Predicted Homicide Rate for 2015 St. Louis James Francois Brad Precht Sophia Russo Ken Webb Xichen Liu.

Slides:

Advertisements

Similar presentations

Problems 2.1, 2.2 and 2.3 pages How Fitting! – The Least Squares Line and How Fitting! – The Least Squares Exponential Fitting Models to Data.

Advertisements

© 2003 Anita Lee-Post Forecasting Part 3 By Anita Lee-Post By.

4/16/ Ardavan Asef-Vaziri Variable of interest Time Series Analysis.

1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

CHAPTER 5 TIME SERIES AND THEIR COMPONENTS (Page 165)

T T18-03 Exponential Smoothing Forecast Purpose Allows the analyst to create and analyze the "Exponential Smoothing Average" forecast. The MAD.

St. Louis City Crime Analysis 2015 Homicide Prediction Presented by: Kranthi Kancharla Scott Manns Eric Rodis Kenneth Stecher Sisi Yang.

Chapter 12 - Forecasting Forecasting is important in the business decision-making process in which a current choice or decision has future implications:

T T18-04 Linear Trend Forecast Purpose Allows the analyst to create and analyze the "Linear Trend" forecast. The MAD and MSE for the forecast.

Chapter 3 Forecasting McGraw-Hill/Irwin

Chapter 13 Forecasting.

FORECASTING. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.

Demand Management and Forecasting. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.

Part II – TIME SERIES ANALYSIS C2 Simple Time Series Methods & Moving Averages © Angel A. Juan & Carles Serrat - UPC 2007/2008.

Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

Chapter 11 Solved Problems 1. Exhibit 11.2 Example Linear and Nonlinear Trend Patterns 2.

CHAPTER 3 Describing Relationships

REGRESSION Predict future scores on Y based on measured scores on X Predictions are based on a correlation from a sample where both X and Y were measured.

T T18-06 Seasonal Relatives Purpose Allows the analyst to create and analyze the "Seasonal Relatives" for a time series. A graphical display of.

Naive Extrapolation1. In this part of the course, we want to begin to explicitly model changes that depend not only on changes in a sample or sampling.

Simple Linear Regression NFL Point Spreads – 2007.

CHAPTER 18 Models for Time Series and Forecasting

Chapter 2 Data Patterns and Choice of Forecasting Techniques

1 Given the following data, compute the tracking signal and decide whether or not the forecast should be reviewed.

1 1 Slide © 2009 South-Western, a part of Cengage Learning Chapter 6 Forecasting n Quantitative Approaches to Forecasting n Components of a Time Series.

Slides by John Loucks St. Edward’s University.

LSS Black Belt Training Forecasting. Forecasting Models Forecasting Techniques Qualitative Models Delphi Method Jury of Executive Opinion Sales Force.

1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

Introduction to Forecasting COB 291 Spring Forecasting 4 A forecast is an estimate of future demand 4 Forecasts contain error 4 Forecasts can be.

Forecasting Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.

1 FORECASTING Regression Analysis Aslı Sencer Graduate Program in Business Information Systems.

Forecasting Professor Ahmadi.

MBA.782.ForecastingCAJ Demand Management Qualitative Methods of Forecasting Quantitative Methods of Forecasting Causal Relationship Forecasting Focus.

Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.

1 1 Slide Forecasting Professor Ahmadi. 2 2 Slide Learning Objectives n Understand when to use various types of forecasting models and the time horizon.

Forecasting Chapter 9. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Define Forecast.

Copyright ©2016 Cengage Learning. All Rights Reserved

Shashin Amatya Yi Gao Lauren Reuther INFSYS-6833 Group B Homicide.

© 1999 Prentice-Hall, Inc. Chap Chapter Topics Component Factors of the Time-Series Model Smoothing of Data Series  Moving Averages  Exponential.

 Looked at Regression Too Small Sample Size  Sought Correlations Too Many  Looked at Linear Trend Lines  Neighborhood Statistics What We Tried.

Welcome to MM305 Unit 5 Seminar Prof Greg Forecasting.

T T18-07 Seasonally Adjusted Linear Trend Forecast Purpose Allows the analyst to create and analyze a "Seasonally Adjusted Linear Trend" forecast.

MANAGEMENT SCIENCE AN INTRODUCTION TO

9.2 Linear Regression Key Concepts: –Residuals –Least Squares Criterion –Regression Line –Using a Regression Equation to Make Predictions.

1 1 Chapter 6 Forecasting n Quantitative Approaches to Forecasting n The Components of a Time Series n Measures of Forecast Accuracy n Using Smoothing.

Forecasting is the art and science of predicting future events.

CHAPTER 12 FORECASTING. THE CONCEPTS A prediction of future events used for planning purpose Supply chain success, resources planning, scheduling, capacity.

Forecas ting Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.

Forecast 2 Linear trend Forecast error Seasonal demand.

Chapter 15 Forecasting. Forecasting Methods n Forecasting methods can be classified as qualitative or quantitative. n Such methods are appropriate when.

Demand Forecasting Production and Operations Management Judit Uzonyi-Kecskés Ph.D. Student Department of Management and Corporate Economics Budapest University.

Chapter 3 Lect 6 Forecasting. Seasonality – Repetition at Fixed Intervals Seasonal variations –Regularly repeating movements in series values that can.

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

3-1Forecasting Weighted Moving Average Formula w t = weight given to time period “t” occurrence (weights must add to one) The formula for the moving average.

T T18-02 Weighted Moving Average Forecast Purpose Allows the analyst to create and analyze the "Weighted Moving Average" forecast for up to 5.

Predicting Future. Two Approaches to Predition n Extrapolation: Use past experiences for predicting future. One looks for patterns over time. n Predictive.

Welcome to MM305 Unit 5 Seminar Dr. Bob Forecasting.

Welcome to MM305 Unit 5 Seminar Forecasting. What is forecasting? An attempt to predict the future using data. Generally an 8-step process 1.Why are you.

FORCASTING AND DEMAND PLANNING

City of saint louis 2015 Homicides

CHAPTER 3 Describing Relationships

T18-08 Calculate MAD, MSE Purpose Allows the analyst to create and analyze the MAD and MSE for a forecast. A graphical representation of history and.

CHAPTER 3 Describing Relationships

CHAPTER 3 Describing Relationships

CHAPTER 3 Describing Relationships

CHAPTER 3 Describing Relationships

CHAPTER 3 Describing Relationships

CHAPTER 3 Describing Relationships

1 1 Slide © 2013 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

CHAPTER 3 Describing Relationships

Presentation transcript:

Predicted Homicide Rate for 2015 St. Louis James Francois Brad Precht Sophia Russo Ken Webb Xichen Liu

Project Objective To locate the best forecasting model to predict the Homicide rate for the City of St. Louis for To show where the Homicides will happen, and how many will happen in each area.

Time Series Analysis and Forecasting The objective of time series analysis is to discover a pattern in the historical data or time series; and then extrapolate the pattern show show predicted values for the future. The forecast is based solely on past values of the variable and/or on past forecast errors.

Time Series Analysis and Forecasting Why it is useful to predict the Homicide rate. Since the Homicide rate has no linear format, the use of time series analysis will give the best predicted values for the future based on historic data.

Forecast Error The key concept associated with measuring forecast accuracy is forecast error, which shows the difference between the predicted value and the actual value, as defined by Forecast Error = Actual Value - Forecast

Mean Squared Forecast Error Another measure that negates the problem of positive and negative forecast errors offsetting each other is obtained by computing the average of the squared forecast errors. Mean Squared Forecast Error = average of the sum of squared forecast errors

Percentage Error The average of the absolute values of the percentage forecast errors. This was used to see how good of a prediction we were getting for each neighborhood. Using percentages instead of unit numbers gives a more universal understanding. Ex: One may not know if being off by 5 murders is a lot, but it is universally understood that being off by 80% is a large amount

District 1 Homicide Forecast District 1 makes up 11% of the overall predicted Homicide Rate for St. Louis City.

District 1 Homicide Forecast By Neighborhood

District 2 Homicide Forecast District 2 makes up 3% of the overall predicted Homicide Rate for St. Louis City.

District 2 Homicide Forecast By Neighborhood

District 3 Homicide Forecast District 3 makes up 11% of the overall predicted Homicide Rate for St. Louis City.

District 3 Homicide Forecast By Neighborhood

District 4 Homicide Forecast District 4 makes up 15% of the overall predicted Homicide Rate for St. Louis City.

District 4 Homicide Forecast By Neighborhood

District 5 Homicide Forecast District 5 makes up 27% of the overall predicted Homicide Rate for St. Louis City.

District 5 Homicide Forecast By Neighborhood

District 6 Homicide Forecast District 6 makes up 34% of the overall predicted Homicide Rate for St. Louis City.

District 6 Homicide Forecast By Neighborhood

Predicted Homicide Rate for St. Louis City

To Summarize: Homicides in St. Louis AreasPredicted Homicides 2015 District 114 District District District District 534 District St. Louis City126