summary_statistics_A <- function(matrix){
vec = sort(as.vector(matrix))
len = length(vec)
if(isSymmetric(matrix) && is.numeric(matrix)){
min = vec[1]
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Category: Programming
Python Task on Pence Piece Shapes using Tegan the Turtle
from turtle import *
import random
if __name__ == ‘__main__’:
number_coins = int(input(‘Enter a number of coins: ‘)) Continue reading
Engineering Programming Task on EIT moodle
Q 1 – ANSWER :
int first, last;
first=1;
last=10;
for (i=first; i<last; i++)
{
} Continue reading
R Task on ANOVA Model
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”)
C++ Programming Task – Pointers/Arrays
#include <iostream>
#include <string>
using namespace std;
int main()
{
int numItem; Continue reading
R Programming Task using the Data Analytics Approach
creditDF <- read.csv(“Downloads/Credit.csv”)
str(creditDF)
# Q1)
# Exploratory Data Analysis Continue reading
Database Task on Baby Names
— Query 1 —
SELECT
SUM(number) AS record_count
FROM
`bigquery-public-data.usa_names.usa_1910_2013` Continue reading
A SQL Task
—————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
Programming Task on Memoization in Scheme
#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
C++ Task – Classes and Data Abstraction
#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
Programming – Jupyter Notebook Task
{
“cells”: [
{
“cell_type”: “code”,
“execution_count”: 1,
“metadata”: {},
“outputs”: [],
“source”: [
“# Loading the required packages\n”,
Continue reading
Machine Learning using Python for Gradescope Task
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)
SQL – Design and Analysis of Data Systems
SQL – Design and Analysis of Data Systems
— phpMyAdmin SQL Dump
— version 4.9.2
— https://www.phpmyadmin.net/
— Continue reading
Cyber Attacks And Security
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
How artificial intelligence is changing the way we analyze data!
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