nsider the dataset {9n}m. Assume m is a multiple cAssignmentAssignnent 1Problem 2.1075 AssignntT1075CIT H075CCIT075 A22 CCTT4075 As2022. CCIT075 AssignCCIT 075 AssimentPebr(a) (1%) Suppose 9, =(b) (1%) Considercos(2rn/3). Estimate the pmf of the random variableOSm-1In == cos(2rn) + V, where V is an r.v. whose pmf reads:mentAsi(i) List all the possible element in G, i.e. the alphabet of G.2022 CCITIO75 AssignmenAssiDue Februapv (v) :(ii) Let a =amentoffa,1(1- a), v=-1/21/L where L is the last digit of your SID. Compute the corresponding pmf of G.6.entDue2022v = 1/2CIT1075 gunt 1 Due FefCCIT 4075 AssignmentDue FebruayD February 27Due FebruaryCCIT4075 AgnmentCCIT4075 AgnmentCIT4075 AssignmentT1075 AssignmentDne FelrD FeDue202Due FebDue Febru075 Assigument 1 Due FebrnryAssienmentment Du

Answered: Image /qna-images/answer/c5faaaf4-ddc0-4f4f-b409-c0639ab940cc.jpg

Problem 2. Consider the dataset {gn}". Assume m is a multiple of 6. (a) (1%) Suppose g, = cos(2rn/3). Estimate the pmf of the random variable G. (b) (1%) Consider gn = cos(2rn) + V, where V is an r.v. whose pmf reads: IT 4075 Asign CIT1075 1075 cnt ent m-1 (i) List all (ii) Let a = 1/L where L is the last digit of your SID. Compute the corresponding pmf of CCIT7 Due Februa pv (v) the possible element in G, i.e. the alphabet of G. Due Feb ment v = 1/2 (1- a), v=-1/2 Due De Febn D Februar Due Februar De Febri 1075 CCIT 1075 CIT4075 Asignt CIT1075 Assgment TH075 Assignment 075 Ass ment Due anent G. Due ment Assiennient

Problem 2. Consider the dataset {gn}". Assume m is a multiple of 6. (a) (1%) Suppose g, = cos(2rn/3). Estimate the pmf of the random variable G. (b) (1%) Consider gn = cos(2rn) + V, where V is an r.v. whose pmf reads: IT 4075 Asign CIT1075 1075 cnt ent m-1 (i) List all (ii) Let a = 1/L where L is the last digit of your SID. Compute the corresponding pmf of CCIT7 Due Februa pv (v) the possible element in G, i.e. the alphabet of G. Due Feb ment v = 1/2 (1- a), v=-1/2 Due De Febn D Februar Due Februar De Febri 1075 CCIT 1075 CIT4075 Asignt CIT1075 Assgment TH075 Assignment 075 Ass ment Due anent G. Due ment Assiennient

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Let X N(3,9) and consider Y = -5 X + 4. Which of the following describes the distribution of Y? Y-N(19, 225 ) OY-N(-11,45 ) OY-N(-11, 225 ) OY-N(-11,-41) Y N(-15, -45) Y-N(7,-45)
Consider another case in which a = 6,286, Xo = 4,985, C = 3,079 and M = 487.i). using the linear congruential method, determine the first 10 random numbers.ii). How robust is this algorithm?
Notice that Ro1(h) can be interpreted as the misclassification rate. That is, if R01(h) .7, then predicting h would result in the wrong answer for 70% of the data points. Given the data set {4, 2, 4, 1, 3, 4, 4, 3, 2, 5}, plot the empirical risk Ro1(h) for h = [0, 5]. Hint: the function should have point discontinuities. = c) Is gradient descent useful for minimizing the risk with zero-one loss? Why or why not? Make reference to your plot of the risk in your answer. Hint: the risk is indeed non-convex, but gradient descent can still be useful for minimizing non-convex functions. Is there some other reason?
4) In the working set model, for: ... 1345 6 03 23 204032921..., if DELTA = 6, then the working set at time t1 (... 04 03 ...) is А. (6, 0, 3, 2] В. (2, 3, 0, 4} С. (0, 4, 3, 2, 9) D. {4, 5, 6, 0, 3, 2}
Estimate the PDF of X = U₁U₂ where U₁ and U₂ are uniform random variables. (Assume U, and U₂ are uniform random variables on the interval [0,1].) Refer to Example 2.1 and the code in Figure 2.7. • Use rng (0) at the start of the script to ensure the same random numbers are generated each time. (Not rand('state',0) as described in the textbook, which is from a previous version of MATLAB.) ▪ Use 1000 realizations. • Use bins centered at 0, 0.1, 0.2, ..., 1. • Save the random variable X in the variable named x (lowercase). • Save the histogram in the variable named h. • Save the PDF in the variable named pxest. Recall that the PDF is the histogram values divided by the total number of realizations, then divided by the bin width. Script> C Reset E MATLAB Documentation
Estimate the PDF of X = U₁U₂ where U₁ and U₂ are uniform random variables. (Assume U, and U₂ are uniform random variables on the interval [0,1].) Refer to Example 2.1 and the code in Figure 2.7. • Use rng (0) at the start of the script to ensure the same random numbers are generated each time. (Not rand('state',0) as described in the textbook, which is from a previous version of MATLAB.) ▪ Use 1000 realizations. • Use bins centered at 0, 0.1, 0.2, ..., 1. • Save the random variable X in the variable named x (lowercase). • Save the histogram in the variable named h. • Save the PDF in the variable named pxest. Recall that the PDF is the histogram values divided by the total number of realizations, then divided by the bin width. Script> C Reset E MATLAB Documentation
4. Outliers can be detected using a statistical approach, one of popular approaches is the Inter Quartile Range (IQR) method. A sample is categorized as an outlier if its value is less than (Q1 - 1.5 * IQR) or greater than (Q3 + 1.5 * 1QR). Use these conditions to detect outliers in the following data: 130,132,138,136,131,153,131,133,129,133,110,132,129,134,135,132, 135,134,133,132,130,131,134,135,135,134,136,133,133,130 note: Q1 = quartile 1, Q3 = quartile 3 Draw the Boxplot to show the outliers %3D
Exercise 3 If we increase the number of replicates to 1000, then we will encounter a larger percentage of possible values for p at frequencies proportional to their probabilities of occurrence. By plotting these replicates in a histogram, we can start to visualize the probability distribution of p. The code below provides an example with sample size n = 10. Alter the plot so that the color is red, the x-axis reads "Proportion of Heads", y-axis reads "Frequency", and the title (main) says "Distribution of Sample Proportions, n=10": Code Start Over 1 values-c("H","T") 2 phats-replicate(1000, sum(sample(values, 10, replace-TRUE)--"H")/10) 3 hist(phots, xlab-"X label here",ylab-"y label here", main-"Put title here",col="orange") Exercise 4 Besides plotting the distribution, we can also use simulations to estimate the expected value/mean or standard deviation of random variables. The output below computes the mean. Change it so that it computes the standard deviation (command=sd): Code Run…
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Please help step to step with Program R (CS) with explanation and final code for understanding thank you.
3. Let h(n) be a heuristic function for a staten in the game where h(n) is the sum of all differences between k and the total of the entries in any row, column or diagonal: n n n h(n)= [(lk-[tijl) + Σ(k-tj) + (k-Σtal) + (k-Σtl) Σ Hi j-l Fl i-1 i-1 where p=n-i+1 For the state depicted by the grid in Table 2, (with k = 15 and n = 3) h(n) = (8 + 4 + 4) + (0+0+0)+4+4=24 Show the heuristic function values for each of the states in the drawing of your answer to question 2.
Consider the following code:> x <- matrix(rnorm(n = 500), ncol = 5)> varx <- var(x)Starting with varx, use two applications of the sweep() function, one dividing each row of the matrix andthe other dividing each column, of a covariance matrix to obtain R, the correlation matrix.