# Matlab Task on Tortion Analysis

%Fit using polyfit in matlab for best fit checking plot

table = xlsread(‘Project04′,’Stepped-Shaft Torsion Data, A-E’)
[p S] = polyfit(table(:,1),table(:,2),5) Continue reading

# Matlab Task on sphereVol

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ENGR 132
% Program Description
% The code computes volume of a sphere as a function of height of fluid
% The input to function call is radius
% The function does not return any variable Continue reading

# Matlab Task on Computer Programming & Numerical Methods

signImage=rgb2gray(A);
figure(1)
imshow(signImage)
%Detect features of first image that is read above%
signPoints=detectSURFFeatures(signImage)
figure(3)
imshow(signImage)
title(‘100 strongest features from sign image’)
hold on
%Top 100 strongest features
plot(selectStrongest(signPoints,100))

[signFeatures2, signPoints2] = extractFeatures(signImage, signPoints)

signImage2=rgb2gray(B);
figure(4)
imshow(signImage2)
%Detect features of first image that is read above%
signPoints2=detectSURFFeatures(signImage2)
figure(5)
imshow(signImage2)
title(‘100 strongest features from sign image’)
hold on
%Top 100 strongest features
plot(selectStrongest(signPoints2,300))
[signFeatures2, signPoints2] = extractFeatures(signImage2, signPoints2)

%Punitive point matches in both the images%

picPairs=matchFeatures(signFeatures, signFeatures2);
matchedSignPoints = signPoints(picPairs(:,1),:);
matchedFindPoints = signPoints2(picPairs(:,2),:);
figure(6)
showMatchedFeatures(signImage,signImage2,matchedSignPoints, matchedFindPoints, ‘montage’)
title(‘Matched points both images’)

%Locate objects using Punitive matches%
%[tform, inlierBoxPoints, inlierScenePoints] =estimateGeometricTransform(matchedSignPoints, matchedFindPoints);
%figure;
%showMatchedFeatures(signImage, signImage2, inlierBoxPoints,inlierScenePoints, ‘montage’);
%title(‘Matched Points (Inliers Only)’);

# R Studio Task on MBA Case Study

dim(CSDATA)
#Fitting the linear regression on all the independent variables Continue reading

# R Programming Task on Model Comparison and Lasso

``````{r setup, include = FALSE}
knitr::opts_chunk\$set(echo = TRUE)

library(cvTools)
#library(glmnet)
library(sandwich) Continue reading →```

# R Programming Task on Naive Bayes Classifier

#data

dim(Universal_bank)

# Python Task on Run-Length Encoding & Decoding

#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <ctype.h>

/* Stores parameters that specify how to the program should behave. * Continue reading

# Python Program on Guessing Game

```import numpy as np
print('')
print("Enter two numbers, low then high.")
l = int(input("low = "))
h = int(input("high = "))

# 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]

# 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”)

# R Programming Task using the Data Analytics Approach

str(creditDF)

# Q1)
# Exploratory Data Analysis Continue reading

# Statistics – Supply and Demand Task

```Solution for Statistics - Supply and Demand Task

(a) β̂1 = −0.75317
Confidence interval is: ( −0.8050502, −0.7012837 )
(b) For a variable to be valid instrument for log_p , it should be correlated with log_p but
uncorrelated with error term (UI
)