Suicide & Poverty in the US

Slides:



Advertisements
Similar presentations
Chapter 3 Examining Relationships Lindsey Van Cleave AP Statistics September 24, 2006.
Advertisements

Multivariate Data/Statistical Analysis SC504/HS927 Spring Term 2008 Week 18: Relationships between variables: simple ordinary least squares (OLS) regression.
Section 10-3 Regression.
Probabilistic & Statistical Techniques Eng. Tamer Eshtawi First Semester Eng. Tamer Eshtawi First Semester
Learning Objectives 1 Copyright © 2002 South-Western/Thomson Learning Data Analysis: Bivariate Correlation and Regression CHAPTER sixteen.
Correlation and Regression Analysis
Regression line – Fitting a line to data If the scatter plot shows a clear linear pattern: a straight line through the points can describe the overall.
1 Relationships We have examined how to measure relationships between two categorical variables (chi-square) one categorical variable and one measurement.
Basic Practice of Statistics - 3rd Edition
Linear Regression.
Relationship of two variables
MAT 254 – Probability and Statistics Sections 1,2 & Spring.
Chapter 4 Correlation and Regression Understanding Basic Statistics Fifth Edition By Brase and Brase Prepared by Jon Booze.
AP STATISTICS LESSON 3 – 3 LEAST – SQUARES REGRESSION.
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 12-1 Correlation and Regression.
Applied Quantitative Analysis and Practices LECTURE#22 By Dr. Osman Sadiq Paracha.
Elementary Statistics Correlation and Regression.
Correlation Correlation is used to measure strength of the relationship between two variables.
Regression. Population Covariance and Correlation.
Linear Regression Model In regression, x = independent (predictor) variable y= dependent (response) variable regression line (prediction line) ŷ = a +
Basic Concepts of Correlation. Definition A correlation exists between two variables when the values of one are somehow associated with the values of.
CHAPTER 5 Regression BPS - 5TH ED.CHAPTER 5 1. PREDICTION VIA REGRESSION LINE NUMBER OF NEW BIRDS AND PERCENT RETURNING BPS - 5TH ED.CHAPTER 5 2.
3.2: Linear Correlation Measure the strength of a linear relationship between two variables. As x increases, no definite shift in y: no correlation. As.
3.3 Correlation: The Strength of a Linear Trend Estimating the Correlation Measure strength of a linear trend using: r (between -1 to 1) Positive, Negative.
Chapter 4 Summary Scatter diagrams of data pairs (x, y) are useful in helping us determine visually if there is any relation between x and y values and,
Chapter 9: Correlation and Regression Analysis. Correlation Correlation is a numerical way to measure the strength and direction of a linear association.
2.6 Scatter Diagrams. Scatter Diagrams A relation is a correspondence between two sets of data X is the independent variable Y is the dependent variable.
Essential Statistics Chapter 51 Least Squares Regression Line u Regression line equation: y = a + bx ^ –x is the value of the explanatory variable –“y-hat”
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
The coefficient of determination, r 2, is The fraction of the variation in the value of y that is explained by the regression line and the explanatory.
Chapters 8 Linear Regression. Correlation and Regression Correlation = linear relationship between two variables. Summarize relationship with line. Called.
BPA CSUB Prof. Yong Choi. Midwest Distribution 1. Create scatter plot Find out whether there is a linear relationship pattern or not Easy and simple using.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
AP STATISTICS LESSON 3 – 3 (DAY 2) The role of r 2 in regression.
CHAPTER 5: Regression ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture Presentation.
BUSINESS MATHEMATICS & STATISTICS. Module 6 Correlation ( Lecture 28-29) Line Fitting ( Lectures 30-31) Time Series and Exponential Smoothing ( Lectures.
Introduction Many problems in Engineering, Management, Health Sciences and other Sciences involve exploring the relationships between two or more variables.
Part II Exploring Relationships Between Variables.
Chapter 5: 02/17/ Chapter 5 Regression. 2 Chapter 5: 02/17/2004 Objective: To quantify the linear relationship between an explanatory variable (x)
Simple Linear Regression In many scientific investigations, one is interested to find how something is related with something else. For example the distance.
Linear Regression Essentials Line Basics y = mx + b vs. Definitions
Copyright © Cengage Learning. All rights reserved.
Statistics 200 Lecture #6 Thursday, September 8, 2016
Clinical Calculation 5th Edition
REGRESSION (R2).
Essential Statistics Regression
Cautions about Correlation and Regression
Chapter One Data Collection
CHAPTER 10 Correlation and Regression (Objectives)
1) A residual: a) is the amount of variation explained by the LSRL of y on x b) is how much an observed y-value differs from a predicted y-value c) predicts.
Linear transformations
a= b= WARM - UP Variable Coef StDev T P
AP STATISTICS LESSON 3 – 3 (DAY 2)
^ y = a + bx Stats Chapter 5 - Least Squares Regression
The Weather Turbulence
Correlation and Regression
Chapter 14 Inference for Regression
Basic Practice of Statistics - 5th Edition Regression
Correlation and Regression
Section 1.4 Curve Fitting with Linear Models
Least-Squares Regression
Correlation and Regression
Basic Practice of Statistics - 3rd Edition Regression
Algebra Review The equation of a straight line y = mx + b
Introduction to simple linear regression
Basic Practice of Statistics - 3rd Edition Lecture Powerpoint
Descriptive Statistics Univariate Data
BEC 30325: MANAGERIAL ECONOMICS
Homework: PG. 204 #30, 31 pg. 212 #35,36 30.) a. Reading scores are predicted to increase by for each one-point increase in IQ. For x=90: 45.98;
Presentation transcript:

Suicide & Poverty in the US Is there a relationship? Based on data collected between 1986 - 2015 Nicolette Whitfield

What is the correlation between the amount of poverty stricken families each year versus the number of deaths caused by suicide each year? Do more or less people commit suicide based on poverty levels?

How is poverty defined by the Us Census? Independent / Explanatory Number of poverty stricken families per year Data collected from US Census website. How is poverty defined by the Us Census? The Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. If a family's total income is less than the family's threshold, then that family and every individual in it is considered in poverty. The official poverty thresholds do not vary geographically. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). The Variables

Poverty Stricken Families Sample Size 30 Minimum 6400 Mean 7,805 Q1 7,098 median 7,640 q3 8,147 maximum 9,645 YEAR Families 1985 7,223 2000 6,400 1986 7,023 2001 6,813 1987 7,005 2002 7,229 1988 6,874 2003 7,607 1989 6,784 2004 7,835 1990 7,098 2005 7,657 1991 7,712 2006 7,668 1992 8,144 2007 7,623 1993 8,393 2008 8,147 1994 8,053 2009 8,792 1995 7,532 2010 9,400 1996 7,708 2011 9,497 1997 7,324 2012 9,520 1998 7,186 2013 9,645 1999 6,792 2014 9,467

The Variables Dependent / Response Number of suicides per year Data collected from Centers for Disease Control & Prevention website. How is suicide defined by the CDC? Suicide is when people direct violence at themselves with the intent to end their lives, and they die as a result of their actions. *Suicide attempts are also recorded by the CDC. However, these numbers were not included in the sample population. The Variables

Suicide Rates Sample Size 30 minimum 29,199 Mean 33,006 Q1 30,575 median 31,122 q3 34,598 maximum 42,773 YEAR Suicides 1985 29,453 2000 29,350 1986 30,904 2001 30,622 1987 30,796 2002 31,655 1988 30,407 2003 31,484 1989 30,232 2004 32,439 1990 30,906 2005 32,637 1991 30,810 2006 33,300 1992 30,484 2007 34,598 1993 31,102 2008 36,035 1994 31,142 2009 36,909 1995 31,284 2010 38,364 1996 30,903 2011 39,518 1997 30,535 2012 40,600 1998 30,575 2013 41,149 1999 29,199 2014 42,773

Data Collection Methods/Sources http://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-people.html United States Suicide Deaths and Rates per 100,000 All Races, Both Sexes, All Ages Produced by: National Center for Injury Prevention and Control, CDC Data Source: NCHS Vital Statistics System for numbers of deaths. Bureau of Census for population estimates. https://http://www.cdc.gov/nchs/fastats/suicide.htm Centers for Disease Control & Prevention Data Collection Methods/Sources

Scatter Plot w/o the Outliers 1993 8,393 31,102 1992 8,144 30,484 1994 8,053 31,142 Scatter Plot w/o the Outliers

the slope, the amount by which 𝑦 changes when 𝑥 increases by one unit Correlation = 0.952780723 The correlation measures the relationship of the linear relationship between the two variables. The correlation between poverty and suicide is very strong making it easier to make estimates regarding future changes. = 0.9078 is the fraction of the variation in the values of y that is explained by the least-squares regression of y on x. Slope = 4 the slope, the amount by which 𝑦 changes when 𝑥 increases by one unit

Predictions y = 3.9274x + 2755.9 If the number of poverty stricken families increased to 15,000 The number of suicides would be expected to reach 61,666. If the number of poverty stricken families decreased to 3,000 The number of suicides is expected to lower to 14,538. Even very strong correlations may not correspond to a real causal relationship (changes in x actually causing changes in y). Correlation may be explained by a lurking variable including other factors that effect poverty stricken families.