Assessment 3 ContextYou will review the theory, logic, and a.docxgalerussel59292
Assessment 3 Context
You will review the theory, logic, and application of t-tests. The t-test is a basic inferential statistic often reported in psychological research. You will discover that t-tests, as well as analysis of variance (ANOVA), compare group means on some quantitative outcome variable.
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test. This is the test of difference between group means. In variations on this model, the two groups can actually be the same people under different conditions, or one of the groups may be assigned a fixed theoretical value. The main idea is that two mean values are being compared. The two groups each have an average score or mean on some variable. The null hypothesis is that the difference between the means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups. Means, and difference between them.
Null Hypothesis Significance Test
The most common forms of the Null Hypothesis Significance Test (NHST) are three types of t tests, and the test of significance of a correlation. The NHST also extends to more complex tests, such as ANOVA, which will be discussed separately. Below, the null hypothesis and the alternative hypothesis are given for each of the following tests. It would be a valuable use of your time to commit the information below to memory. Once this is done, then when we refer to the tests later, you will have some structure to make sense of the more detailed explanations.
1. One-sample t test: The question in this test is whether a single sample group mean is significantly different from some stated or fixed theoretical value - the fixed value is called a parameter.
· Null Hypothesis: The difference between the sample group mean and the fixed value is zero in the population.
· Alternative hypothesis: T.
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docxcurwenmichaela
BUS 308 Week 3 Lecture 1
Examining Differences - Continued
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around multiple testing
2. The basics of the Analysis of Variance test
3. Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
Last week, we found out ways to examine differences between a measure taken on two
groups (two-sample test situation) as well as comparing that measure to a standard (a one-sample
test situation). We looked at the F test which let us test for variance equality. We also looked at
the t-test which focused on testing for mean equality. We noted that the t-test had three distinct
versions, one for groups that had equal variances, one for groups that had unequal variances, and
one for data that was paired (two measures on the same subject, such as salary and midpoint for
each employee). We also looked at how the 2-sample unequal t-test could be used to use Excel
to perform a one-sample mean test against a standard or constant value. This week we expand
our tool kit to let us compare multiple groups for similar mean values.
A second tool will let us look at how data values are distributed – if graphed, would they
look the same? Different shapes or patterns often means the data sets differ in significant ways
that can help explain results.
Multiple Groups
As interesting as comparing two groups is, often it is a bit limiting as to what it tells us.
One obvious issue that we are missing in the comparisons made last week was equal work. This
idea is still somewhat hard to get a clear handle on. Typically, as we look at this issue, questions
arise about things such as performance appraisal ratings, education distribution, seniority impact,
etc.
Some of these can be tested with the tools introduced last week. We can see, for
example, if the performance rating average is the same for each gender. What we couldn’t do, at
this point however, is see if performance ratings differ by grade, do the more senior workers
perform relatively better? Is there a difference between ratings for each gender by grade level?
The same questions can be asked about seniority impact. This week will give us tools to expand
how we look at the clues hidden within the data set about equal pay for equal work.
ANOVA
So, let’s start taking a look at these questions. The first tool for this week is the Analysis
of Variance – ANOVA for short. ANOVA is often confusing for students; it says it analyzes
variance (which it does) but the purpose of an ANOVA test is to determine if the means of
different groups are the same! Now, so far, we have considered means and variance to be two
distinct characteristics of data sets; characteristics that are not related, yet here we are saying that
looking at one will give us insight into the other.
The reason is due to the way the variance is an.
6
ONE-WAY BETWEEN-
SUBJECTS ANALYSIS OF
VARIANCE
6.1 Research Situations Where One-Way Between-Subjects
Analysis of Variance (ANOVA) Is Used
A one-way between-subjects (between-S) analysis of variance (ANOVA) is
used in research situations where the researcher wants to compare means on a
quantitative Y outcome variable across two or more groups. Group
membership is identified by each participant’s score on a categorical X
predictor variable. ANOVA is a generalization of the t test; a t test provides
information about the distance between the means on a quantitative outcome
variable for just two groups, whereas a one-way ANOVA compares means
on a quantitative variable across any number of groups. The categorical
predictor variable in an ANOVA may represent either naturally occurring
groups or groups formed by a researcher and then exposed to different
interventions. When the means of naturally occurring groups are compared
(e.g., a one-way ANOVA to compare mean scores on a self-report measure of
political conservatism across groups based on religious affiliation), the design
is nonexperimental. When the groups are formed by the researcher and the
researcher administers a different type or amount of treatment to each group
while controlling extraneous variables, the design is experimental.
The term between-S (like the term independent samples) tells us that each
participant is a member of one and only one group and that the members of
samples are not matched or paired. When the data for a study consist of
repeated measures or paired or matched samples, a repeated measures
ANOVA is required (see Chapter 22 for an introduction to the analysis of
repeated measures). If there is more than one categorical variable or factor
included in the study, factorial ANOVA is used (see Chapter 13). When there
is just a single factor, textbooks often name this single factor A, and if there
are additional factors, these are usually designated factors B, C, D, and so
forth. If scores on the dependent Y variable are in the form of rank or ordinal
data, or if the data seriously violate assumptions required for ANOVA, a
nonparametric alternative to ANOVA may be preferred.
In ANOVA, the categorical predictor variable is called a factor; the
groups are called the levels of this factor. In the hypothetical research
example introduced in Section 6.2, the factor is called “Types of Stress,” and
the levels of this factor are as follows: 1, no stress; 2, cognitive stress from a
mental arithmetic task; 3, stressful social role play; and 4, mock job
interview.
Comparisons among several group means could be made by calculating t
tests for each pairwise comparison among the means of these four treatment
groups. However, as described in Chapter 3, doing a large number of
significance tests leads to an inflated risk for Type I error. If a study includes
k groups, there are k(k – 1)/2 pairs of means; thus, for a set of four groups, the .
Describes the design, assumptions, and interpretations for one-way ANOVA, one-way repeated measures ANOVA, factorial ANOVA, SPANOVA, ANCOVA, and MANOVA. More info: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/ANOVA_II
Assessment 3 ContextYou will review the theory, logic, and a.docxgalerussel59292
Assessment 3 Context
You will review the theory, logic, and application of t-tests. The t-test is a basic inferential statistic often reported in psychological research. You will discover that t-tests, as well as analysis of variance (ANOVA), compare group means on some quantitative outcome variable.
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test. This is the test of difference between group means. In variations on this model, the two groups can actually be the same people under different conditions, or one of the groups may be assigned a fixed theoretical value. The main idea is that two mean values are being compared. The two groups each have an average score or mean on some variable. The null hypothesis is that the difference between the means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups. Means, and difference between them.
Null Hypothesis Significance Test
The most common forms of the Null Hypothesis Significance Test (NHST) are three types of t tests, and the test of significance of a correlation. The NHST also extends to more complex tests, such as ANOVA, which will be discussed separately. Below, the null hypothesis and the alternative hypothesis are given for each of the following tests. It would be a valuable use of your time to commit the information below to memory. Once this is done, then when we refer to the tests later, you will have some structure to make sense of the more detailed explanations.
1. One-sample t test: The question in this test is whether a single sample group mean is significantly different from some stated or fixed theoretical value - the fixed value is called a parameter.
· Null Hypothesis: The difference between the sample group mean and the fixed value is zero in the population.
· Alternative hypothesis: T.
BUS 308 Week 3 Lecture 1 Examining Differences - Continued.docxcurwenmichaela
BUS 308 Week 3 Lecture 1
Examining Differences - Continued
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around multiple testing
2. The basics of the Analysis of Variance test
3. Determining significant differences between group means
4. The basics of the Chi Square Distribution.
Overview
Last week, we found out ways to examine differences between a measure taken on two
groups (two-sample test situation) as well as comparing that measure to a standard (a one-sample
test situation). We looked at the F test which let us test for variance equality. We also looked at
the t-test which focused on testing for mean equality. We noted that the t-test had three distinct
versions, one for groups that had equal variances, one for groups that had unequal variances, and
one for data that was paired (two measures on the same subject, such as salary and midpoint for
each employee). We also looked at how the 2-sample unequal t-test could be used to use Excel
to perform a one-sample mean test against a standard or constant value. This week we expand
our tool kit to let us compare multiple groups for similar mean values.
A second tool will let us look at how data values are distributed – if graphed, would they
look the same? Different shapes or patterns often means the data sets differ in significant ways
that can help explain results.
Multiple Groups
As interesting as comparing two groups is, often it is a bit limiting as to what it tells us.
One obvious issue that we are missing in the comparisons made last week was equal work. This
idea is still somewhat hard to get a clear handle on. Typically, as we look at this issue, questions
arise about things such as performance appraisal ratings, education distribution, seniority impact,
etc.
Some of these can be tested with the tools introduced last week. We can see, for
example, if the performance rating average is the same for each gender. What we couldn’t do, at
this point however, is see if performance ratings differ by grade, do the more senior workers
perform relatively better? Is there a difference between ratings for each gender by grade level?
The same questions can be asked about seniority impact. This week will give us tools to expand
how we look at the clues hidden within the data set about equal pay for equal work.
ANOVA
So, let’s start taking a look at these questions. The first tool for this week is the Analysis
of Variance – ANOVA for short. ANOVA is often confusing for students; it says it analyzes
variance (which it does) but the purpose of an ANOVA test is to determine if the means of
different groups are the same! Now, so far, we have considered means and variance to be two
distinct characteristics of data sets; characteristics that are not related, yet here we are saying that
looking at one will give us insight into the other.
The reason is due to the way the variance is an.
6
ONE-WAY BETWEEN-
SUBJECTS ANALYSIS OF
VARIANCE
6.1 Research Situations Where One-Way Between-Subjects
Analysis of Variance (ANOVA) Is Used
A one-way between-subjects (between-S) analysis of variance (ANOVA) is
used in research situations where the researcher wants to compare means on a
quantitative Y outcome variable across two or more groups. Group
membership is identified by each participant’s score on a categorical X
predictor variable. ANOVA is a generalization of the t test; a t test provides
information about the distance between the means on a quantitative outcome
variable for just two groups, whereas a one-way ANOVA compares means
on a quantitative variable across any number of groups. The categorical
predictor variable in an ANOVA may represent either naturally occurring
groups or groups formed by a researcher and then exposed to different
interventions. When the means of naturally occurring groups are compared
(e.g., a one-way ANOVA to compare mean scores on a self-report measure of
political conservatism across groups based on religious affiliation), the design
is nonexperimental. When the groups are formed by the researcher and the
researcher administers a different type or amount of treatment to each group
while controlling extraneous variables, the design is experimental.
The term between-S (like the term independent samples) tells us that each
participant is a member of one and only one group and that the members of
samples are not matched or paired. When the data for a study consist of
repeated measures or paired or matched samples, a repeated measures
ANOVA is required (see Chapter 22 for an introduction to the analysis of
repeated measures). If there is more than one categorical variable or factor
included in the study, factorial ANOVA is used (see Chapter 13). When there
is just a single factor, textbooks often name this single factor A, and if there
are additional factors, these are usually designated factors B, C, D, and so
forth. If scores on the dependent Y variable are in the form of rank or ordinal
data, or if the data seriously violate assumptions required for ANOVA, a
nonparametric alternative to ANOVA may be preferred.
In ANOVA, the categorical predictor variable is called a factor; the
groups are called the levels of this factor. In the hypothetical research
example introduced in Section 6.2, the factor is called “Types of Stress,” and
the levels of this factor are as follows: 1, no stress; 2, cognitive stress from a
mental arithmetic task; 3, stressful social role play; and 4, mock job
interview.
Comparisons among several group means could be made by calculating t
tests for each pairwise comparison among the means of these four treatment
groups. However, as described in Chapter 3, doing a large number of
significance tests leads to an inflated risk for Type I error. If a study includes
k groups, there are k(k – 1)/2 pairs of means; thus, for a set of four groups, the .
Describes the design, assumptions, and interpretations for one-way ANOVA, one-way repeated measures ANOVA, factorial ANOVA, SPANOVA, ANCOVA, and MANOVA. More info: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/ANOVA_II
This Slides presents different types of Parametric Test- like
T-test,
Parametric Test,
Assumption of Parametric Test,
Paired T Test,
One Sample T Test,
ANOVA,
ANCOVA,
Regression,
Two Way ANOVA,
Repeated Measure ANOVA,
Multiple Regression
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, ...Musfera Nara Vadia
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, confidence interval, two-tailed and one tailed test, and other misunderstood issues.
Statistical inference: Statistical Power, ANOVA, and Post Hoc testsEugene Yan Ziyou
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 4 (statistical power, ANOVA, and post hoc tests).
The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
Assessment 4 ContextRecall that null hypothesis tests are of.docxfestockton
Assessment 4 Context
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test again, with the added capability of comparing the means among more than two group at a time. This is the same type of test of difference between group means. In variations on this model, the groups can actually be the same people under different conditions. The main idea is that several group mean values are being compared. The groups each have an average score or mean on some variable. The null hypothesis is that the difference between all the group means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups.
One might ask why we would not use multiple t tests in this situation. For instance, with three groups, why would I not compare groups one and two with a t test, then compare groups one and three, and then compare groups two and three?
The answer can be found in our basic probability review. We are concerned with the probability of a TYPE I error (rejecting a true null hypothesis). We generally set an alpha level of .05, which is the probability of making a TYPE I error. Now consider what happens when we do three t tests. There is .05 probability of making a TYPE I error on the first test, .05 probability of the same error on the second test, and .05 probability on the third test. What happens is that these errors are essentially additive, in that the chances of at least one TYPE I error among the three tests much greater than .05. It is like the increased probability of drawing an ace from a deck of cards when we can make multiple draws.
ANOVA allows us do an "overall" test of multiple groups to determine if there are any differences among groups within the set. Notice that ANOVA does not tell us which groups among the three groups are different from each other. The primary test ...
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.”
This Slides presents different types of Parametric Test- like
T-test,
Parametric Test,
Assumption of Parametric Test,
Paired T Test,
One Sample T Test,
ANOVA,
ANCOVA,
Regression,
Two Way ANOVA,
Repeated Measure ANOVA,
Multiple Regression
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, ...Musfera Nara Vadia
STATISTICS : Changing the way we do: Hypothesis testing, effect size, power, confidence interval, two-tailed and one tailed test, and other misunderstood issues.
Statistical inference: Statistical Power, ANOVA, and Post Hoc testsEugene Yan Ziyou
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 4 (statistical power, ANOVA, and post hoc tests).
The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
Assessment 4 ContextRecall that null hypothesis tests are of.docxfestockton
Assessment 4 Context
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test again, with the added capability of comparing the means among more than two group at a time. This is the same type of test of difference between group means. In variations on this model, the groups can actually be the same people under different conditions. The main idea is that several group mean values are being compared. The groups each have an average score or mean on some variable. The null hypothesis is that the difference between all the group means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups.
One might ask why we would not use multiple t tests in this situation. For instance, with three groups, why would I not compare groups one and two with a t test, then compare groups one and three, and then compare groups two and three?
The answer can be found in our basic probability review. We are concerned with the probability of a TYPE I error (rejecting a true null hypothesis). We generally set an alpha level of .05, which is the probability of making a TYPE I error. Now consider what happens when we do three t tests. There is .05 probability of making a TYPE I error on the first test, .05 probability of the same error on the second test, and .05 probability on the third test. What happens is that these errors are essentially additive, in that the chances of at least one TYPE I error among the three tests much greater than .05. It is like the increased probability of drawing an ace from a deck of cards when we can make multiple draws.
ANOVA allows us do an "overall" test of multiple groups to determine if there are any differences among groups within the set. Notice that ANOVA does not tell us which groups among the three groups are different from each other. The primary test ...
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.”
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.
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.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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2. Types of t-tests
One sample t-test
Between subjects
t-test
Within subjects
t-test
1
2
3
3. 2. Between Subjects t-test
Also known as independent samples t-test, it is used to compare
groups which are not related (i.e., independent)
4. A researcher wanted to find out if there is a difference in
time spent on social media between males and females.
She hypothesised that females spend more time a day on
social media, compared to males. The researcher collected
data from 25 males and 25 females
Do females spend more time in a day on social media
compared to males?
Example
6. Assumptions Testing…
1. Analyze -> Explore
2. Move ‘HoursOnSocialMedia’
to Dependent List, and
‘Gender’ to Factor List
3. Click on Plots, select
‘Normality plots with tests’
4. Continue, and OK
7. Assumptions Testing…
Since the Shapiro-Wilk p values are both > .05, we conclude that assumption
of normality is not violated
8. Onto SPSS!
• Analyze -> Compare Means -> Independent Samples T Test
• Move ‘HoursOnSocialMedia’ to the right column as the Test Variable
• Select ‘Gender’ as the Grouping Variable
9. Onto SPSS!
• Click on Define Groups
• Since female is coded as ‘1’, and male as ‘2’, type in
‘1’ and ‘2’ under groups 1 and 2, respectively (you
can switch them around if you wish)
• Click continue, and OK!
10. This is to evaluate if the variances
between 2 groups were significantly
different from each other (Assumption
test for homogeneity of Variance)
p-value was .378, which is larger than
.05, indicating assumed equality of
variances. Hence we focus on this row
Onto SPSS!
t value = 8.12, df = 48, and
p value <.001.
This means there was a
significant difference on
social media usage in a
day between males and
females
Females spent more time on
social media per day compared
to males (almost double the
time!)
12. • Select both ‘PreRemedial’ and
‘PostRemedial’ and move them
over to the right column (you can
hold the ctrl key to select multiple
variables)
• OK!
Onto SPSS!
13. We can say that, on average, students who underwent remedial classes
improved their grades from 43.65 to 57.60 (check p value for statistical
significance)
Looking at the output file, we get a t
score = -5.834.
Onto SPSS!
This is the degrees of freedom (n -
number of pairs = 19)
p-value < .001 (smaller than the critical alpha .05).
We reject the null hypothesis. Therefore, we
conclude that scores before and after remedial
lessons were significantly different.
14. When can we use ANOVA?
• The t-test is used to compare the means of two-
groups.
• One-way ANOVA is used to compare the means of
two or more groups.
• We can use one-way ANOVA whenever the
dependent variable (DV) is numerical and the
independent variable (IV) is categorical.
• The independent variable in ANOVA is also called a
factor. 14
15. Examples
The following are situations where we can use
ANOVA:
• Testing the differences in blood pressure among
different groups of people (DV is blood pressure
and the group is the IV).
• Testing which type of social media affects hours of
sleep (type of social media used is the IV and hours
of sleep is the DV).
15
16. Assumptions of ANOVA
• The observations in each group are normally
distributed.
This can be tested by plotting the numerical variable
separately for each group and checking that they all have a
bell shape.
Alternatively, you could use the Shapiro-Wilk test for
normality.
16
17. Assumptions
• The groups have equal variances (i.e., homogeneity of
variance).
You can plot each group separately and check that they exhibit similar
variability.
Alternatively, you can use Levene’s test for homogeneity.
• The observations in each group are independent.
This could be assessed by common sense looking at the study design.
For example, if there is a participant in more than one group, your
observations are not independent.
17
18. Hypothesis Testing
ANOVA tests the null hypothesis:
H0 : The groups have equal means
versus the alternative hypothesis:
H1 : At least one group mean is different from the
other group means.
18
F-Test
19. ANOVA in SPSS
Example:
Is there a difference in optimism scores for young, middle-
aged and old participants?
Categorical IV - Age with 3 levels:
• 29 and younger
• Between 30 and 44
• 45 or above
Continuous DV – Optimism scores
19
20. ANOVA in SPSS
1. Click on Analyze, Compare Means, then One-
way ANOVA.
2. Click on your continuous dependent variable
(e.g., Total Optimism: toptim). Move this into the
box marked Dependent List by clicking on the
arrow button.
3. Click on your independent, categorical variable
(e.g., age 3 groups: agegp3). Move this into the
box labelled Factor.
20
21. ANOVA in SPSS
4. Click the Options button and click on
Descriptive, Homogeneity of variance test,
Brown-Forsythe, Welch test and Means plot.
5. For Missing Values, make sure there is a dot in
the option marked Exclude cases analysis by
analysis. Click on Continue.
6. Click on the button marked Post Hoc. Click on
Tukey.
7. Click on Continue and then OK.
21
22. ANOVA in SPSS
Interpreting the output:
1. Check that the groups have equal variances using Levene’s test for
homogeneity.
• Check the significance value (Sig.) for Levene’s test Based on Mean.
• If this number is greater than .05 you have not violated the assumption of
homogeneity of variance.
22
23. ANOVA in SPSS
Interpreting the output:
2. Check the significance of the ANOVA.
• If the Sig. value is less than or equal to .05, there is a significant difference
somewhere among the mean scores on your dependent variable for the
three groups.
• However, this does not tell us which group is different from which other
group.
23
24. ANOVA in SPSS
Interpreting the output:
3. ONLY if the ANOVA is significant, check the significance of the
differences between each pair of groups in the table labelled
Multiple Comparisons.
24
25. ANOVA in SPSS
Calculating effect size:
• In an ANOVA, effect size will tell us how large the difference between
groups is.
• We will calculate eta squared, which is one of the most common
effect size statistics.
25
Eta squared
=
Sum of squares between groups
Total sum of squares
26. ANOVA in SPSS
Calculating effect size:
26
179.07
8513.02
= .02
According to Cohen (1988):
Small effect: .01
Medium effect: .06
Large effect: .14
27. ANOVA in SPSS
Example results write-up:
A one way between-groups analysis of variance was conducted to explore the impact of
age on levels of optimism. Participants were divided into three groups according to
their age (Group 1: 29yrs or less; Group 2: 30 to 44yrs; Group 3: 45yrs and above).
There was a statistically significant difference at the p < .05 level in optimism scores for
the three age groups: F (2, 432) = 4.6, p = .01. Despite reaching statistical significance,
the actual difference in mean scores between the groups was quite small. The effect
size, calculated using eta squared, was .02. Post-hoc comparisons using the Tukey HSD
test indicated that the mean score for Group 1 (M = 21.36, SD = 4.55) was significantly
different from Group 3 (M = 22.96, SD = 4.49).
27
30. Correlation
Is there a statistically significant association between numerical (continuous) variables?
Ex: HH expenditure share on food & HHsize
Analyze => Correlate => Bivariate
31. Correlation
Is there a statistically significant association between numerical (continuous) variables?
Ex: HH expenditure share on food & HHsize
Analyze => Correlate => Bivariate
32. Correlation
1- Correlations Coefficient - r
• In a range from -1 to +1 (Direction) moving in the same
direction or opposite direction.
• r= 0.2 (weak + association or correlation)
• r= - 0.8 (strong - association or correlation)
2- P < 0.05 (significance cutoff point)
What to look at ?
3- Interpretation
The variables are significantly associated (or behaving together ) either at the
same direction or in opposite direction. NO CAUSALITY.. ! No one makes the
other to happen..!
33. Correlation SPSS Output
Is there a statistically significant association between numerical (continuous) variables?
Ex: Age and income
Analyze => Correlate => Bivariate
Correlations Coefficient -
r
P <
0.05
34. Regression
• Regression analysis is the statistical test used to assess the CAUSALITY .. How variable affect the other.
• Regression analysis is the test to be used to say that the variable X induce the variable Y to happen with the magnitude of
Z.
• We are using a simple linear regression to assess the impact of one independent variable on another dependent variable.
How the HH size impact the FCS or the expenditure on food…?
35. Outcome
Variable
Are the observations independent or correlated?
Alternatives if the normality
assumption is violated (and
small sample size):
independent correlated
Continuous
(e.g. pain
scale,
cognitive
function)
Ttest: compares means between
two independent groups
ANOVA: compares means
between more than two
independent groups
Pearson’s correlation
coefficient (linear
correlation): shows linear
correlation between two continuous
variables
Linear regression:
multivariate regression technique
used when the outcome is
continuous; gives slopes
Paired ttest: compares means
between two related groups (e.g.,
the same subjects before and after)
Repeated-measures
ANOVA: compares changes over
time in the means of two or more
groups (repeated measurements)
Mixed models/GEE
modeling: multivariate regression
techniques to compare changes over
time between two or more groups;
gives rate of change over time
Non-parametric statistics
Wilcoxon sign-rank test:
non-parametric alternative to the
paired ttest
Wilcoxon sum-rank test
(=Mann-Whitney U test): non-
parametric alternative to the ttest
Kruskal-Wallis test: non-
parametric alternative to ANOVA
Spearman rank correlation
coefficient: non-parametric
alternative to Pearson’s correlation
coefficient
36. Scatter Plots of Data with Various
Correlation Coefficients
Y
X
Y
X
Y
X
Y
X
Y
X
r = -1 r = -.6 r = 0
r = +.3
r = +1
Y
X
r = 0
Slide from: Statistics for Managers Using Microsoft® Excel 4th Edition, 2004 Prentice-Hall
40. Continuous outcome (means)
Outcome
Variable
Are the observations independent or correlated?
Alternatives if the normality
assumption is violated (and
small sample size):
independent correlated
Continuous
(e.g. pain
scale,
cognitive
function)
Ttest: compares means between
two independent groups
ANOVA: compares means
between more than two
independent groups
Pearson’s correlation
coefficient (linear
correlation): shows linear
correlation between two continuous
variables
Linear regression:
multivariate regression technique
used when the outcome is
continuous; gives slopes
Paired ttest: compares means
between two related groups (e.g.,
the same subjects before and after)
Repeated-measures
ANOVA: compares changes over
time in the means of two or more
groups (repeated measurements)
Mixed models/GEE
modeling: multivariate
regression techniques to compare
changes over time between two or
more groups; gives rate of change
over time
Non-parametric statistics
Wilcoxon sign-rank test:
non-parametric alternative to the
paired ttest
Wilcoxon sum-rank test
(=Mann-Whitney U test): non-
parametric alternative to the ttest
Kruskal-Wallis test: non-
parametric alternative to ANOVA
Spearman rank correlation
coefficient: non-parametric
alternative to Pearson’s correlation
coefficient
41. Linear regression
In correlation, the two variables are treated as
equals. In regression, one variable is considered
independent (=predictor) variable (X) and the other
the dependent (=outcome) variable Y.
42. Prediction
If you know something about X, this knowledge helps you
predict something about Y. (Sound familiar?…sound
like conditional probabilities?)
43. Multiple linear regression…
• What if age is a confounder here?
• Older men have lower vitamin D
• Older men have poorer cognition
• “Adjust” for age by putting age in the model:
• DSST score = intercept + slope1xvitamin D + slope2 xage
44. Multiple Linear Regression
• More than one predictor…
E(y)= + 1*X + 2 *W + 3 *Z…
Each regression coefficient is the amount of change in the outcome
variable that would be expected per one-unit change of the
predictor, if all other variables in the model were held constant.
45. • A salesperson for a large car brand wants to determine whether
there is a relationship between an individual's income and the
price they pay for a car. As such, the individual's "income" is the
independent variable and the "price" they pay for a car is the
dependent variable. The salesperson wants to use this
information to determine which cars to offer potential customers
in new areas where average income is known.
46. This table provides the R and R2 values. The R value represents the simple
correlation and is 0.873 (the "R" Column), which indicates a high degree of
correlation. The R2 value (the "R Square" column) indicates how much of
the total variation in the dependent variable, Price, can be explained by the
independent variable, Income. In this case, 76.2% can be explained, which
is very large.
47. The next table is the ANOVA table, which reports how well the regression
equation fits the data (i.e., predicts the dependent variable) and is shown below:
This table indicates that the regression model predicts the dependent variable
significantly well. How do we know this? Look at the "Regression" row and go to
the "Sig." column. This indicates the statistical significance of the regression model
that was run. Here, p < 0.0005, which is less than 0.05, and indicates that, overall,
the regression model statistically significantly predicts the outcome variable (i.e., it
is a good fit for the data).
48. The Coefficients table provides us with the necessary information to
predict price from income, as well as determine whether income
contributes statistically significantly to the model (by looking at the "Sig."
column). Furthermore, we can use the values in the "B" column under the
"Unstandardized Coefficients" column
49. Dr. Said T. EL Hajjar
49
SPSS is a tool
-If you provide it with flower, it gives you honey
-If you provide it with rubbish, it gives you garbage
Thank you
