# 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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