The Analytics Division of your organization is entrusted with a consulting contract for a city that plans to institute a policy on home sales transactions. As a member of the team, you have accessed a random sample of transactions obtained for home sales between 2015 and 2018. The data file contains information on sales prices as it relates to various characteristics of the homes and surrounding property. Each line of the data set has an identification number and provides information on 6 other variables.

COMM 215: Business Statistics

Section EC

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The Analytics Division of your organization is entrusted with a consulting contract for a city that plans to institute a policy on home sales transactions. As a member of the team, you have accessed a random sample of transactions obtained for home sales between 2015 and 2018. The data file contains information on sales prices as it relates to various characteristics of the homes and surrounding property. Each line of the data set has an identification number and provides information on 6 other variables.
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Winter 2020 

Data Analysis Project (8%)

Data file:         housesales_data.xlsx

Report uploading Deadline:

Description

The Analytics Division of your organization is entrusted with a consulting contract for a city that plans to institute a policy on home sales transactions. As a member of the team, you have accessed a random sample of transactions obtained for home sales between 2015 and 2018. The data file contains information on sales prices as it relates to various characteristics of the homes and surrounding property. Each line of the data set has an identification number and provides information on 6 other variables.

PPS sample with constraints on column values was taken from a bigger data set, (kc_house_data.csv) accessed from kaggle.com, cleaned and modified to meet the level of this course for a data analysis project.

The data set, HouseSales_data.xlsx, contains the following variables:

IDNO Identification number
PRICE Prediction target Price (dollars)
BEDROOMS Bedrooms/House
BATHROOMS Bathrooms/House
SQFT_LIVING Square footage of the home
SQFT_LOT Square footage of the lot
GRADE Overall grade given to the housing unit (1 for lowest, …, 11 for the best)

 

Managerial Report

Use appropriate (applicable) methods of descriptive statistics, including tables and plots, to summarize the data (depending on the data type – quantitative continuous or discrete and qualitative or categorical). That is, prepare appropriate tables of descriptive statistics (mean, standard deviation, coefficient of variation, quartiles, IQR, correlations) and appropriate plots (histograms, dotplots, bar/pie charts, stem plots, boxplots, scatterplots) of the variables, overall and when appropriate, by categories. You are expected to produce displays that will allow you to describe the distribution in terms of symmetry, dispersion, and outlying observations. Present the summaries of statistical results that will be beneficial in communicating to others.  The results must be fully discussed and clearly explained in context addressing the issues.

Follow the guideline for preparing the project report.  As part of the analysis, you are expected to address the following questions:

Follow the guideline to prepare the report. As part of the analysis, you are expected to address the following questions.

    1. Prepare scatterplots of sales price on square footage of homes, overall and by categories of bedrooms, bathrooms, and grade. Do the plots suggest relationships between the variables? Discuss the nature of the relationships.
    2. Prepare correlations of price and square footage of homes, overall and by categories of bedrooms, bathrooms, and grade. Test for significances of the relationships and interpret your results.
  1. Use the least-squares procedure to fit a prediction equation for price based on square footage of homes, and square footage of lots. Test hypotheses, construct confidence intervals for the significant relationships and interpret all your findings.

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