quantitative reasoning ii - final project presentation

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  • Quantitative reasoning II - final project

    Sarah Lee Hannah Pierce

  • Introduction

  • Variables Variables

    Urban population [ %Urban ] Percentage population with commute over 1 hr [ %LongCommute ] Percentage population with bachelors degrees [ %BA ] Percentage population with graduate degrees [ % MA ]

    Hypotheses Negative linear relationship between %Longcommute VS %BA/MA Positive linear relationship between %Longcommute VS %Urban Positive linear relationship between %Urban VS %BA/MA

  • Patterns Across the United States %Urban

    1st highest D.C. at 100% 2nd highest California at 95% Lowest Maine with 38.7% Highest urban populations on the East and West coasts

    %LongCommute Highest New York at 16.6% Lowest South Dakota at 2.6% Highest commute times in Northeastern states Lowest commute times in Midwestern states

  • Patterns Across the United States %BA

    1st highest D.C. at 50% 2nd highest Massachusetts at 39% Lowest West Virginia at 17.5%

    Highest degree holders in Northeastern states

    Lowest degree holders in Southeastern states

    %MA 1st highest D.C. at 26.9% 2nd highest Massachusetts at 16.7% Lowest Arkansas at 6.3% Regional trends similar to %BA

  • Univariate analysis

  • %BA

    Median = 27.1%Mean = 28%SD = 5.7%

    Distribution is positively / right skewed

    Outlier: D.C.Excluding outlier, distribution is normal

  • %MA

    Median = 9.4%Mean = 10.3%SD = 3.4%

    Distribution is positively / right skewed

    Outlier: D.C.Excluding outlier, distribution is similar

  • %UrbanMedian = 74.2%Mean = 74.1%SD = 14.9%

    Distribution is almost normal

    Outlier: D.C.Excluding outlier, distribution is similar or slightly negative / left skewed

  • %LongCommuteMedian = 5.8%Mean = 6.8%SD = 14.9%

    Distribution is positively / right skewed

    Outliers: Maryland, New Jersey, New YorkExcluding outliers, distribution is closer to normal but still positively / right skewed

  • Bivariate analysis

  • %BA VS %Urban

    Positive linear relationship

    y = 37.88 + 1.30x

    r = +0.50 (moderate)

    r2 = 0.25

  • %MA VS %Urban

    Positive linear relationship

    y = 52.23 + 2.12x

    r = +0.48 (moderate)

    r2 = 0.23

  • %BA VS %LongCommute

    Positive linear relationship

    y = 0.86 + 0.21x

    r = +0.43 (moderate)

    r2 = 0.18

  • %MA VS %LongCommute

    Positive linear relationship

    y = 2.39 + 0.43x

    r = +0.51 (moderate)

    r2 = 0.26

  • %Urban VS %LongCommute

    Positive linear relationship

    y = 0.37 + 0.087x

    r = +0.45 (moderate)

    r2 = 0.21

  • Summary and investigation

  • Summary of observations of relationships Highest correlation between %MA and %LongCommute at

    r = +0.51 %LongCommute increases as %Urban increases Both are unexpected results, results are correlated As % graduates increases, more people live in urban areas Commute times increase due to high traffic in urban areas Walking or biking slower modes of transportation than cars,

    potential increase in %LongCommute data

  • Regional Comparisons

  • Regional Comparisons

  • Regional Comparisons

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