Can Weather Influence Homicide Rates?

Slides:

Advertisements

Similar presentations

Lesson 10: Linear Regression and Correlation

Advertisements

Time-Series Analysis and Forecasting – Part III

Chapter 8 Linear Regression © 2010 Pearson Education 1.

Chapter 10 Regression. Defining Regression Simple linear regression features one independent variable and one dependent variable, as in correlation the.

Econ 140 Lecture 121 Prediction and Fit Lecture 12.

Regression and Correlation

Statistical Analysis SC504/HS927 Spring Term 2008 Session 5: Week 20: 15 th February OLS (2): assessing goodness of fit, extension to multiple regression.

Long-Term Ambient Noise Statistics in the Gulf of Mexico Mark A. Snyder & Peter A. Orlin Naval Oceanographic Office Stennis Space Center, MS Anthony I.

1 Econometric Load Forecasting Peak and Energy Forecast 06/14/2005 Econometric Load Forecasting Peak and Energy Forecast 06/14/2005.

Relationships Among Variables

The Correlation between Atlantic Hurricane Frequency and ENSO Jessica Johnson EAS 4480 Spring 2012.

Correlation and Linear Regression

Correlation and Regression

Copyright © Cengage Learning. All rights reserved.

Copyright © 2014, 2011 Pearson Education, Inc. 1 Chapter 25 Categorical Explanatory Variables.

Graphical Analysis. Why Graph Data? Graphical methods Require very little training Easy to use Massive amounts of data can be presented more readily Can.

Relationships between Variables. Two variables are related if they move together in some way Relationship between two variables can be strong, weak or.

Biostatistics Unit 9 – Regression and Correlation.

Correlation Correlation measures the strength of the LINEAR relationship between 2 quantitative variables. Labeled as r Takes on the values -1 < r < 1.

Chapter 12 Examining Relationships in Quantitative Research Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.

Earthquakes: Increasing Over Time? By: Dan Arrington EAS 4480 Spring 2012 Image obtained from:

Copyright © 2014, 2011 Pearson Education, Inc. 1 Chapter 19 Linear Patterns.

Regression Regression relationship = trend + scatter

Objective Data  The outlined square marks the area of the study arranged in most cases in a coarse 24X24 grid.  Data from the NASA Langley Research Center.

Federal Departement of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Revisiting Swiss temperature trends * Paulo.

Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Lynn Smith.

Mind’s On – Terms Review

Christina Bonfanti University of Miami- RSMAS MPO 524.

Hurricane Frequency and Sea Surface Temperature EAS 4803 Sheliza Bhanjee.

June 30, 2008Stat Lecture 16 - Regression1 Inference for relationships between variables Statistics Lecture 16.

Comparative Analysis water temperature, wind direction, wind speed and gust from Buoy Stations Sandy Hook New Jersey and the Battery New York in Hudson.

Psychology 202a Advanced Psychological Statistics October 22, 2015.

The Relationship of Weather and Movie Ticket Sales An analysis to show if temperature and precipitation influence ticket sales of Atlantic Station Regal.

Simple Linear Regression The Coefficients of Correlation and Determination Two Quantitative Variables x variable – independent variable or explanatory.

Arctic Oscillation Index vs. FFC Temperatures DJF Will Lanxton.

Maths Study Centre CB Open 11am – 5pm Semester Weekdays

Continuous Outcome, Dependent Variable (Y-Axis) Child’s Height

Unit 5: Regression & Correlation Week 1. Data Relationships Finding a relationship between variables is what we’re looking for when extracting data from.

Correlation and Regression Ch 4. Why Regression and Correlation We need to be able to analyze the relationship between two variables (up to now we have.

Forecasting. Model with indicator variables The choice of a forecasting technique depends on the components identified in the time series. The techniques.

Yandell – Econ 216 Chap 16-1 Chapter 16 Time-Series Forecasting.

Lecture 9 Forecasting. Introduction to Forecasting * * * * * * * * o o o o o o o o Model 1Model 2 Which model performs better? There are many forecasting.

Correlation and Linear Regression

Copyright © Cengage Learning. All rights reserved.

Lesson 4.5 Topic/ Objective: To use residuals to determine how well lines of fit model data. To use linear regression to find lines of best fit. To distinguish.

Entry Task What is the slope of the following lines? 1) 2y = 8x - 70

Linear Regression.

Regression and Correlation

Belinda Boateng, Kara Johnson, Hassan Riaz

The Seasonality of Belief:

Correlations between Atlantic SST and drought conditions

“The Art of Forecasting”

Correlation A Lecture for the Intro Stat Course

Liver Cancer Around the World

Chapter 12 Curve Fitting : Fitting a Straight Line Gab-Byung Chae

Tree Rings vs. Annual Precipitation

Linear Regression.

Weighted Least Squares Fit

Hurricanes & Cruise Passengers in the Caribbean

Inferential Statistics and Probability a Holistic Approach

Solar Variation and Climate

Bivariate Data credits.

Regression All about going back (regressing) from the points to the function from which they came. We are looking at linear regression.

Section 6.2 Prediction.

Algebra Review The equation of a straight line y = mx + b

Regression and Correlation of Data

Presentation transcript:

Can Weather Influence Homicide Rates? Kaitlin Gardner

Motivation Criminal Minds!!! Oakland County Child Killer Occurred during winter months and bodies dumped after snowfall Suicide Correlation During Spring and Summer suicide rates jump Temperature and Rainfall Studies have shown a correlation between higher temperature and rainfall and aggressive behavior

Questions for Analysis Is there a correlation between rainfall data and homicides that indicates that the former causes the later? Is there a trend in the two data sets?

Datasets Rainfall Data Homicide Data Benefits NOAA Weather Station in Boston Range 1988-2008 Monthly totals Homicide Data 20 Year Homicide Report from Boston Mass. Benefits Equal number of data points so no interpolation of any kind

Histogram and Boxplots of Data

Regression Analysis and Correlation Coefficient Least Squares Reduced Major Axis Principle Component Regression Visually Least Squares looks the best Correlation Coefficient R = -0.0733 P = 0.246 The correlation is not strong and it is not significant

Residual Analysis Least Squares Regression has the best fit because the residual analysis shows the least amount of difference between actual and fitted values

Periodogram of Rainfall and homicide Data Expected to see trends that matched if there are high correlations Since there are not it does not surprise me that are trends that aren’t matching There are lots of peaks so it does not seem that there is one trend that really stands out

Coherence of Rainfall and homicide Data Low coherence at every point except for the very beginning on both series Indicates that there is a lot of noise

Conclusion There is no correlation in this specific city however this only took into account one very specific population and area in the United States.