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Perform secondary data analysis by visiting the US Census Bureau’s American FactFinder

 Demographic Report

You will present demographic data on the city, town, or county where you currently live in preparation for the Research Paper, which is due in Week 5. To do this, you will perform secondary data analysis by visiting the US Census Bureau’s American FactFinder (Links to an external site.). Identify the various social and economic characteristics (i.e., education, population, housing, race, gender, age, income, etc.) of this locale and report the statistics. You can also check government websites for your state, county or city for data. Then, using the statistics you report, identify your findings of social stratification or social inequality found in this locale as highlighted in the reading in the text, Sociology: Beyond Common Sense, for Week 3. In other words, what does the data tell you about the social stratification and social inequalities found in your locale? Be sure to choose a locale that you have access to, as you will be conducting in-person observations in this locale for the Research Paper due in Week 5. If you are currently living outside of the U.S. or will have difficulties physically accessing a location in the U.S., please contact your instructor for additional instructions.

Your demographic report should follow the following structure:

· Introduction (including the locality chosen and general socio-economic info on your locality)

· At least five body paragraphs presenting the social or economic characteristics and specific data for the locality (please state both numbers and percentages if available). In this section of your paper, be sure to present statistics related to education, population, race, gender, age, income (and poverty), housing, as well as any other factors you think relate to the stratification and/or inequality in your chosen locale.

Examples of how data can be presented:

· A table of all the variables you found (if you select this option, please do not copy and paste tables from the American Factfinder website.  Rather, create your own table with all of your variables).

· [Insert/Table in MS Word; add as many columns and rows as you like]:

Location: [examples] San Diego, Kern County, etc…PercentageNumber (n=)
Population (male):\45%45,000
Population (female)55%55,000
Median Income $40,000
% of Population with College Degree30%30,000

· Or a bullet point list of all the variables you found.

· Location

· Population

· Male – 45% (n=45,000)

· Female – 55% (n=55,000)…etc.

· At least two body paragraphs describing how the statistics you report relate to social stratification and social inequality (use your text, Sociology: Beyond Common Sense as reference). In other words, think about what picture the statistics paint about the type of social hierarchies and inequalities that exist in your locale. (Hint: Think about stratification and inequalities based on race, gender, age, social class, etc. Finding statistics about education, income, housing, etc. by race/gender/age can be useful places to make these observations).

· Conclusion (summarize the socio-economic status of your locality based on your findings and related discussions on social stratification and social inequality )

· Reference page.

The paper must be four to five pages in length and formatted according to APA style. You must use at least five resources from the Census website. You must use your text to support your work on social stratification and social inequality. Cite your resources in text and on the reference page

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use census data from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau.

In this homework assignment you will use census data from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau. A copy of this dataset is located at UCI Machine Learning 

Need help with Python programming

In this homework assignment you will use census data from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau. A copy of this dataset is located at UCI Machine Learning repository, please see this link to reach to the dataset website. This dataset contains census data extracted from the 1994 and 1995 Current Population Surveys. We will only work with ‘training’ data (a link to that is provided to you below). The data contains 41 demographic and employment related variables. The abbreviated column names provided to you below. You are expected to read the documentation of this dataset, understand the features and preprocess this dataset. Additional information can be found in the data description and additional comments. Below you will find a code snippet to download and read the data into a pandas dataframe. You can alternatively download it yourself, extract and read it manually. The questions are shown in the subsequent cells. You can provide your answers in this file. Make sure to change the notebook’s name and add your name before submitting. 

### Q1 – Identify the data scales and data types for each variable in census data. Identify the domain for each variable by checking the attributes’ values. Then, create a data quality report for both categorical (nominal, ordinal) and continuous (interval, ratio) variables. For data scales, identify whether an attribute is nominal, ordinal, interval or ratio scale. For data types, identify the domain and provide an appropriate data type (integer, float, String, date, Boolean). See if that data type is correct in your dataframe. For domain, inspect each distinct value for each attribute. Identify missing values. Also include the bar plots and histograms for visualizing the distributions. The examples for a continious and a categorical feature can be seen below. You do not need to use jupyter formatting provided here. You can print a DataFrame or read a csv, and display it. Make sure you have the csv in your zip file. 

Q2 – Identify the data quality issues for each attribute. Create a data quality plan and implement the changes. Identify missing values, potential anomalies and outliers. Finally, remedy these issues. 

Q3 – For all the interval and ratio scale features, create a scatter plot matrix (pairplots) and correlation matrix. For all ordinal scale features, create a correlation matrix using Spearman correlation. Use the data scales list you created in when creating a correlation matrix using Spearman rank, please use all features having an order (i.e., ordinal, interval, ratio scale variables) and not just ordinal features.  

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census data from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau

In this homework assignment you will use census data from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau. A copy of this dataset is located at UCI Machine Learning re

Need help with Python programming

In this homework assignment you will use census data from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau. A copy of this dataset is located at UCI Machine Learning repository, please see this link to reach to the dataset website. This dataset contains census data extracted from the 1994 and 1995 Current Population Surveys. We will only work with ‘training’ data (a link to that is provided to you below). The data contains 41 demographic and employment related variables. The abbreviated column names provided to you below. You are expected to read the documentation of this dataset, understand the features and preprocess this dataset. Additional information can be found in the data description and additional comments. Below you will find a code snippet to download and read the data into a pandas dataframe. You can alternatively download it yourself, extract and read it manually. The questions are shown in the subsequent cells. You can provide your answers in this file. Make sure to change the notebook’s name and add your name before submitting. 

### Q1 – Identify the data scales and data types for each variable in census data. Identify the domain for each variable by checking the attributes’ values. Then, create a data quality report for both categorical (nominal, ordinal) and continuous (interval, ratio) variables. For data scales, identify whether an attribute is nominal, ordinal, interval or ratio scale. For data types, identify the domain and provide an appropriate data type (integer, float, String, date, Boolean). See if that data type is correct in your dataframe. For domain, inspect each distinct value for each attribute. Identify missing values. Also include the bar plots and histograms for visualizing the distributions. The examples for a continious and a categorical feature can be seen below. You do not need to use jupyter formatting provided here. You can print a DataFrame or read a csv, and display it. Make sure you have the csv in your zip file. 

Q2 – Identify the data quality issues for each attribute. Create a data quality plan and implement the changes. Identify missing values, potential anomalies and outliers. Finally, remedy these issues. 

Q3 – For all the interval and ratio scale features, create a scatter plot matrix (pairplots) and correlation matrix. For all ordinal scale features, create a correlation matrix using Spearman correlation. Use the data scales list you created in when creating a correlation matrix using Spearman rank, please use all features having an order (i.e., ordinal, interval, ratio scale variables) and not just ordinal features

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Identify the data scales and data types for each variable in census data

In this homework assignment you will use census data from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau. A copy of this dataset is located at UCI Machine Learning re

Need help with Python programming

In this homework assignment you will use census data from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau. A copy of this dataset is located at UCI Machine Learning repository, please see this link to reach to the dataset website. This dataset contains census data extracted from the 1994 and 1995 Current Population Surveys. We will only work with ‘training’ data (a link to that is provided to you below). The data contains 41 demographic and employment related variables. The abbreviated column names provided to you below. You are expected to read the documentation of this dataset, understand the features and preprocess this dataset. Additional information can be found in the data description and additional comments. Below you will find a code snippet to download and read the data into a pandas dataframe. You can alternatively download it yourself, extract and read it manually. The questions are shown in the subsequent cells. You can provide your answers in this file. Make sure to change the notebook’s name and add your name before submitting. 

### Q1 – Identify the data scales and data types for each variable in census data. Identify the domain for each variable by checking the attributes’ values. Then, create a data quality report for both categorical (nominal, ordinal) and continuous (interval, ratio) variables. For data scales, identify whether an attribute is nominal, ordinal, interval or ratio scale. For data types, identify the domain and provide an appropriate data type (integer, float, String, date, Boolean). See if that data type is correct in your dataframe. For domain, inspect each distinct value for each attribute. Identify missing values. Also include the bar plots and histograms for visualizing the distributions. The examples for a continious and a categorical feature can be seen below. You do not need to use jupyter formatting provided here. You can print a DataFrame or read a csv, and display it. Make sure you have the csv in your zip file. 

Q2 – Identify the data quality issues for each attribute. Create a data quality plan and implement the changes. Identify missing values, potential anomalies and outliers. Finally, remedy these issues. 

Q3 – For all the interval and ratio scale features, create a scatter plot matrix (pairplots) and correlation matrix. For all ordinal scale features, create a correlation matrix using Spearman correlation. Use the data scales list you created in when creating a correlation matrix using Spearman rank, please use all features having an order (i.e., ordinal, interval, ratio scale variables) and not just ordinal features.  

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  • Writing services provided by experts. Looking for expert essay writers, thesis and dissertation writers, personal statement writers, or writers to provide any other kind of custom writing service?
  • Enjoy our bonus services. You can make a free inquiry before placing and your order and paying this way, you know just how much you will pay. A verdict was rendered against three parent chaperones. How was the third parent included in the case?
  • Premium papers. We provide the highest quality papers in the writing industry. Our company only employs specialized professional writers who take pride in satisfying the needs of our huge client base by offering them premium writing services Identify the data scales and data types for each variable in census data

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