# R Programming Task on Matrix

summary_statistics_A <- function(matrix){
vec = sort(as.vector(matrix))
len = length(vec)

if(isSymmetric(matrix) && is.numeric(matrix)){
min = vec[1]

# 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

int first, last;
first=1;
last=10;
for (i=first; i&lt;last; i++)
{

# R Task on ANOVA Model

rm(list = ls())
options(warn = -1)

## 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()
{

# R Programming Task using the Data Analytics Approach

str(creditDF)

# Q1)
# Exploratory Data Analysis Continue reading

# Database Task on Baby Names

— Query 1 —

SELECT
SUM(number) AS record_count
FROM

—————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,
“outputs”: [],
“source”: [

#!/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)