summary_statistics_A <- function(matrix){

vec = sort(as.vector(matrix))

len = length(vec)

if(isSymmetric(matrix) && is.numeric(matrix)){

min = vec[1]

Continue reading

summary_statistics_A <- function(matrix){

vec = sort(as.vector(matrix))

len = length(vec)

if(isSymmetric(matrix) && is.numeric(matrix)){

min = vec[1]

Continue reading

from turtle import *

import random

if __name__ == ‘__main__’:

number_coins = int(input(‘Enter a number of coins: ‘)) Continue reading

**Q 1 – ANSWER :**

int first, last;

first=1;

last=10;

for (i=first; i<last; i++)

{

} Continue reading

rm(list = ls())

options(warn = -1)

library(readxl)

## Reading the data from excel

Project_2_Data <- read_excel(“Stat 481 Project 2 Data.xls”)

str(Project_2_Data)

## Cleaning and attributing the dtaa

Project_2_Data$courses = as.factor(Project_2_Data$courses)

Project_2_Data$gender = as.factor(Project_2_Data$gender)

levels(Project_2_Data$gender) <- c(“Female”, “Male”)

levels(Project_2_Data$courses) <- c(“Algebra”, “Algebra&Geometry”, “Calculus”)

attach(Project_2_Data)

## Descriptives

library(ggplot2)

library(hrbrthemes)

library(dplyr)

library(tidyr)

library(viridis)

temp = aggregate(score~courses+gender, Project_2_Data, FUN = mean)

qqnorm(score)

ggplot(Project_2_Data, aes(x = score)) + geom_histogram()

summary(Project_2_Data)

p1 <- ggplot(data=Project_2_Data, aes(x=score, fill=courses)) + geom_density(adjust=1.5, alpha=.4) + theme_ipsum()

p2 <- ggplot(data=Project_2_Data, aes(x=score, fill=gender)) + geom_density(adjust=1.5, alpha=.4) + theme_ipsum()

## Model

## Test of normality and other assumptions

ks.test(score, pnorm, mean = mean(score), sd= sd(score))

bartlett.test(score~courses, data = Project_2_Data)

bartlett.test(score~gender, data = Project_2_Data)

## Linear model

model1 = anova(score ~ courses + gender, data = Project_2_Data)

model1

summary(model1)

## Post Hoc

library(DescTools)

PostHocTest(model1, method = “bonferroni”)

PostHocTest(model1, method = “hsd”)

#include <iostream>

#include <string>

using namespace std;

int main()

{

int numItem; Continue reading

creditDF <- read.csv(“Downloads/Credit.csv”)

str(creditDF)

# Q1)

# Exploratory Data Analysis Continue reading

— Query 1 —

SELECT

SUM(number) AS record_count

FROM

`bigquery-public-data.usa_names.usa_1910_2013` Continue reading

—————CREATE CUSTOMER

CREATE TABLE CUSTOMER(CUSTOMERID INT NOT NULL PRIMARY KEY,

CUSTOMERFIRST VARCHAR(50) NOT NULL,

CUSTOMERLAST VARCHAR(50) NOT NULL ,

CUSTOMERSTREET VARCHAR(50) NOT NULL , Continue reading

#lang racket

#| Problem 1 |#

; Define the Fibonacci function fib as usual

(define (fib n)

(if (<= n 2) 1

(+ (fib (- n 1)) (fib (- n 2))))) Continue reading

#include <iostream>

#include <iomanip>

#include “matrix.h”

using namespace std;

Matrix :: Matrix() {

cnt++;

row_size = maxRowSize;

col_size = maxColSize;

for (int r = 0; r < getRowSize(); r++) {

for (int c = 0; c < getColumnSize(); c++) {

matrix[r][c] = rand() % 10 + 1; Continue reading

{

“cells”: [

{

“cell_type”: “code”,

“execution_count”: 1,

“metadata”: {},

“outputs”: [],

“source”: [

“# Loading the required packages\n”,

Continue reading

**Solution for Machine Learning using Python for Gradescope Task**

#!/usr/bin/env python

# coding: utf-8

# Essential Problem 1:

# a). Here at least one point is required in each grid, thus the least number of data points are 100

# b). Here the dimension has changed to 3. Thus the least number of data points are 10^3 = 1000.

# c). Here the dimension has changed to 3. Thus the least number of data points are 10^(10)

— phpMyAdmin SQL Dump

— version 4.9.2

— https://www.phpmyadmin.net/

— Continue reading

Digital Security is a standout amongst the most critical worries of individuals as the whole working framework has developed to include in a virtual working stage. The issue has turned out to be one noteworthy risk to security parts of individuals, all things considered, way. Continue reading

Artificial intelligence is taking the world by storm. This has made has a strong mark in the technology and manufacturing industries. This is also helping organizations to analyze the data. Today, technology has gone to the extent where a computer is able to talk to humans, recognize their faces and even is turning off the lights and refrigerators in our homes. Continue reading