relational Statistics - workshops 1, II, III.pptx
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Three hands-on workshops on how to apply relational statistics in news reporting. In teaching partnership with the amazing Jodi Upton.
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Recommandé
Basic stats
This document provides an overview of basic statistical concepts including:
- Types of data such as nominal, ordinal, interval, and ratio scales of measurement.
- Descriptive statistics like frequency distributions, measures of central tendency (mean, median, mode), and measures of variability (range, variance, standard deviation).
- Inferential statistics concepts including population parameters, sample statistics, null hypotheses, types of statistical errors, and common statistical tests like t-tests, F-tests, chi-square tests, and correlation.
- Key considerations in statistical analysis like practical versus statistical significance and biases in published literature.
Session1b.ppt
This document provides an introduction to concepts in biostatistics and hypothesis testing. It outlines the objectives of learning about study design, types of data, hypothesis testing, p-values, and choosing appropriate statistical tests. It also describes office hours, topics beyond the scope of the class, and objectives for learning about stages of research studies, hypothesis tests, t-tests, and Wilcoxon tests.
Katagorisel veri analizi
This document provides an overview of categorical data analysis techniques. It discusses chi-square tests for independence and their limitations in describing association strength. Better measures include comparing proportions, calculating odds ratios, and examining concordant/discordant pairs. Larger sample sizes can make weak associations appear statistically significant with chi-square tests, so other measures are preferable. The document also covers logistic regression and residual analysis for categorical data.
A 2016 Election Post-Mortem: The ABC News/Washington Post Tracking Poll
This document summarizes the findings of a post-mortem analysis of the 2016 ABC News/Washington Post tracking poll conducted after Donald Trump's unexpected election victory. The analysis found the final poll estimate of Hillary Clinton leading by 4 points was accurate based on the poll's historical average error of 2 points. While some state polls underestimated Trump support, the national poll found no evidence of "shy" Trump voters or other issues. Overall, the national popular vote estimate was sound despite missing Trump's electoral college victory.
#06198 Topic PSY 325 Statistics for the Behavioral & Social Scien.docx
#06198 Topic: PSY 325 Statistics for the Behavioral & Social Sciences
Number of Pages: 3 (Double Spaced)
Number of sources: 10
Writing Style: APA
Type of document: Other (Not listed)
Academic Level:Undergraduate
Category: Physics
Language Style: English (U.S.)
Order Instructions: ATTACHEDS
follow the requirements as answer the questions and one of them is to answer instead.
Basically is to make comments in each of the person names and make some questions as the requirements acquire as I copy and paste in the first page.
I don't really have much time for this assignment because is due tomorrow as you can I have no time remaining because I already use my accommodations because I was sick.
Please like the time I play because otherwise, I will get 0 grade which I don't want it. we had this problem in the past.
Thank you for your understanding
Guided Response: Review several of your classmates’ posts. Provide a substantive response to at least three of your peers, and respond to comments on your post. Do you agree with your classmate’s selection of the best value based upon their data? What suggestions might you make for other options? Explain your suggestions citing relevant information from the article and/or your text. Cite your sources in APA format as outlined in the Ashford Writing Center. FOLLOWW THE REQUIREMENTS AS NEEDED. ALL IS TO MAKE COMMENTS AND QUESTIONS. UNDER THE ANGELA ONLY NEED TO ANSWER INSTEAD ASK QUESTION.
1) Esther Landsberg
· Begin your discussion by reporting your results for each of the values listed above.
My data points were 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20.
Mean: 10.5
Standard error: 1.32287566
Median: 10.5
Mode: no mode
Standard deviation: 5.91607978
Sample variance: 35
Kurtosis: 1.70428571
Skewness: 0
Range: 19
Minimum: 1
Maximum: 20
Sum: 210
Count: 20
· Based on this output, which single value best describes this set of data and why?
Based on this output, I would say that the single value that best describes this set of data would be the mean because it tells us the average of the data points.
· If you could pick three of these values instead of only one, which three would you choose and why?
If I could pick three values, I would say the mean, standard deviation, and sample variance would best describe the set of data. The mean because it tells us the average, sample deviation because it tells us how close to the average or spread out the numbers actually are, and sample variance because it helps to estimate unbiasedly.
ANSWER THE QUESTIONS AND MAKE COMMENTS AS FOLLOWING THE REQUIREMENTS ABOVE.
2) Brenda Kyle
Brenda Kyle
PSY 325 Statistics for the Behavioral & Social Sciences
Instructor: Nikola Lucas
Week 1-Discussion
June 4, 2019
At first, I had chosen number 1 through 20 but then seen another classmate had the same thing so had to change it. The chosen numbers are 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 15.
MELJUN CORTES research seminar_1__data_analysis_basics_slides
This document discusses the basics of descriptive data analysis, including defining different types of variables, coding principles, and univariate data analysis. It describes continuous variables as always numeric and categorical variables as information sorted into categories, with ordinal variables having intrinsic order, nominal variables lacking order, and dichotomous variables having only two levels. The document outlines steps for coding variables and cleaning data, as well as techniques for univariate analysis of continuous and categorical variables to check data quality.
MELJUN CORTES research seminar_1_data_analysis_basics
This document discusses the basics of descriptive data analysis, including defining different types of variables, coding principles, and univariate data analysis. It describes continuous and categorical variables, including ordinal, nominal, and dichotomous variables. The document outlines steps for coding variables and cleaning data, as well as techniques for univariate analysis of continuous and categorical variables, including examining distributions, frequencies, and comparing observed vs expected values. The goal is to familiarize readers with fundamental concepts for organizing, coding, and performing initial exploratory analysis of data.
MELJUN CORTES research seminar_1__data_analysis_basics_slides_2nd_updates
This document discusses the basics of descriptive data analysis, including defining different types of variables, coding principles, and univariate data analysis. It describes continuous variables, which are always numeric, and categorical variables such as ordinal, nominal, and dichotomous. The document outlines how to code different variable types using values and labels and provides examples. It also covers cleaning data, calculating basic statistics, and examining the distribution and frequencies of variables to check data quality in univariate analysis.
Recommandé
Basic stats
This document provides an overview of basic statistical concepts including:
- Types of data such as nominal, ordinal, interval, and ratio scales of measurement.
- Descriptive statistics like frequency distributions, measures of central tendency (mean, median, mode), and measures of variability (range, variance, standard deviation).
- Inferential statistics concepts including population parameters, sample statistics, null hypotheses, types of statistical errors, and common statistical tests like t-tests, F-tests, chi-square tests, and correlation.
- Key considerations in statistical analysis like practical versus statistical significance and biases in published literature.
Session1b.ppt
This document provides an introduction to concepts in biostatistics and hypothesis testing. It outlines the objectives of learning about study design, types of data, hypothesis testing, p-values, and choosing appropriate statistical tests. It also describes office hours, topics beyond the scope of the class, and objectives for learning about stages of research studies, hypothesis tests, t-tests, and Wilcoxon tests.
Katagorisel veri analizi
This document provides an overview of categorical data analysis techniques. It discusses chi-square tests for independence and their limitations in describing association strength. Better measures include comparing proportions, calculating odds ratios, and examining concordant/discordant pairs. Larger sample sizes can make weak associations appear statistically significant with chi-square tests, so other measures are preferable. The document also covers logistic regression and residual analysis for categorical data.
A 2016 Election Post-Mortem: The ABC News/Washington Post Tracking Poll
This document summarizes the findings of a post-mortem analysis of the 2016 ABC News/Washington Post tracking poll conducted after Donald Trump's unexpected election victory. The analysis found the final poll estimate of Hillary Clinton leading by 4 points was accurate based on the poll's historical average error of 2 points. While some state polls underestimated Trump support, the national poll found no evidence of "shy" Trump voters or other issues. Overall, the national popular vote estimate was sound despite missing Trump's electoral college victory.
#06198 Topic PSY 325 Statistics for the Behavioral & Social Scien.docx
#06198 Topic: PSY 325 Statistics for the Behavioral & Social Sciences
Number of Pages: 3 (Double Spaced)
Number of sources: 10
Writing Style: APA
Type of document: Other (Not listed)
Academic Level:Undergraduate
Category: Physics
Language Style: English (U.S.)
Order Instructions: ATTACHEDS
follow the requirements as answer the questions and one of them is to answer instead.
Basically is to make comments in each of the person names and make some questions as the requirements acquire as I copy and paste in the first page.
I don't really have much time for this assignment because is due tomorrow as you can I have no time remaining because I already use my accommodations because I was sick.
Please like the time I play because otherwise, I will get 0 grade which I don't want it. we had this problem in the past.
Thank you for your understanding
Guided Response: Review several of your classmates’ posts. Provide a substantive response to at least three of your peers, and respond to comments on your post. Do you agree with your classmate’s selection of the best value based upon their data? What suggestions might you make for other options? Explain your suggestions citing relevant information from the article and/or your text. Cite your sources in APA format as outlined in the Ashford Writing Center. FOLLOWW THE REQUIREMENTS AS NEEDED. ALL IS TO MAKE COMMENTS AND QUESTIONS. UNDER THE ANGELA ONLY NEED TO ANSWER INSTEAD ASK QUESTION.
1) Esther Landsberg
· Begin your discussion by reporting your results for each of the values listed above.
My data points were 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20.
Mean: 10.5
Standard error: 1.32287566
Median: 10.5
Mode: no mode
Standard deviation: 5.91607978
Sample variance: 35
Kurtosis: 1.70428571
Skewness: 0
Range: 19
Minimum: 1
Maximum: 20
Sum: 210
Count: 20
· Based on this output, which single value best describes this set of data and why?
Based on this output, I would say that the single value that best describes this set of data would be the mean because it tells us the average of the data points.
· If you could pick three of these values instead of only one, which three would you choose and why?
If I could pick three values, I would say the mean, standard deviation, and sample variance would best describe the set of data. The mean because it tells us the average, sample deviation because it tells us how close to the average or spread out the numbers actually are, and sample variance because it helps to estimate unbiasedly.
ANSWER THE QUESTIONS AND MAKE COMMENTS AS FOLLOWING THE REQUIREMENTS ABOVE.
2) Brenda Kyle
Brenda Kyle
PSY 325 Statistics for the Behavioral & Social Sciences
Instructor: Nikola Lucas
Week 1-Discussion
June 4, 2019
At first, I had chosen number 1 through 20 but then seen another classmate had the same thing so had to change it. The chosen numbers are 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 15.
MELJUN CORTES research seminar_1__data_analysis_basics_slides
This document discusses the basics of descriptive data analysis, including defining different types of variables, coding principles, and univariate data analysis. It describes continuous variables as always numeric and categorical variables as information sorted into categories, with ordinal variables having intrinsic order, nominal variables lacking order, and dichotomous variables having only two levels. The document outlines steps for coding variables and cleaning data, as well as techniques for univariate analysis of continuous and categorical variables to check data quality.
MELJUN CORTES research seminar_1_data_analysis_basics
This document discusses the basics of descriptive data analysis, including defining different types of variables, coding principles, and univariate data analysis. It describes continuous and categorical variables, including ordinal, nominal, and dichotomous variables. The document outlines steps for coding variables and cleaning data, as well as techniques for univariate analysis of continuous and categorical variables, including examining distributions, frequencies, and comparing observed vs expected values. The goal is to familiarize readers with fundamental concepts for organizing, coding, and performing initial exploratory analysis of data.
MELJUN CORTES research seminar_1__data_analysis_basics_slides_2nd_updates
This document discusses the basics of descriptive data analysis, including defining different types of variables, coding principles, and univariate data analysis. It describes continuous variables, which are always numeric, and categorical variables such as ordinal, nominal, and dichotomous. The document outlines how to code different variable types using values and labels and provides examples. It also covers cleaning data, calculating basic statistics, and examining the distribution and frequencies of variables to check data quality in univariate analysis.
Answer all questions individually and cite all work!!1. Provid.docx
Answer all questions individually and cite all work!!
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2. Identify three catalysts to enable innovativeness. Explain how they would enable innovation in your organization.
3. Why is it significant that an organization allow for failure? What are some significant ways an organization can allow for failure and still find success?
4. Making a pivot has saved organizations from completely deteriorating. Research an organization of your choice that has made an impactful pivot. Write an 8-10 sentence summary of the organization and the monumental pivot.
iStockphoto/Thinkstock
chapter 11
Nominal Data and the
Chi-Square Tests: What Occurs
Versus What Is Expected
Learning Objectives
After reading this chapter, you will be able to. . .
1. describe nominal data.
2. complete and explain the chi-square goodness-of-fit test.
3. complete and explain the chi-square test of independence.
4. present and interpret the results of the two types of chi-square test in proper APA format.
CN
CO_LO
CO_TX
CO_NL
CT
CO_CRD
suk85842_11_c11.indd 407 10/23/13 1:45 PM
CHAPTER 11Section 11.1 Nominal Data
When there was an important development in statistical analysis in the early part of the 20th century, more often than not Karl Pearson was associated with it. Many of those
who made important contributions were members of the department that Pearson founded
at University College London. William Sealy Gosset, who developed the t-tests; Ronald A.
Fisher, who developed analysis of variance; and Charles Spearman, who developed factor
analysis, all gravitated to Pearson’s department at some point. Although social relations
among these men were not always harmonious, they were enormously productive schol-
ars, and this was particularly true of Pearson. Besides the correlation coefficient named for
him, Pearson also developed an analytical approach related to Spearman’s factor analysis
called principal components analysis, and he developed the procedures that are the sub-
jects of this chapter, the chi-square tests. The Greek letter chi (x) is pronounced “kie” like
“pie.” Chi is the equivalent of the letter c, rather than the letter x, which it resembles.
11.1 Nominal Data
With the exception of Spearman’s rho in Chapter 9, the attention in Chapters 1 through 10 has been directed at procedures designed for interval or ratio data. Sometimes
the data is not interval scale, nor is it the ordinal-scale data that Spearman’s rho accom-
modates. When the data is nominal scale, often one of the chi-square (x2) tests is used.
It will be helpful to review what makes data nominal scale. Nominal data either fits into a
category or it does not, which is why nominal data is sometimes called categorical or clas-
sification data. Because the analysis is based on counting the data, it is also called count or
frequency data. Compared to ratio, interva ...
29510Nominal Data and the Chi-Square TestsJupiterimag.docx
295
10Nominal Data and the Chi-Square Tests
Jupiterimages/Stockbyte/Thinkstock
Chapter Learning Objectives
After reading this chapter, you should be able to do the following:
1. Describe nominal data.
2. Complete and explain the chi-square goodness-of-fit-test.
3. Complete and explain the chi-square test of independence.
tan82773_10_ch10_295-320.indd 295 3/3/16 2:33 PM
© 2016 Bridgepoint Education, Inc. All rights reserved. Not for resale or redistribution.
Section 10.1 Nominal Data
Introduction
When an important development in statistical analysis took place in the early part of the 20th
century, more often than not Karl Pearson was associated with it. As the text previously noted,
many of those who made important contributions were members of the department that Pear-
son founded at University College London. Those who gravitated to Pearson’s department
included William Sealy Gosset, who developed the t tests; R. A. Fisher, who developed analysis
of variance; and Charles Spearman, who did the early work on factor analysis. Although social
relations among these men were not always harmonious, they were enormously productive
scholars, and this was particularly true of Pearson. Besides the correlation coefficient named
for him, Pearson developed an analytical approach related to Spearman’s factor analysis called
principal components analysis, as well as the procedures that are the subjects of this chapter,
the chi-square tests. (The Greek letter chi [χ] is pronounced “kye” and rhymes with sky. Chi is
the Greek equivalent of the letter c, rather than the letter x, which it resembles.)
10.1 Nominal Data
With the exception of Spearman’s rho in Chapter 8, Chapters 1 through 9 have focused on pro-
cedures designed for interval or ratio data. Sometimes, however, the data are neither interval
scale nor the ordinal-scale data that Spearman’s rho accommodates. When the data are nomi-
nal scale, researchers often use one of the chi-square tests.
Because our focus has been so much on interval- and ratio-scale data, it might be helpful to
review what makes data nominal scale. Nominal data either fit a category or do not, which
is why they are sometimes referred to as “categorical data.” Because of this presence-or-
absence quality, analyses of nominal data are based on counting how frequently they occur,
and for that reason they are also called “count data.” Compared to ratio, interval, and even
ordinal data, nominal data provide relatively little information. They reveal only the presence
or absence of a characteristic, not how much of the characteristic, or how the individual’s pos-
session of the characteristic compares to others in the category. To illustrate: when people are
classified according to whether they are
1. left-handed or right-handed, or
2. Buddhist, Jewish, Muslim, or
3. African American, Hispanic, or Native American, or
4. blue-eyed or brown-eyed, or
5. introverted or extroverted,
then the resulting.
SOC2002 Lecture 11
The document discusses various techniques for quantitative data analysis, including descriptive analysis, exploratory analysis, and statistical analysis. Descriptive analysis involves frequency tables, charts, and summary statistics to describe individual and groups of variables. Exploratory analysis examines relationships between two or more variables using cross-tabulations and correlations. Statistical analysis tests for significant relationships using techniques like chi-squared tests, t-tests, and regression analysis. The remainder of the document provides examples and explanations of these analytical methods.
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An independent samples t-test was conducted to compare perceptions of democracy in North Africa and Southern Africa using data from the 2015 Afrobarometer survey. The test revealed a statistically significant difference in perceptions between the two regions, with North African citizens reporting lower average levels of perceived democracy (M=4.44, SD=2.31) than Southern African citizens (M=5.31, SD=2.12), t(1083)=-6.36, p<0.001. This suggests that recent social movements in North Africa may have negatively impacted perceptions of democracy in that region relative to Southern Africa.
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This document describes using dummy predictor variables in multiple regression analysis. It provides an example using hypothetical data on faculty salaries. Key points:
- Dummy variables allow inclusion of categorical predictors like gender or political party in regression by coding them numerically.
- For k categories, k-1 dummy variables are needed. This example uses gender (coded 0,1) and college (coded 1,2,3) as predictors.
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Vergoulas Choosing the appropriate statistical test (2019 Hippokratia journal)
This document provides a step-by-step guide for choosing the appropriate statistical test for data analysis. It outlines 7 key steps: 1) determining if the analysis is univariate or multivariable, 2) identifying if the study examines differences or correlations, 3) determining if the data is paired or independent, 4) characterizing the type of outcome variable, 5) assessing the normality of distribution for continuous variables, 6) identifying the number of groups for independent variables, and 7) selecting valid statistical tests that match the characteristics identified in the previous steps, such as t-tests, ANOVA, regression analyses. Examples of applying this process are provided.
Essay On Juvenile Incarceration
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Choosing a test.pptx
This document discusses statistical tests for different types of data and research questions. It explains that there are parametric tests that assume a normal distribution, like the t-test, z-test, and F-tests, and non-parametric tests that don't make distribution assumptions, like chi-square, Mann-Whitney, and Wilcoxon. It provides examples of the types of data and questions each test is suited for, such as using t-tests for comparing two means, chi-square for nominal associations, and Spearman's rank for ordinal correlations. Finally, it presents a summary table outlining which statistical tests to use for different sample characteristics and levels of measurement.
Statistics What you Need to KnowIntroductionOften, when peop.docx
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
The research question itself
The sample size
The type of data you have collected
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generalizations, and possibilities regarding the relationship between the independent variable and the dependent variable to indicate how those inferences answer the research question. Researchers can make predictions and estimations about how the results will fit the overall population. Statistics can also be described in terms of the types of data they can analyze. Non-parametric statistics can be used with nominal or ordinal data, while parametric statistics can be used with interval and ratio data types.
Types of Data
There are four types of data that a researcher may collect.
Nominal Data Sets
The Nominal data set includes simple classifications of data into categories which are all of equal weight and value. Examples of categories that are equal to each other include gender (male, female), state of birth (Arizona, Wyoming, etc.), membership in a group (yes, no). Each of these categories is equivalent to the other, without value judgments.
Ordinal Data Sets
Ordinal data sets also have data classified into categories, but these categories have some form or order or ranking attached, often of some sort of value / val.
GradTrack: Getting Started with Statistics September 20, 2018
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology Evans Library Introduction to Statistics: Don't be afraid
Video presentation with audio available on YouTube:http://bit.ly/GradTrackStatistics2018
GradTrack: Getting Started with Statistics September 20, 2018
GradTrack: Getting Started with Statistics September 20, 2018Evans Library at Florida Institute of Technology
YouTube Presentation: http://bit.ly/GradTrackStatistics2018
Dr. Gary Burns, Professor, School of Psychology, Florida Institute of Technology, Evans Library GradTrack Workshop1 1 data
The document discusses the steps involved in conducting a statistical investigation to test a claim:
1) State the specific, measurable, and comparable claim and identify an appropriate benchmark
2) Collect relevant data from the population or a representative sample
3) Calculate appropriate statistical values from the data
4) Run statistical tests to determine whether to accept or reject the initial claim
It also provides definitions and classifications that are important for statistical analysis, such as the differences between populations and samples, parameters and statistics, and nominal, ordinal, interval, and ratio data types.
Overview of different statistical tests used in epidemiological
This document provides an overview of different statistical tests used in epidemiological studies and their applications. It discusses topics such as data types (quantitative, categorical), variables, statistics, null and alternative hypotheses, errors in significance testing, and choices between parametric and nonparametric tests. The key information provided includes classifications of variable types, definitions of common statistics, explanations of hypotheses testing and p-values, and guidance on selecting appropriate tests based on the scale and distribution of the data.
T test
This document provides an overview of a presentation on statistical hypothesis testing using the t-test. It discusses what a t-test is, how to perform a t-test, and provides an example of a t-test comparing spelling test scores of two groups that received different teaching strategies. The document outlines the six steps for conducting statistical hypothesis testing using a t-test: 1) stating the hypotheses, 2) choosing the significance level, 3) determining the critical values, 4) calculating the test statistic, 5) comparing the test statistic to the critical values, and 6) writing a conclusion.
Impact of Race and Ethnicity on Preemployment Psychological Assessment
A review of Brower Psychological Services' evaluation practices from 2018-2022 found:
1) No violations of adverse impact standards or statistically significant correlations between race/ethnicity and pass rates when looking at all agencies served in aggregate.
2) No adverse impact violations for the Aurora Police Department in any year, though 2021 first-stage evaluations showed a possible significant relationship between race and pass rates.
3) Additional analyses suggest the 2021 Aurora PD results were likely due to an undisclosed special recruiting program, not inherent bias in evaluation practices. Uniform processes across time and agencies were observed.
Data screening
This document discusses important concepts for screening data, including detecting and handling errors, missing data, outliers, and ensuring assumptions of analyses are met. It describes why data screening is important to obtain accurate results and avoid bias. Key topics covered include identifying patterns of missing data, different types of missing data (MCAR, MAR, MNAR), and various methods for treating missing values. Outliers are defined and their impact explained. Common transformations are presented to achieve normality, linearity, and homoscedasticity. Checklists are provided for conducting data screening.
Stat11t chapter1
The document summarizes key concepts from Chapter 1 of the textbook "Elementary Statistics" including:
- The difference between a population and a sample, and how statistics uses samples to make inferences about populations.
- The different types of data: quantitative, categorical, discrete vs. continuous data.
- The different levels of measurement for data: nominal, ordinal, interval, and ratio.
- The importance of critical thinking when analyzing data and statistics, including considering context, sources, sampling methods, and avoiding misleading graphs, samples, conclusions, or survey questions.
Stat11t Chapter1
This document provides an overview of key concepts from Chapter 1 of the textbook "Elementary Statistics". It defines important statistical terms like population, sample, parameter, and statistic. It also distinguishes between different types of data and levels of measurement. Additionally, it discusses the importance of collecting sample data through appropriate random sampling methods. Critical thinking in statistics is emphasized, highlighting factors like the context, source, and sampling method of data when evaluating statistical claims. Different ways of collecting data through studies and experiments are also introduced.
Tools of the Trade: First Generation SAT Preparation: Best Practices for Over...
The Princeton Review Foundation discusses their approach to preparing low-income and first-generation students for the SAT and SAT.
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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chapter 11
Nominal Data and the
Chi-Square Tests: What Occurs
Versus What Is Expected
Learning Objectives
After reading this chapter, you will be able to. . .
1. describe nominal data.
2. complete and explain the chi-square goodness-of-fit test.
3. complete and explain the chi-square test of independence.
4. present and interpret the results of the two types of chi-square test in proper APA format.
CN
CO_LO
CO_TX
CO_NL
CT
CO_CRD
suk85842_11_c11.indd 407 10/23/13 1:45 PM
CHAPTER 11Section 11.1 Nominal Data
When there was an important development in statistical analysis in the early part of the 20th century, more often than not Karl Pearson was associated with it. Many of those
who made important contributions were members of the department that Pearson founded
at University College London. William Sealy Gosset, who developed the t-tests; Ronald A.
Fisher, who developed analysis of variance; and Charles Spearman, who developed factor
analysis, all gravitated to Pearson’s department at some point. Although social relations
among these men were not always harmonious, they were enormously productive schol-
ars, and this was particularly true of Pearson. Besides the correlation coefficient named for
him, Pearson also developed an analytical approach related to Spearman’s factor analysis
called principal components analysis, and he developed the procedures that are the sub-
jects of this chapter, the chi-square tests. The Greek letter chi (x) is pronounced “kie” like
“pie.” Chi is the equivalent of the letter c, rather than the letter x, which it resembles.
11.1 Nominal Data
With the exception of Spearman’s rho in Chapter 9, the attention in Chapters 1 through 10 has been directed at procedures designed for interval or ratio data. Sometimes
the data is not interval scale, nor is it the ordinal-scale data that Spearman’s rho accom-
modates. When the data is nominal scale, often one of the chi-square (x2) tests is used.
It will be helpful to review what makes data nominal scale. Nominal data either fits into a
category or it does not, which is why nominal data is sometimes called categorical or clas-
sification data. Because the analysis is based on counting the data, it is also called count or
frequency data. Compared to ratio, interva ...
29510Nominal Data and the Chi-Square TestsJupiterimag.docx
295
10Nominal Data and the Chi-Square Tests
Jupiterimages/Stockbyte/Thinkstock
Chapter Learning Objectives
After reading this chapter, you should be able to do the following:
1. Describe nominal data.
2. Complete and explain the chi-square goodness-of-fit-test.
3. Complete and explain the chi-square test of independence.
tan82773_10_ch10_295-320.indd 295 3/3/16 2:33 PM
© 2016 Bridgepoint Education, Inc. All rights reserved. Not for resale or redistribution.
Section 10.1 Nominal Data
Introduction
When an important development in statistical analysis took place in the early part of the 20th
century, more often than not Karl Pearson was associated with it. As the text previously noted,
many of those who made important contributions were members of the department that Pear-
son founded at University College London. Those who gravitated to Pearson’s department
included William Sealy Gosset, who developed the t tests; R. A. Fisher, who developed analysis
of variance; and Charles Spearman, who did the early work on factor analysis. Although social
relations among these men were not always harmonious, they were enormously productive
scholars, and this was particularly true of Pearson. Besides the correlation coefficient named
for him, Pearson developed an analytical approach related to Spearman’s factor analysis called
principal components analysis, as well as the procedures that are the subjects of this chapter,
the chi-square tests. (The Greek letter chi [χ] is pronounced “kye” and rhymes with sky. Chi is
the Greek equivalent of the letter c, rather than the letter x, which it resembles.)
10.1 Nominal Data
With the exception of Spearman’s rho in Chapter 8, Chapters 1 through 9 have focused on pro-
cedures designed for interval or ratio data. Sometimes, however, the data are neither interval
scale nor the ordinal-scale data that Spearman’s rho accommodates. When the data are nomi-
nal scale, researchers often use one of the chi-square tests.
Because our focus has been so much on interval- and ratio-scale data, it might be helpful to
review what makes data nominal scale. Nominal data either fit a category or do not, which
is why they are sometimes referred to as “categorical data.” Because of this presence-or-
absence quality, analyses of nominal data are based on counting how frequently they occur,
and for that reason they are also called “count data.” Compared to ratio, interval, and even
ordinal data, nominal data provide relatively little information. They reveal only the presence
or absence of a characteristic, not how much of the characteristic, or how the individual’s pos-
session of the characteristic compares to others in the category. To illustrate: when people are
classified according to whether they are
1. left-handed or right-handed, or
2. Buddhist, Jewish, Muslim, or
3. African American, Hispanic, or Native American, or
4. blue-eyed or brown-eyed, or
5. introverted or extroverted,
then the resulting.
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Types of Data
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一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
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二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
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本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
原版办理【微信号:BYZS866】【巴斯大学毕业证(Bath毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【关于学历材料质量】
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路、）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版制作【微信:41543339】【利兹贝克特大学毕业证(LeedsBeckett毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
一比一原版(harvard毕业证书）哈佛大学毕业证如何办理
一模一样【微信：A575476】【(harvard毕业证书）哈佛大学毕业证成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：A575476】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：A575476】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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3:国家专业人才认证中心颁发入库证书
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留信网服务项目：
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3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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relational Statistics - workshops 1, II, III.pptx
- 1. Stats I, II and II Frequencies, crosstabs, correlation, ANOVA, regression Jodi Upton and Crina Boros CIJ Summer 2017
- 2. The Data Ladder -- categorical I. One type of response (yes or no) Frequencies: Crosstabs: Yes 432 45.3% No 521 54.7% Live in Texas Like Bush Yes No Yes 382 200 No 125 307
- 3. The Data Ladder-- categorical II. Two or more types of responses (race) Frequencies: Race Frequency Asian 4,766 Black 12,807 White 9,766 Hispanic 7.236 Crosstabs: Race Warning Ticket None Black 1 6 0 White 4 3 1 Hispanic 0 1 2 Unknown 3 2 2
- 4. The Data Ladder-- categorical III. Ordinal Data (use crosstabs and frequencies) When the value doesn’t mean much, but the order does: Grade levels Age categories Income categories
- 5. The Data Ladder-- continuous data Examples: Income Housing prices Response time (police and fire) Distance travelled (commute) What you can do: Mean Median Range Rank Correlation ANOVA Regression
- 6. Go to Kahoot.it (on your phone or computer)
- 8. In traditional statistics, the normal curve means 95% of observations will fall within most of this curve
- 11. Independent vs. Dependent variable Independent Comes first in time Can be more than one variable Dependent What you are measuring
- 12. Polling A March 9, 2016 Quinnipiac poll found the following results, with a +- 3.7 margin of error, at the 95% confidence level. Who is really ahead? What’s the MOE for women? White males?
- 14. CORRELATION AKA: Pearson’s r or coefficient of correlation ● Between 1 and -1 ● If both variables move in the same direction → positive relationship ● If variables move in opposite direction → negative relationship -1 0 +1 Strong relationship weak weak strong
- 15. Got it, so far?
- 16. ANOVA What it assumes: Normal distribution Independence of errors Outliers removed* Equal variance (*but journalists love those!) What it measures: Whether the difference within the group is greater than the difference between the groups
- 17. ANOVA needs an hypothesis Null hypothesis: the treatment has no impact F = the treatment variance + the random variance the random variance
- 18. What you’re looking for: The F statistic is between 0 and 1 (if it’s negative, you’ve made a mistake) If F > F crit, you must reject the null hypothesis (treatment had an impact) If F < F crit, you can’t rule out the null hypothesis The p value If the p value is less than alpha (.05) then the result is significant (it matters) If the p value is greater than alpha, the results are not significant
- 19. In Massachusetts, are there more suicides in local jails or in the prison system?
- 20. What you still don’t know What accounts for the difference? For that you need a t-test, regression or other tool.
- 21. ‘HOW TO CHOOSE’ MADE EASY THE 2 MOST ESSENTIAL QUESTIONS: 1. DO YOU HAVE CATEGORICAL OR CONTINUOUS DATA IN THE VARIABLES? 2. WHAT IS YOUR INDEPENDENT VARIABLE AND DEPENDENT VARIABLE? INDEPENDENT DEPENDENT STATISTICS Categorical Categorical CROSS-TAB Continuous Continuous LINEAR-REGRESSION / MULTIPLE REGRESSION Categorical Continuous ANALYSIS OF VARIANCE / ANOVA Continuous Categorical LOGISTIC REGRESSION
- 22. iT’S A FINE DAY FOR LINEAR REGRESSION! Image by Paul Wesley
- 23. Linear Regression I. Does the data fit the 1st assumption: is there a linear relationship? 1. Scatter plot 2. Trendline 3. Create a new variable II. The last assumption: the data should approximate a Bell curve (normal distribution). 1. Data analysis toolpak - Descriptive statistics 1. Mean and average should be close to each other 1. Tick Summary Statistics 2. Tick Confidence level >> 95%
- 26. Source: Indian Journal of Dermatology https://tinyurl.com/ydad546c
- 27. Linear Regression Conditions met? Run the Regression from the Data Analysis tool pack: Y Range - Dependant X Range - Independent Turn on LABELS CONFIDENCE LEVEL 95% NEW WORKSHEET - REGRESSION RESIDUALS ADJUSTED R SQUARE 0 TO 1.0. The closer it gets to 1, the closest is to perfection. SIGNIFICANT F THE RESIDUAL STORY - Sort! THE LINEAR REGRESSION IS JUST THE BEGINNING OF THE REPORTING Conrad Carlberg - Statistical Analysis
- 29. Thank you! Jodi Upton: jodi.upton@gmail.com and @jodiupton Crina Boros: crinaboros@gmail.com Special thanks to: Jennifer LaFleur, Center for Investigative Reporting/Reveal Steve Doig, Arizona State University
Notes de l'éditeur
- Starting at the bottom...
- Heard continuous referred to as ‘infinite’ but it’s really not. Income and prices, for example, are limited to two decimal places. Another way that may help: Continuous (measured) vs discrete (counted) If it would take ‘forever’ to count, it’s probably continuous
- Amy Poehler
- Skew = body (positive or negative); kurtosis = tail
- All you really need to know: is my data evenly distributed
- The margin of error does not depend on the size of the population; it depends on the size of the sample. (In astronomy, the margin of error is 4.12 light years -- the distance to Proxima Centauri)
- Important: 1 in 20 observations will NOT! Bayesian: start with a different hypothesis, 100% within curve, but may be ‘off’
- ANOVA was created by an evolutionary biologist and statistician, who wanted to be able to tell if two groups were the same or different, ie were they the same species or not?
- In other words, is there enough randomness within the sample, that it outweighs any variance between the samples -- and any measured difference is the result of chance?
- “F” stands for Sir Reginald Fisher, who invented this “P” stands for the probability that -- if the null hypothesis is true -- the results are ‘extreme’ (one in 500 chance of being wrong)
- A paper ‘suicide suit’ worn by a model
- ANOVA was created by an evolutionary biologist and statistician, who wanted to be able to tell if two groups were the same or different, ie were they the same species or not?