%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$set(echo = TRUE)
library(cvTools)
#library(glmnet)
library(sandwich) Continue reading
R Programming Task on Naive Bayes Classifier
#data
Universal_bank = read.csv(“UniversalBank.csv”, header = T)
dim(Universal_bank)
head(Universal_bank) Continue reading
Python Task on Run-Length Encoding & Decoding
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <ctype.h>
/* Stores parameters that specify how to the program should behave. * Continue reading
Python Program on Guessing Game
import numpy as np
print('')
print("Enter two numbers, low then high.")
l = int(input("low = "))
h = int(input("high = "))
Continue reading
R Programming Task on Matrix
summary_statistics_A <- function(matrix){
vec = sort(as.vector(matrix))
len = length(vec)
if(isSymmetric(matrix) && is.numeric(matrix)){
min = vec[1]
Continue reading
R Task on ANOVA Model
rm(list = ls())
options(warn = -1)
library(readxl)
## Reading the data from excel
Project_2_Data <- read_excel(“Stat 481 Project 2 Data.xls”)
str(Project_2_Data)
## Cleaning and attributing the dtaa
Project_2_Data$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
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 ) Continue reading