Data analysis answering questions using RStudio
Description
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neighborhood id:220 Borough ID:4
220: SO.Jamaica-Baisley Park
Data:https://go.wetransfer.com/t-VHUUlRqaYp
Question A:
- Filter the data to include only residential real estate of your neighborhood. Group it by year, and summarize the data to show the average price of 1 square foot of real estate. List the results.
- Do numbers show for all years? If a year shows N/A, why is that happening?
- Filter your data to focus on residential real estate with adequate information for your model (remove fields with 0 for price or square feet)
- Group the filtered data by year, and summarize the data to show the average price of 1 square foot of real estate. List the results.
- Compare your selected neighborhood with 2 nearby neighborhoods. List the numbers.
- Produce a plot that compares the neighborhoods. Explain the plot and the reason you chose that type of plot.
Question B:
Part One: Time Series
- Perform time series analysis on the total dollar amount of residential real estate sales on your neighborhood.
- Use sales beginning in the year 2009 to develop your model. Develop a forecast for the next 8 quarters of sales.
- Present your findings to include all the below:
- A table that shows the forecast numbers, confidence bands for the next 8 quarters.
- Define the model type that you used (Additive or Multiplicative) and why you used it.
- Determine whether your model factored trend and seasonality and why it did or did not.
Part Two: Regression Forecast
In this part, you are to develop two models. One that includes time only as a predictor. The other includes both time and seasonality. For both models, list and discuss the below points.
- The model equation.
- The significance of each predictor. What does that value represent and what does it mean for your model?
- The R squared and the adjusted R squared. What does each metric mean?
Part Three: Regression Prediction
Use a multiple regression model to determine the sale of a given residential property in your neighborhood. Include:
- Sale Date
- Year built
- Building type (categorical)
- Gross Square Feet
- Number of Units
After you build the model, answer the following questions
- what are the most and least useful predictors of the amount of a sale?
- Are there any redundant independent variables? How can you tell?
- According to your model from (3), which properties were the biggest bargains and which were the most overpriced? How might you account for these disparities?
Part Four: Analysis
Write a paragraph to summarize your findings with a focus on the output, interpretation of the output, and what the insights mean for our decision-making process
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