Data Analysis And Business Analytics
Please read the assignment carefully. No plagiarism please See the attached worksheets
the first assignment is document the second assignment is a presentation.
1. Regression Modeling WK 5
This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.
Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_File See attached document
The Excel file for this assignment contains a database with information about the tax assessment value assigned to medical office buildings in a city. The following is a list of the variables in the database:
· Floor Area: square feet of floor space
· Offices: number of offices in the building
· Entrances: number of customer entrances
· Age: age of the building (years)
· Assessed Value: tax assessment value (thousands of dollars)
Use the data to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics.
· Construct a scatter plot in Excel with Floor Area as the independent variable and Assessment Value as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?
· Use Excel’s Analysis Tool Pak to conduct a regression analysis of Floor Area and Assessment Value. Is Floor Area a significant predictor of Assessment Value?
· Construct a scatter plot in Excel with Age as the independent variable and Assessment Value as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?
· Use Excel’s Analysis Tool Pak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of Assessment Value?
Construct a multiple regression model.
· Use Excel’s Analysis Tool Pak to conduct a regression analysis with Assessment Value as the dependent variable and Floor Area, Offices, Entrances, and Age as independent variables. What is the overall fit r^2? What is the adjusted r^2?
· Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated?
· What is the final model if we only use Floor Area and Offices as predictors?
· Suppose our final model is:
· Assessed Value = 115.9 + 0.26 x Floor Area + 78.34 x Offices
· What would be the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?
This assignment illustrates how data analytics can be used to create strategies for sustainable organizational success while integrating the organization’s mission with societal values. You’ll apply statistical time series modeling techniques to identify patterns and develop time-dependent demand models. You’ll practice organizing and delivering a presentation to senior decision-makers. The PowerPoint presentation includes an audio component in addition to speaker notes.
Resources: Microsoft Excel®, DAT565_v3_Wk6_Data_File
Instructions: Work with the provided Excel database. This database has the following columns:
· Lot Code: A unique code that identifies the parking lot
· Lot Capacity: A number with the respective parking lot capacity
· Lot Occupancy: A number with the current number of cars in the parking lot
· Time Stamp: A day/time combination indicating the moment when occupancy was measured
· Day: The day of the week corresponding to the Time Stamp
· Insert a new column, Occupancy Rate, recording occupancy rate as a percentage with one decimal. For instance, if the current Lot Occupancy is 61 and Lot Capacity is 577, then the Occupancy Rate would be reported as 10.6 (or 10.6%).
· Using the Occupancy Rate and Day columns, construct box plots for each day of the week. You can use Insert > Insert Statistic Chart >Box and Whisker for this purpose. Is the median occupancy rate approximately the same throughout the week? If not, which days have lower median occupancy rates? Which days have higher median occupancy rates? Is this what you expected?
· Using the Occupancy Rate and Lot Code columns, construct box plots for each parking lot. You can use Insert > Insert Statistic Chart >Box and Whisker for this purpose. Do all parking lots experience approximately equal occupancy rates? Are some parking lots more frequented than others? Is this what you expected?
· Select any 2 parking lots. For each one, prepare as catter plot showing occupancy rate against Time Stamp for the week 11/20/2016 –11/26/2016. Are occupancy rates time dependent? If so, which times seem to experience highest occupancy rates? Is this what you expected?
Create a 10- to 12-slide presentation with speaker notes and audio. Your audience is the City Council members who are responsible for deciding whether the city invests in resources to set in motion the smart parking space app.
Complete the following in your presentation:
· Outline the rationale and goals of the project.
· Utilize boxplots showing the occupancy rates for each day of the week. Include your interpretation of results.
· Utilize box plots showing the occupancy rates for each parking lot. Include your interpretation of results.
· Provide scatter plots showing occupancy rate against time of day of your selected four parking lots. Include your interpretation of results.
· Make a recommendation about continuing with the implementation of this project.