California Water Usage Research Question What factors influence California residence’s daily water usage? Hypotheses H 0 (Null): The independent variables.

Slides:



Advertisements
Similar presentations
Exam Feb 28: sets 1,2 Set 1 due Thurs Memo C-1 due Feb 14 Free tutoring will be available next week Plan A: MW 4-6PM OR Plan B: TT 2-4PM VOTE for Plan.
Advertisements

1 Avalaura L. Gaither and Eric C. Newburger Population Division U.S. Census Bureau Washington, D.C. June 2000 Population Division Working Paper No. 44.
Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and l Chapter 12 l Multiple Regression: Predicting One Factor from Several Others.
Economic and Demographic Scan Towns of Madison & Mayodan November 15, 2012.
Indianapolis-Carmel MSA
Inference for Regression
Paul L. Robinson, Norma Guzman-Becerra, Richard S. Baker Charles R. Drew University of Medicine and Science Didra Brown-Taylor, Integrated Substance Abuse.
Multiple Regression Analysis
Changing Demographics in Texas
St. Louis City Crime Analysis 2015 Homicide Prediction Presented by: Kranthi Kancharla Scott Manns Eric Rodis Kenneth Stecher Sisi Yang.
Regresi dan Analisis Varians Pertemuan 21 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
STATISTICS TUTORIAL Applied Research In Organizational Behavior By: Dr. Goli Sadri.
Texas: Demographic Characteristics and Trends Texas Association of Healthcare Interpreters and Translators August 19, 2011 Dallas, TX.
Tests of Hypotheses: Small Samples Chapter Rejection region.
Consistency of the Weather Nicole Baratelle, Cara Barskey, Youjin Kwon.
© 2000 Prentice-Hall, Inc. Chap Multiple Regression Models.
Multiple Regression Models. The Multiple Regression Model The relationship between one dependent & two or more independent variables is a linear function.
Lecture 22 Multiple Regression (Sections )
Demographic Trends and Missouri’s Children Missouri State Board of Education April 21, 2005 Dr. Bill Elder University of Missouri-Columbia Office of Social.
Lecture 24: Thurs., April 8th
Demographics 14,583 people. 6,137 housing units The racial makeup 97.31% White, 0.23% African American, 2.03% Native American, 0.76% Asian,
Part IV – Hypothesis Testing Chapter 4 Statistics for Managers Using Microsoft Excel, 7e © 2014 Pearson Prentice-Hall, Inc. Philip A. Vaccaro, PhD MGMT.
Introduction to Sociology Social Contexts Slideshow Dr. Carol Caronna Fall 2008 Note: The source for all data is the U.S. Census Bureau. If the data are.
Simple Linear Regression Analysis
Who Attends Private Schools? Enrollment rates by ethnicity in California Magali Barbieri, Shelley Lapkoff, Jeanne Gobalet Lapkoff & Gobalet Demographic.
© 2001 Prentice-Hall, Inc.Chap 14-1 BA 201 Lecture 23 Correlation Analysis And Introduction to Multiple Regression (Data)Data.
The Changing Population of Texas Government Finance Officers Association of Texas October 25, 2012 San Marcos, TX.
The Economy Today: What our measures tell us about the current recession Keith Hall Commissioner Bureau of Labor Statistics March 05, 2010.
Active Learning Lecture Slides
Cody Britton Gregory Ortiz Stephano Bonham Carlos Fierro GROUP MEMBERS.
Population Change in the United States: Hobby Center for the Study of Texas at Rice University A presentation by Dr. Judith Dykes-Hoffmann Using data prepared.
Chapter 13: Inference in Regression
Lecture 14 Testing a Hypothesis about Two Independent Means.
Cultural Difference: Investment Attitudes and Behaviors of High Income Americans Tahira K. Hira – Iowa State University
Exhibit 1. Uninsured Rates for Blacks and Hispanics Are One-and-a-Half to Two Times Higher Than for Whites (2013) Notes: Black and white refer to black.
Lecture 14 Multiple Regression Model
Do not think the point of your paper is to interpret in detail every single regression statistic. Don’t give each of the independent variables equal emphasis.
1 Representations of the Childhood Overweight Problem in Los Angeles County June 24, 2007 County of Los Angeles Public Health Department Nutrition Program.
The Uninsured in Alameda County 2010 December 2010.
Who Lives in New Orleans and Metro Parishes Now? Nihal Shrinath, Vicki Mack, and Allison Plyer datacenterresearch.org.
Demographic Characteristics and Trends in Texas Texas Farm Bureau April 13, 2015 Austin, Texas.
Total Population. Change in Population 1990 to 2012.
Texas Indigent Healthcare Association State Conference October 31, 2013 Austin, Texas Texas Demographic Characteristics and Trends and Health Issues.
SPATIAL RELATIONSHIPS BETWEEN LAND USE REGULATIONS AND DEMOGRAPHY IN CALIFORNIA CITIES Jenneille Hsu, MPP 2011 Feb. 7, 2011 UP206A Introduction to GIS.
CLASS 5 Normal Distribution Hypothesis Testing Test between means of related groups Test between means of different groups Analysis of Variance (ANOVA)
Texas Rural Health Association Conference November 19, 2013 Fort Worth, Texas Texas Demographic Characteristics and Trends and Health Issues.
Who Lives in New Orleans and the Metro Area Now? Vicki Mack and Elaine Ortiz.
Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.
© 2006 by The McGraw-Hill Companies, Inc. All rights reserved. 1 Chapter 12 Testing for Relationships Tests of linear relationships –Correlation 2 continuous.
BULL OR BEAR: The Business Climate in North Carolina.
The Geography of HIV in Harris County, Texas,
Chief Financial Officers of Public Universities in Texas November 5, 2013 Galveston, Texas Texas Demographic Characteristics and Trends and Higher Education.
Greene County Community Health Needs Assessment Sociodemographic Indicators.
Rensselaer County Community Health Needs Assessment Sociodemographic Indicators.
The Changing Population of Texas BP Business Leaders November 8, 2012 Austin, TX.
1.What is Pearson’s coefficient of correlation? 2.What proportion of the variation in SAT scores is explained by variation in class sizes? 3.What is the.
Air pollution is the introduction of chemicals and biological materials into the atmosphere that causes damage to the natural environment. We focused.
The Assessment of Improved Water Sources Across the Globe By Philisile Dube.
2010 Fresno and Bakersfield Population City or County Name % change Fresno City428,000500,00016% Fresno County800,000930, % Bakersfield247,000347,00041%
Albany County Community Health Needs Assessment Sociodemographic Indicators.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.
Schenectady County Community Health Needs Assessment Sociodemographic Indicators.
STAT 104 Section 9 Daniel Moon. Agenda Tests of Population mean μ X Comparisons of two means F-test for equal variances Multiple Linear Regression.
Columbia County Community Health Needs Assessment Sociodemographic Indicators.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Multiple Regression Chapter 14.
 Confidence Intervals  Around a proportion  Significance Tests  Not Every Difference Counts  Difference in Proportions  Difference in Means.
26134 Business Statistics Week 5 Tutorial
Hypothesis Tests for Proportions
Our theory states Y=f(X) Regression is used to test theory.
Presentation transcript:

California Water Usage Research Question What factors influence California residence’s daily water usage? Hypotheses H 0 (Null): The independent variables have no statistically significant associations with domestic daily water usage/person (gal.). H A ( Alternative): The independent variables do have statistically significant associations with domestic daily water usage/person (gal.). By Gabby Hernandez, Meme Torio, Kendall Thompson

Sources U.S. Geological Survey Collected from local, state, and federal agencies National Oceanic & Atmospheric Administration Collected from Climatic Weather Stations 2010 U.S. Census Collected by the federal government (US Census Bureau) Sample Size All 58 California Counties Dependent Daily use/Person) gals. Domestic Water Usage (Daily use/Person) gals. Independent Annual Precipitation (in.) Avg. Annual Temp. (F°) Avg. Household Size % of Population w/ BA or higher (25+) % Black or African American % White (non-Hispanic or Latino) % Hispanic or Latino About Our Data

VariableCoefficientsStandard ErrorP-Value Total Precipitation (In.) Mean Annual Temp. (F°) % Black or African American % White (non-Hispanic or Latino) % Hispanic or Latino % of Pop. With BA or higher (25+) Avg. Household Size F-stat6.287Significance F0.000 Adjusted R-square0.394Number of Observations58 At the 95% confidence level the following variables were statistically significant: Total Annual Precipitation, % of CA residents who are White, % of Population w/ BA or Higher (25+), and Average Household Size. Ex: As the percentage of the population (25 and older) with Bachelor’s degrees or higher increases by 1%, daily domestic water usage per person decreases by 0.75 gallons. At the 95% confidence level the following variables were statistically significant: Total Annual Precipitation, % of CA residents who are White, % of Population w/ BA or Higher (25+), and Average Household Size. Ex: As the percentage of the population (25 and older) with Bachelor’s degrees or higher increases by 1%, daily domestic water usage per person decreases by 0.75 gallons. *The following variables were also included in the data, but were omitted from our regression because they were statistically insignificant: Agricultural Income, Land Area (sq. mi), Population Density (tot. pop/sq. mil), Median Household Income, and Private Nonfarm Employment.

Results: A multiple regression analysis was conducted to examine 7 potential predictors relationship with California residents’ daily water usage/person (gal.). A multiple regression analysis was conducted to examine 7 potential predictors relationship with California residents’ daily water usage/person (gal.). The multiple regression model with all 7 predictors produced an adjusted R 2 = (meaning 39.4% of the dependent variable, California’s residents daily water usage/person (gal.), is responding to the independent variables). The multiple regression model with all 7 predictors produced an adjusted R 2 = (meaning 39.4% of the dependent variable, California’s residents daily water usage/person (gal.), is responding to the independent variables). F(7, 58) = > F-crit.: indicates all coefficients of the independent variables are not statistically equal to zero. F(7, 58) = > F-crit.: indicates all coefficients of the independent variables are not statistically equal to zero. P = < 0.005: rejects the H 0 : stating that at the 95% confidence level, there is a significant relationship between the independent variable and California residents’ daily water usage/person (.gal) (4 out of 7 were statistically significant). P = < 0.005: rejects the H 0 : stating that at the 95% confidence level, there is a significant relationship between the independent variable and California residents’ daily water usage/person (.gal) (4 out of 7 were statistically significant). Looking at the p-values < 0.05 for each predictor we can state: Looking at the p-values < 0.05 for each predictor we can state: California residents’ daily water usage/person (gal.) has a moderately statistically significant direct association with percentage of Californians that are white AND average household size. California residents’ daily water usage/person (gal.) has a moderately statistically significant direct association with percentage of Californians that are white AND average household size. California residents’ daily water usage/person (gal.) has a moderately statistically significant inverse association with total precipitation AND percentage of CA population with a B.A. or higher (25+) California residents’ daily water usage/person (gal.) has a moderately statistically significant inverse association with total precipitation AND percentage of CA population with a B.A. or higher (25+) Possible improvements: Possible improvements: Expand sample size to every CA city. Expand sample size to every CA city. Examine data from multiple years. This study only considers data collected from Examine data from multiple years. This study only considers data collected from 2010.