the general social s u r v e y
play

The General Social S u r v e y IN FE R E N C E FOR C ATE G OR IC - PowerPoint PPT Presentation

Oct 05, 2022 •200 likes •861 views

The General Social S u r v e y IN FE R E N C E FOR C ATE G OR IC AL DATA IN R Andre w Bra y Assistant Professor of Statistics at Reed College INFERENCE FOR CATEGORICAL DATA IN R INFERENCE FOR CATEGORICAL DATA IN R INFERENCE FOR CATEGORICAL

  1. The General Social S u r v e y IN FE R E N C E FOR C ATE G OR IC AL DATA IN R Andre w Bra y Assistant Professor of Statistics at Reed College

  2. INFERENCE FOR CATEGORICAL DATA IN R

  3. INFERENCE FOR CATEGORICAL DATA IN R

  4. INFERENCE FOR CATEGORICAL DATA IN R

  5. INFERENCE FOR CATEGORICAL DATA IN R

  6. INFERENCE FOR CATEGORICAL DATA IN R

  7. E x ploring GSS library(dplyr) glimpse(gss) Observations: 3,300 Variables: 25 $ id <dbl> 518, 1092, 2094, 229, 979, 554, 491, 319, 3143, 1... $ year <dbl> 1982, 1982, 1982, 1982, 1982, 1982, 1982, 1982, 1... $ age <fct> 49, 22, 26, 75, 71, 33, 56, 33, 69, 40, 44, 42, 5... $ class <fct> WORKING CLASS, WORKING CLASS, WORKING CLASS, LOWE... $ degree <fct> HIGH SCHOOL, HIGH SCHOOL, HIGH SCHOOL, LT HIGH SC... $ sex <fct> MALE, MALE, MALE, MALE, FEMALE, FEMALE, MALE, FEM... $ happy <fct> HAPPY, HAPPY, HAPPY, HAPPY, HAPPY, HAPPY, HAPPY, ... INFERENCE FOR CATEGORICAL DATA IN R

  8. E x ploring GSS gss2016 <- filter(gss, year == 2016) ggplot(gss2016, aes(x = happy)) + geom_bar() INFERENCE FOR CATEGORICAL DATA IN R

  9. E x ploring GSS gss2016 <- filter(gss, year == 2016) ggplot(gss2016, aes(x = happy)) + geom_bar() INFERENCE FOR CATEGORICAL DATA IN R

  10. E x ploring GSS p_hat <- gss2016 %>% summarize(prop_happy = mean(happy == "HAPPY")) %>% pull() p_hat 0.7733333 INFERENCE FOR CATEGORICAL DATA IN R

  11. General 95% confidence inter v al ( ^ − 2 × SE , ^ + 2 × SE ) p p Sample proportion pl u s or min u s t w o standard errors INFERENCE FOR CATEGORICAL DATA IN R

  12. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  13. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  14. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  15. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  16. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  17. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  18. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  19. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  20. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  21. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  22. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  23. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  24. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  25. Bootstrap INFERENCE FOR CATEGORICAL DATA IN R

  26. Bootstrap Confidence Inter v al library(infer) boot boot <- gss2016 %>% specify(response = happy, Response: happy (factor) success = “HAPPY”) %>% # A tibble: 500 x 2 generate(reps = 500, replicate stat type = "bootstrap") %>% <int> <dbl> calculate(stat = "prop") 1 1 0.827 2 2 0.740 3 3 0.780 4 4 0.773 5 5 0.747 6 6 0.753 INFERENCE FOR CATEGORICAL DATA IN R

  27. Bootstrap Confidence Inter v al ggplot(boot, aes(x = stat)) + geom_density() INFERENCE FOR CATEGORICAL DATA IN R

  28. Bootstrap Confidence Inter v al SE <- boot %>% summarize(sd(stat)) %>% pull() SE 0.03482251 ( ^ − 2 × SE , ^ + 2 × SE ) p p c(p_hat - 2 * SE, p_hat + 2 * SE) 0.7051883 0.8412784 INFERENCE FOR CATEGORICAL DATA IN R

  29. Let ' s practice ! IN FE R E N C E FOR C ATE G OR IC AL DATA IN R

  30. Interpreting a Confidence Inter v al IN FE R E N C E FOR C ATE G OR IC AL DATA IN R Andre w Bra y Assistant Professor of Statistics at Reed College

  31. Confidence inter v als Concl u sion : the tr u e proportion of Americans that are happ y is bet w een 0.705 and 0.841. What do w e mean b y con � dent ? INFERENCE FOR CATEGORICAL DATA IN R

  32. Dataset 1 ds1 <- filter(gss, year == 2016) p_hat <- ds1 %>% summarize(mean(happy == "HAPPY")) %>% pull() SE <- ds1 %>% specify(response = happy, success = "HAPPY") %>% generate(reps = 500, type = "bootstrap") %>% calculate(stat = "prop") %>% summarize(sd(stat)) %>% pull() c(p_hat - 2 * SE, p_hat + 2 * SE) 0.7073114 0.8393553 INFERENCE FOR CATEGORICAL DATA IN R

  33. INFERENCE FOR CATEGORICAL DATA IN R

  34. INFERENCE FOR CATEGORICAL DATA IN R

  35. INFERENCE FOR CATEGORICAL DATA IN R

  36. INFERENCE FOR CATEGORICAL DATA IN R

  37. INFERENCE FOR CATEGORICAL DATA IN R

  38. INFERENCE FOR CATEGORICAL DATA IN R

  39. INFERENCE FOR CATEGORICAL DATA IN R

  40. INFERENCE FOR CATEGORICAL DATA IN R

  41. INFERENCE FOR CATEGORICAL DATA IN R

  42. INFERENCE FOR CATEGORICAL DATA IN R

  43. Dataset 2 ds2 <- filter(gss, year == 2014) p_hat <- ds1 %>% summarize(mean(happy == "HAPPY")) %>% pull() SE <- ds1 %>% specify(response = happy, success = "HAPPY") %>% generate(reps = 500, type = "bootstrap") %>% calculate(stat = "prop") %>% summarize(sd(stat)) %>% pull() c(p_hat - 2 * SE, p_hat + 2 * SE) 0.8348831 0.9384503 INFERENCE FOR CATEGORICAL DATA IN R

  44. Dataset 3 ds3 <- filter(gss, year == 2012) p_hat <- ds1 %>% summarize(mean(happy == "HAPPY")) %>% pull() SE <- ds1 %>% specify(response = happy, success = "HAPPY") %>% generate(reps = 500, type = "bootstrap") %>% calculate(stat = "prop") %>% summarize(sd(stat)) %>% pull() c(p_hat - 2 * SE, p_hat + 2 * SE) 0.7626359 0.8906974 INFERENCE FOR CATEGORICAL DATA IN R

  45. Dataset 3 ds3 <- filter(gss, year == 2012) p_hat <- ds3 %>% summarize(mean(happy == "HAPPY")) %>% pull() SE <- ds3 %>% specify(response = happy, success = "HAPPY") %>% generate(reps = 500, type = "bootstrap") %>% calculate(stat = "prop") %>% summarize(sd(stat)) %>% pull() c(p_hat - 2 * SE, p_hat + 2 * SE) 0.7626359 0.8906974 INFERENCE FOR CATEGORICAL DATA IN R

  46. Dataset 3 ds3 <- filter(gss, year == 2012) p_hat <- ds3 %>% summarize(mean(happy == "HAPPY")) %>% pull() SE <- ds3 %>% specify(response = happy, success = "HAPPY") %>% generate(reps = 500, type = "bootstrap") %>% calculate(stat = "prop") %>% summarize(sd(stat)) %>% pull() c(p_hat - 2 * SE, p_hat + 2 * SE) 0.7626359 0.8906974 INFERENCE FOR CATEGORICAL DATA IN R

  47. Dataset 3 ds3 <- filter(gss, year == 2012) p_hat <- ds3 %>% summarize(mean(happy == "HAPPY")) %>% pull() SE <- ds3 %>% specify(response = happy, success = "HAPPY") %>% generate(reps = 500, type = "bootstrap") %>% calculate(stat = "prop") %>% summarize(sd(stat)) %>% pull() c(p_hat - 2 * SE, p_hat + 2 * SE) 0.7626359 0.8906974 INFERENCE FOR CATEGORICAL DATA IN R

  48. Dataset 3 ds3 <- filter(gss, year == 2012) p_hat <- ds3 %>% summarize(mean(happy == "HAPPY")) %>% pull() SE <- ds3 %>% specify(response = happy, success = "HAPPY") %>% generate(reps = 500, type = "bootstrap") %>% calculate(stat = "prop") %>% summarize(sd(stat)) %>% pull() c(p_hat - 2 * SE, p_hat + 2 * SE) 0.7626359 0.8906974 INFERENCE FOR CATEGORICAL DATA IN R

  49. Dataset 3 ds3 <- filter(gss, year == 2012) p_hat <- ds3 %>% summarize(mean(happy == "HAPPY")) %>% pull() SE <- ds3 %>% specify(response = happy, success = "HAPPY") %>% generate(reps = 500, type = "bootstrap") %>% calculate(stat = "prop") %>% summarize(sd(stat)) %>% pull() c(p_hat - 2 * SE, p_hat + 2 * SE) 0.7626359 0.8906974 INFERENCE FOR CATEGORICAL DATA IN R

  50. Confidence Inter v als Interpretation : “ We ’ re 95% con � dent that the tr u e proportion of Americans that are happ y is bet w een 0.705 and 0.841.” Width of the inter v al a � ected b y n con � dence le v el p INFERENCE FOR CATEGORICAL DATA IN R

  51. Let ' s practice ! IN FE R E N C E FOR C ATE G OR IC AL DATA IN R

  52. The appro x imation shortc u t IN FE R E N C E FOR C ATE G OR IC AL DATA IN R Andre w Bra y Assistant Professor of Statistics at Reed College

  53. Confidence Inter v als SE Standard errors increase w hen n is small 0.009998905 p is close to 0.5 SE_small_n 0.03809731 SE_low_p 0.00547912 INFERENCE FOR CATEGORICAL DATA IN R

  54. INFERENCE FOR CATEGORICAL DATA IN R

  55. INFERENCE FOR CATEGORICAL DATA IN R

  56. The normal distrib u tion A . K . A the " bell c u r v e ". If obser v ations are independent n is large Then ^ p follo w s a normal distrib u tion INFERENCE FOR CATEGORICAL DATA IN R

  57. Standard de v iation √ ^ × (1 − ^ ) p p n INFERENCE FOR CATEGORICAL DATA IN R

  58. Assessing model ass u mptions Ho w do I check " obser v ations are independent "? This depends u pon the data collection method . What does " n is large " mean ? n × ^ > 10 p n × (1 − ^ ) > 10 p INFERENCE FOR CATEGORICAL DATA IN R

  59. Calc u lating standard error : appro x imation p_hat <- gss2016 %>% summarize(mean(happy == "HAPPY")) %>% pull() n <- nrow(gss2016) c(n * p_hat, n * (1 - p_hat)) 116 35 SE_approx <- sqrt(p_hat * (1 - p_hat) / n) SE_approx 0.03418468 INFERENCE FOR CATEGORICAL DATA IN R

Recommend

Social Well-being in New Zealand Insights from the New Zealand General Social Survey 2012 Philip

Social Well-being in New Zealand Insights from the New Zealand General Social Survey 2012 Philip

Social Well-being in New Zealand Insights from the New Zealand General Social Survey 2012 Philip Walker and Henriette Rawlings The New Zealand General Social Survey Face-to-face interviews with 8,500 people nationwide Held every two years

585 views • 30 slides
General Presentation The legacy Communication on a Social Business Initiative (2011)

General Presentation The legacy Communication on a Social Business Initiative (2011)

Actions and strategy for the social economy and social enterprises The way forward General Presentation The legacy Communication on a Social Business Initiative (2011) follow-up; Council Conclusions on the Social Economy (December

309 views • 12 slides
Being a Social Class Ally for Social Justice General Assembly Thursday, June 25, 2020

Being a Social Class Ally for Social Justice General Assembly Thursday, June 25, 2020

Being a Social Class Ally for Social Justice General Assembly Thursday, June 25, 2020 #uuclassstories Rev. Kimberly Johnson, Denise Moorehead Diane Pansire, Rev. Megan Visser Funded by the Unitarian Universalism Funding Program and the New

399 views • 21 slides
INT/721 Social Impact Measurements Services of General Interst Group 2011-12-06 Context

INT/721 Social Impact Measurements Services of General Interst Group 2011-12-06 Context

European Economic and Social Committee INT/721 Social Impact Measurements Services of General Interst Group 2011-12-06 Context EESC engaged in the growth and development of Social Enterprise Single Market Act 2 calls for a method

48 views • 4 slides
THE AUDITING OF SOCIAL ACCOUNTS SOME GENERAL CONSIDERATIONS INDEPENDENT AUDITING (with

THE AUDITING OF SOCIAL ACCOUNTS SOME GENERAL CONSIDERATIONS INDEPENDENT AUDITING (with

16/11/2012 THE AUDITING OF SOCIAL ACCOUNTS SOME GENERAL CONSIDERATIONS INDEPENDENT AUDITING (with certification) OF SOCIAL ACCOUNTS A DEBATE (CONSIDERING IN THIS CASE ONLY ITALY): A) ONLY VOLUNTARY B) COMPULSORY BY LAW 1

322 views • 7 slides
WELCOME, STAKEHOLDERS OVERVIEW General Information &amp; Updates Licensing Social

WELCOME, STAKEHOLDERS OVERVIEW General Information &amp; Updates Licensing Social

WELCOME, STAKEHOLDERS OVERVIEW General Information &amp; Updates Licensing Social Equity Program Special Announcement General Information &amp; Updates Council Updates Cannabis Laws, Restructuring Phase 3 Eligibility

751 views • 57 slides
SOCIAL MEDIA USERS GROUP 10-29-18 | 9:30 11:00 | Brynhild-Haugland AGENDA General Updates

SOCIAL MEDIA USERS GROUP 10-29-18 | 9:30 11:00 | Brynhild-Haugland AGENDA General Updates

SOCIAL MEDIA USERS GROUP 10-29-18 | 9:30 11:00 | Brynhild-Haugland AGENDA General Updates Branding Reinvention Open Spaces GRATITUDE EMPOWER PEOPLE IMPROVE LIVES INSPIRE SUCCESS GENERAL UPDATES GOOGLE CUSTOM SEARCH

740 views • 40 slides
Agenda 1. Personal Introduction 2. Introduction to Social Media Marketing (SMM) 3. General

Agenda 1. Personal Introduction 2. Introduction to Social Media Marketing (SMM) 3. General

Agenda 1. Personal Introduction 2. Introduction to Social Media Marketing (SMM) 3. General Planning &amp; Thought Process 4. General Execution &amp; Follow Up 5. Introduction to ViralGains, a SMM approach to Video Marketing Page 2 |

617 views • 27 slides
and experience Ana Pedroso, Secretary-General Portuguese Economic and Social Council The role

and experience Ana Pedroso, Secretary-General Portuguese Economic and Social Council The role

The Portuguese model and experience Ana Pedroso, Secretary-General Portuguese Economic and Social Council The role and functioning of ESC Secretaries ILO Technical Meeting for the Economic and Social Councils Sarajevo, 15 November 2017 The

366 views • 14 slides
Social Interactions: General Ideas Steven N. Durlauf Department of Economics, University of

Social Interactions: General Ideas Steven N. Durlauf Department of Economics, University of

Social Interactions: General Ideas Steven N. Durlauf Department of Economics, University of Wisconsin 1 Outline 1. Basic ideas 2. Theory 3. Econometrics 4. Public Policy 2 Social Influences and Individual Outcomes: Basic Ideas 1.

632 views • 47 slides
CULTURE SHOCK General Definition of Culture The ideas, customs, and social behavior of a

CULTURE SHOCK General Definition of Culture The ideas, customs, and social behavior of a

CULTURE SHOCK General Definition of Culture The ideas, customs, and social behavior of a particular people or society. General Definition of Shock A sudden upsetting or surprising event or experience. Confusion, discomfort,

640 views • 25 slides
State of play of the European social dialogue FYROM MONTENEGRO - SERBIA Belgrade

State of play of the European social dialogue FYROM MONTENEGRO - SERBIA Belgrade

State of play of the European social dialogue FYROM MONTENEGRO - SERBIA Belgrade 22-23/01/2015 Outline General overview, actors and outcomes of EU social dialogue Examples of EU SD instruments Budget lines GENERAL OVERVIEW ACTORS

320 views • 19 slides
Introducing Students to the Geography of Environmental Justice Spatial Visualization and Social

Introducing Students to the Geography of Environmental Justice Spatial Visualization and Social

Introducing Students to the Geography of Environmental Justice Spatial Visualization and Social Science for General Education using PolicyMap Victor W . Perez Summer Faculty Institute 2015 Spatial Analysis in the Social Sciences Social issues

352 views • 10 slides
Soc Social ial Wor ork k Sco Scotland tland Annual nual Ge General neral Me Meet eting

Soc Social ial Wor ork k Sco Scotland tland Annual nual Ge General neral Me Meet eting

Soc Social ial Wor ork k Sco Scotland tland Annual nual Ge General neral Me Meet eting ing Frid iday y 17 May y 2018 18 Do Dovecot ecot Studios dios, 10 Infirmar firmary y Stree eet, Edin inbur burgh gh EH1 H1 1LT

712 views • 42 slides
General Threshold Model for Social Cascades Jie Gao, Golnaz Ghasemiesfeh, Grant Schoenebeck,

General Threshold Model for Social Cascades Jie Gao, Golnaz Ghasemiesfeh, Grant Schoenebeck,

General Threshold Model for Social Cascades Jie Gao, Golnaz Ghasemiesfeh, Grant Schoenebeck, Fang-Yi Yu Contagions, diffusion, cascade Ideas, beliefs, behaviors, and technology adoption spread through network Why do we need to study

569 views • 52 slides
G7 Initiative and Vision Zero Fund (VZF) Susanne Hoffmann Director-General for European and

G7 Initiative and Vision Zero Fund (VZF) Susanne Hoffmann Director-General for European and

G7 Initiative and Vision Zero Fund (VZF) Susanne Hoffmann Director-General for European and International Employment and Social Policies, Federal Ministry of Labour and Social Affairs, Germany ILO-ISSA Conference, Dsseldorf, 28-29 October

279 views • 8 slides
Getting Social What is social media? Why does social media matter? What social media

Getting Social What is social media? Why does social media matter? What social media

pwcom .co.uk CIOB Hertfordshire March 2012 Who am I? Getting Social What is social media? Why does social media matter? What social media tools can we apply in construction? How do we start? Paul Wilkinson

396 views • 18 slides
Worker-friendly Explanation of ECOWAS General Convention on Social Security Being presentation at

Worker-friendly Explanation of ECOWAS General Convention on Social Security Being presentation at

Worker-friendly Explanation of ECOWAS General Convention on Social Security Being presentation at sub- regional meeting on ECOWAS Protocols for West African Trade Unions held at Treasure Suites and Conference Centre, Abuja. Iduh L. Onah*

275 views • 12 slides
Provision of social housing REPORT OF THE AUDITOR-GENERAL No. 8 of 2015 16 Why this audit?

Provision of social housing REPORT OF THE AUDITOR-GENERAL No. 8 of 2015 16 Why this audit?

Provision of social housing REPORT OF THE AUDITOR-GENERAL No. 8 of 2015 16 Why this audit? Significant state expenditure on the provision of public and community housing Requests from Parliamentarians Included on our Annual Plan

564 views • 37 slides
Social Media Users Group 7-26-18 | 10:00 11:30 | Brynhild-Haugland Agenda General Updates

Social Media Users Group 7-26-18 | 10:00 11:30 | Brynhild-Haugland Agenda General Updates

Social Media Users Group 7-26-18 | 10:00 11:30 | Brynhild-Haugland Agenda General Updates 10:00 10:10 Interactive Map App 10:10 10:30 Search Appliance Migration 10:30 10:40 BREAK 10:40 10:50 Digital Asset Management

524 views • 25 slides
Launch of National Social Security Strategy (NSSS) Action Plan and Inauguration of Social

Launch of National Social Security Strategy (NSSS) Action Plan and Inauguration of Social

Launch of National Social Security Strategy (NSSS) Action Plan and Inauguration of Social Security Programme Review Conference Organized by Social Security Policy Support (SSPS) Programme Cabinet Division and General Economics Division Basic

162 views • 13 slides
Arlington Democrats June 2020 General Meeting June 3, 2020 7:00 PM Welcome to Resisting While

Arlington Democrats June 2020 General Meeting June 3, 2020 7:00 PM Welcome to Resisting While

Arlington Democrats June 2020 General Meeting June 3, 2020 7:00 PM Welcome to Resisting While Social Distancing MOMENT OF ACTION *Flash* Social Media Challenge! Take Out Your Phones! Open Facebook and search for Find this pinned post:

960 views • 83 slides
Re-imagining General Education FooD for Thought: Social Justice in Your Classroom Making

Re-imagining General Education FooD for Thought: Social Justice in Your Classroom Making

Re-imagining General Education FooD for Thought: Social Justice in Your Classroom Making positive change in the world Connecting with students Discussing Real-world problems and multiple perspectives Creating classroom

387 views • 14 slides
Social Work Continuing Education Series October 15, 2019 Dan Rafferty Office of General Counsel

Social Work Continuing Education Series October 15, 2019 Dan Rafferty Office of General Counsel

Social Work Continuing Education Series October 15, 2019 Dan Rafferty Office of General Counsel Topics When a resident is unable to give consent? What are the proper steps when initiating a Durable Power of Attorney for Healthcare

344 views • 17 slides

More recommend