How many people do I need to survey? How many is too many. What are the costs v the benefits. Determining sample size --- the correct sample--- is the foundation for great surveys and part of your overall market research strategy.
How many people do I need to survey? How many is too many. What are the costs v the benefits. Determining sample size --- the correct sample--- is the foundation for great surveys and part of your overall market research strategy.
Practical Methods To Overcome Sample Size ChallengesnQuery
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
This presentation will address the issue of sample size determination for social sciences. A simple example is provided for every to understand and explain the sample size determination.
Lecture to Master of Business Management Students (MBM) at the Moshi Cooperative University, Moshi Tanzania. The Objective was that at the end of the lecture students should be able to determine sample size scientifically.
A non technical overview of sample size calculation and why it is necessary with some brief examples of how to approach the problem and why it is useful to actually think of these calculations.
Practical Methods To Overcome Sample Size ChallengesnQuery
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
This presentation will address the issue of sample size determination for social sciences. A simple example is provided for every to understand and explain the sample size determination.
Lecture to Master of Business Management Students (MBM) at the Moshi Cooperative University, Moshi Tanzania. The Objective was that at the end of the lecture students should be able to determine sample size scientifically.
A non technical overview of sample size calculation and why it is necessary with some brief examples of how to approach the problem and why it is useful to actually think of these calculations.
Running head Statistics 2Statistics Statistics Na.docxagnesdcarey33086
Running head: Statistics
2
Statistics
Statistics
Name:
Course:
Instructor:
Institution:
Date of Submission:
Assignment #4: Model Diagnostics
A fundamental requirement in the classical linear regression is that the regression error term must be normally distributed with zero mean and constant variance (Greene, 2008). The normality tests results are presented below.
All the plots have values greater than the threshold probability value of 0.05 thus the null hypothesis of normality of the regression residuals could not be rejected at 5 per cent significance level. Conclusion is thus made that the regression residuals from the estimated equations followed a normal distribution. Since any linear function of normally distributed variables is considered to be normally distributed, normal distribution of the residuals had the implication that the coefficients of the estimates were also themselves normally distributed (Gujarati, 2008).
The residual plot is shown below:
From the residual plot it can be seen that all the residuals fall within the standard error bands thus confirming that the model is stable and can thus be used for forecasting.
References
Greene, W. (2008). Econometric analysis, 6th ed. . New Jersey: Pearson-Prentice Hall.
Gujarati, D. (2004). Basic econometrics 4th ed. . New York: McGraw Hill Companies.
Normal Probability Plot
2.6315789473684208 7.8947368421052602 13.15789473684211 18.421052631578942 23.684210526315791 28.947368421052641 34.21052631578948 39.473684210526301 44.73684210526315 50 55.26315789 4736857 60.526315789473699 65.789473684210563 71.052631578947384 76.315789473684163 81.578947368420984 86.842105263157904 92.105263157894726 97.368421052631547 10.7 11.3 11.8 11.9 12 12 12 12.4 12.5 12.6 13.1 13.2 13.4 13.5 13.5 14.2 14.5 14.5 14.6
Sample Percentile
MedianSchoolYears
Age Residual Plot
60 30 62 44 0 30 62 68 46 56 36 28 0 0 34 26 52 50 44 0.50878516451792 1.7144464705013149 -0.42159945482941003 0.54117037792769895 0.71299080887547295 1.269413932725179 0.26951686627728799 0.22131431339594501 -0.13472012994437299 0.22075061567252199 -1.3199768562363781 -0.18681496091028299 0.380020030213299 -1.451131014273024 -0.56052688701790399 -0.116260966970037 -0.67291294283960901 -0.49015761805784802 -0.48430774902780499
Age
Residuals
RUNNING HEADER: WEEK 3 ASSIGNMENT 4 1
WEEK 3 ASSIGNMENT 4 13
Week 3 Assignment 4
Introduction
In this project I selected six variables from the ' SampleDataSet.xlsx'. Among these six variables three of them were continuous and the reaming three were discrete variables. The continuous variables selected for this study are Age, WealthScore and MedianSchoolYears. The discrete variables selected for this study are NumberOfChildren, MailResponder and NumberOfCars.
Analysis
Age
The age is a continuous variable which takes only positive values even though we usually consider the integer part of it. The descriptive statistics summary of the age variable .
I am Samson H. I am a Multiple Linear Regression Homework Expert at statisticshomeworkhelper.com. I hold a Master's in Statistics, from Michigan, USA. I have been helping students with their homework for the past 12 years. I solved homework related to Multiple Linear Regression.
Visit statisticshomeworkhelper.com or email info@statisticshomeworkhelper.com.You can also call on +1 678 648 4277 for any assistance with Multiple Linear Regression Homework Help.
Statistical Analysis is complex part but reporting of data in proper manner with proper selective graphs & interpretations is also necessary part of data analysis !!!
Character Recognition using Data Mining Technique (Artificial Neural Network)Sudipto Krishna Dutta
This Presentation is on Character Recognition using Artificial Neural networks,
Presented to
Farhana Afrin Duty
Assistant Professor
Department of Statistics
Jahangirnagar University
Savar, Dhaka-1342, Bangladesh
We have done this slide for Research Methodology course. Recent E-commerce conditions of Bangladesh.We have collected data from recent survey and try to show the present condition.
We discussed about drawbacks and prospectus about E-commerce in Bangladesh.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Determination and Analysis of Sample size
1. This Presentation is on
Determination and Analysis of Sample size
by observing and using sample with appropriate sampling scheme
Submitted By
Sudipto Krishna Dutta
ID: 20204021
Section: A, Batch: 04
Course Title: Sampling Methodology (PM-ASDS02)
2. Sampling Frame, Population Data and Survey Questions
Total Population = 50 Students
Sampling Units = Students
Given Survey Questions
Demographic
Information
Gender
Age
Marital Status
Weight
Residence
Division of
Present
Residence
Location of
Present
Residence
Socio Economic
Information
Current Occupation
Amount of money
spent yesterday
Behavioral Information
Do you smoke?
Do you use Facebook?
Do you watch
Bangladeshi TV
channels?
Do you read
newspaper?
Do you read story
books?
Do you like cricket?
Do you like football?
3. Sampling Methodology and Sample Size Determination
• Stratifies Sampling : Stratified Sampling method is selected for greater precision and to
ensure representation of small sub groups. Also proportional allocation technique is used to
allocate sample size to different starta.
• Stratum : (1) Male (2) Female
Allocating sample size to strata
• Population Size : N = 50
• Desired Sample Size : n = 44
Here, I used simple random sampling technique with considering 5% level of
significance and the desired precision is 0.05 and “P” & “Q” are unknown
Sl. # (i) Stratum Ni Wi = Ni/N ni
1 Male 33 0.66 29
2 Female 17 0.34 15
Total 50 1.00 44
4. Tabular Representation of Sampling Data
Table 1 : Frequency distribution on Residence of 29 Male stratum.
Table 2 : Frequency distribution on Residence of 15 Female stratum.
5. Graphical Representation of Sampling Data
Figure 1 :Bar Graph representing the
residence of male stratum.
Figure 2 :Bar Graph representing the
residence of female stratum.
6. Graphical Representation of Sampling Data
Figure 3 : Pie Chart representing
the residence of male stratum
Figure 4 :Bar Graph representing
the residence of female stratum.
7. Descriptive Summery Statistics on Demographic
Information
Male Female
Mean 27.6 26.5
Standard
Error
0.94 0.88
Median 25 25
Mode 25 25
Standard
Deviation
5.07 3.40
Sample
Variance
25.68 11.55
Kurtosis 1.17 0.68
Skewness 1.52 1.55
Range 18 9
Minimum 23 24
Maximum 41 26.5
Table 3 : Summary Statistics on Age of 29
male and 15 female stratum.
31%
7%
62%
Currently
Married
Others
Never
Married
Figure 5 : Pie Chart representing the marital
status of male stratum.
8. Descriptive Summery Statistics on Demographic
Information
Male Female
Mean 75.2 54.9
Standard
Error
2.51 2.42
Median 73 50
Mode 65 45
Standard
Deviation
13.50 9.36
Sample
Variance
182.24 87.55
Kurtosis -1.39 -1.71
Skewness 0.18 0.26
Range 40 23
Minimum 56 45
Maximum 96 68
Table 4 : Summary Statistics on weight of 29
male and 15 female stratum.
40%
20%
40%
Currently
Married
Others
Never
Married
Figure 6: Pie Chart representing the maritial status of
female stratum .
9. Descriptive Summery Statistics on Socio Economic
Male Female
Mean 758.1 1253.3
Standard Error 159.6 295.7
Median 300 500
Mode 200 2800
Standard Deviation 859.4 1145.1
Sample Variance 738595.2 1311238
Kurtosis 2.0 -1.7
Skewness 1.6 0.39
Range 3350 2800
Minimum 50 0
Maximum 3400 2800
Table 5 : Summary Statistics on amount of money yesterday of male and female stratum.
10. Descriptive Summery Statistics on Socio Economic
0
5
10
15
Business Service Student Others
Male
Male
0
5
10
Service Students
Female
Female
Figure 7 : Bar Graph representing the occupation of male stratum.
Figure 8: Bar Graph representing the occupation of male stratum.
Explanation:
• In the table 5, male and female
spent the mean amount of money
yesterday are 758 and 1253
respectively, which indicates
female used to spend more money
than male, through the variability
among the data are very high and
distribution are not normal.
•In the figure 7 and 8, male and
female are mostly engaged in
“Service” and “Student”. Though,
female presence in business and
others occupation is zero.
12. Behavioral Information Analysis
17%
83%
Male
Yes
No
13%
87%
Female
Yes
No
Figure 10 : Pie Chart representing the Facebook usage ratio among male and female stratum.
Explanation:
• In the figure 10 the ratio using Facebook among the male and female stratum are in 83%
and 87% respectively.
Hence, we can say that, the more number female use Facebook, in compared to the male.
13. Behavioral Information Analysis
0
10
20
30
40
Yes
No
Figure 11 : Bar Graph representing the Bangladeshi TV channel watching frequency of male stratum, female stratum and combined.
Watching Bangladeshi TV Channels
0
10
20
30
Yes
No
Reading Newspaper
Figure 12 : Bar Graph representing the newspaper reading frequency of male stratum, female stratum and combined.
0
10
20
30
Yes
No
Figure 13 : Bar Graph representing the story books reading frequency of male stratum, female stratum and combined.
Reading Story Books
Figure 11 : Only 12 respondents among 44 watch BD TV
channel. The ratio is almost similar for male and female.
So, watching BD TV channels are quite low.
Figure 12 : 29 respondents among 44 read newspaper.
Though the frequency of reading newspaper among
female is much higher than the male.
Figure 13 : 23 respondents among 44 read story books,
though the frequency of reading newspaper among female
is much higher than the male and the female read story
books more than the male.
14. Behavioral Information Analysis
0
10
20
30
Male Female Combined
Yes
No
Football Lovers
Figure 14: Bar Graph representation the football lovers among male stratum, female stratum and combined respondents.
Explanation: In this figure 14 20 respondents like football among44, though in male stratum 16 respondents like
football among 29, where only 4 female respondents like football among 15. Here we can diffrentiate that, male
like football more than female.
70%
30%
Male
Yes
No
73%
27%
Female
Yes
No 75
%
25
%
Combined
Yes
No
Figure 15: Pie Chart representation the fan of cricket among male stratum, female stratum and combined.
Explanation: In this figure 15% males and 75.6% female like cricket which are quite pretty to combined ratio of
70%. Here, we can differentiate that,70 % respondents like cricket and slightly more than male among the sample
respondents.
Cricket Lovers
