%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
Category: Statistics
Matlab Task on sphereVol
function A09Prob1_sphereVol_kang401(radius)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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
A=imread(‘one.png’)
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)
B=imread(‘two.png’)
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
library(readxl)
CSDATA <- read_excel(“CSDATA.xlsx”)
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$set(echo = TRUE)
library(cvTools)
#library(glmnet)
library(sandwich) Continue reading
R Programming Task on Naive Bayes Classifier
#data
Universal_bank = read.csv(“UniversalBank.csv”, header = T)
dim(Universal_bank)
head(Universal_bank) Continue reading
Python Task on Run-Length Encoding & Decoding
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <ctype.h>
/* Stores parameters that specify how to the program should behave. * Continue reading
Python Program on Guessing Game
import numpy as np
print(”)
print(“Enter two numbers, low then high.”)
l = int(input(“low = “))
h = int(input(“high = “))
R Programming Task on Matrix
summary_statistics_A <- function(matrix){
vec = sort(as.vector(matrix))
len = length(vec)
if(isSymmetric(matrix) && is.numeric(matrix)){
min = vec[1]
Continue reading
R Task on ANOVA Model
rm(list = ls())
options(warn = -1)
library(readxl)
## Reading the data from excel
Project_2_Data <- read_excel(“Stat 481 Project 2 Data.xls”)
str(Project_2_Data)
## Cleaning and attributing the dtaa
Project_2_Data$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”)
R Programming Task using the Data Analytics Approach
creditDF <- read.csv(“Downloads/Credit.csv”)
str(creditDF)
# Q1)
# Exploratory Data Analysis Continue reading
A Task on Statistical Discussion Topic
Post a total of 3 substantive responses over 2 separate days for full participation. This includes your
initial post and 2 replies to other students.
Respond to the following in a minimum of 175 words: 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
)
Supply And Logistics
Selected supply and Logistics company: Muscat International shipping and Logistics
Comprehensive History of systems
Muscat international shipping and Logistics has a great track record for the logistics services in the company. The company has over fifteen years of experience in logistics services. The system which is applied in Muscat international is related to the smooth and pre-post shipment of the freight (Ho, Zheng, Yildiz, & Talluri, 2015).The system which Muscat international shipping and logistics apply for controlling the mode of transportation uses the Cargo wise system for most of the entries. This systemis adopted conventionally from three to four years and it has benefit to provide a solution to the company. Continue reading
Statistics: How is it evolving with the introduction of new analytical and statistical tools?
Use of Statistics has been on the rise. Today, every company is using statistical and analytical tools to analyze data in a matter of few minutes. Earlier, people used to gather data through various sources and to analyze this data it used to take weeks and months together. Continue reading