Create a Class with the name employee to represent the employees and define its characteristics (name, ID, date of birth).

5- Create a Class Driver so that it inherits from Class employees with additional driver characteristics defined such as licenseID.

6- Create a Class Flyer so that it inherits from the Employee Class with additional pilot properties defined such as licenseID.

7- Create a Booking Class that contains the driver and his / her vehicle (reservationDate, employeeId, vehiclesId, reservationID).

8- After creating a mixed, blended, mixed, blended, extruded, scrambled, two planes.

9-Write a function that receives the employee’s id with the vehicle id and verify, in the event that the employee is a pilot and the vehicle is a car, a message of their incompatibility will be printed, and if the employee is a pilot and the vehicle is a plane, an object will be created and stored in the Class reserved.

10 -Save all reservations into an array.

11- Print the array’s content using the map function.