DAT 565 University of Phoenix

This assignment will help you learn how to apply statistical methods when analyzing operational data, evaluate current marketing strategies’ performance, and recommend actionable business decisions. This opportunity will build critical thinking and problem-solving skills within the context of data analysis and interpretation. In addition, you’ll gain a first-hand understanding of how data analytics supports decision-making and adds value to an organization

Scenario:

Pasta’s R Us, Inc. is a fast-casual restaurant chain specializing in noodle-based dishes, soups, and salads. Since its inception, the business development team has favored opening new restaurants in areas (within a 3-mile radius) that satisfy the following demographic conditions:

Median age between 25 – 45 years old
Household median income above the national average
At least 15% college-educated adult population
Last year, the marketing department rolled out a Loyalty Card strategy to increase sales. Under this program, customers present their Loyalty Card when paying for their orders and receive free food after making ten purchases.

The company has collected data from its 74 restaurants to track important variables such as average sales per customer, year-on-year sales growth, sales per sqft, Loyalty Card usage as a percentage of sales, etc. A key metric of financial performance in the restaurant industry is annual sales per sqft. For example, if a 1200 sqft restaurant recorded $2 million in sales last year, it sold $2,000,000/1200 = $1,667 per sqft.

Executive management wants to know whether they can improve their current expansion criteria. In addition, they want to evaluate the effectiveness of the Loyalty Card marketing strategy and identify feasible, actionable opportunities for improvement. As a member of the analytics department, you are responsible for conducting a thorough statistical analysis of the company’s available database to answer executive management’s questions.

Use CLASS Pasta’s R Us Data.xlsx

Report:

Use the APA formatted Week 2 Signature Assignment Statistical Report.docx (below) as it is APA formatted.

Write about a 750-word statistical report that includes the three sections (a) scope and statistics; (b) analysis; (c) recommendations and implementation

Section 1 – Scope and Descriptive Statistics

State the report’s objective.
Discuss the nature of the current database. What variables were analyzed?
Summarize your descriptive statistics findings from Excel. Use a table and insert appropriate graphs.
Section 2 – Analysis

Use Excel, Insert/Scatter plots with a Trendline that displays the regression equation and R2 for each of the following pairs of variables. Include x- and y-axis labels.

Bach Deg% versus Sales/Sqft
Med Income versus Sales/Sqft
Med Age versus Sales/Sqft
Loyalty Card% versus Sales Growth%
In your report, for each scatter plot, designate the type of relationship observed (increasing/positive, decreasing/negative, or no relationship) and determine what you can conclude from these relationships.

Section 3: Recommendations and Implementation

Based on your findings above, assess which expansion criteria seem to be more effective. Could any expansion criterion be changed or eliminated? If so, which one and why?
Based on your findings above, does it appear as if the Loyalty Card has a positive correlation with sales growth? Would you recommend changing this marketing strategy?
Based on your previous findings, recommend a marketing positioning that targets a specific demographic. (Hint: Are younger people patronizing the restaurants more than older people?)
Indicate what information should be collected to track and evaluate the effectiveness of your recommendations at a period after implementation. How can this data be collected? (Hint: Would you use survey/samples or census?)

DAT 565 University of Phoenix

I’m studying for my Data Analytics class and need an explanation.

 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.  

Resource: Microsoft Excel®, DAT565_v3_Wk6_Data_File

Scenario: A city’s administration isn’t driven by the goal of maximizing revenues or profits but instead looks at improving the quality of life of its residents. Many American cities are confronted with high traffic and congestion. Finding parking spaces, whether in the street or a parking lot, can be time-consuming and contribute to congestion. Some cities have rolled out data-driven parking space management to reduce congestion and make traffic more fluid. 

You’re a data analyst working for a mid-size city that has anticipated significant increments in population and car traffic. The city is evaluating whether it makes sense to invest in infrastructure to count and report the number of parking spaces available at the different parking lots downtown. This data would be collected and processed in real-time, feeding an app that motorists can access to find parking space availability in different parking lots throughout the city. 

Instructions: Work with the provided Excel database. This database has the following columns:

  • LotCode: A unique code that identifies the parking lot
  • LotCapacity: A number with the respective parking lot capacity
  • LotOccupancy: A number with the current number of cars in the parking lot
  • TimeStamp: A day/time combination indicating the moment when occupancy was measured
  • Day: The day of the week corresponding to the TimeStamp
  • Insert a new column, OccupancyRate, recording occupancy rate as a percentage with one decimal. For instance, if the current LotOccupancy is 61 and LotCapacity is 577, then the OccupancyRate would be reported as 10.6 (or 10.6%).
  • Using the OccupancyRate 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 OccupancyRate and LotCode 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 a scatter plot showing the occupancy rate against TimeStamp for the week 11/20/2016 –11/26/2016. Are occupancy rates time-dependent? If so, which times seem to experience the highest occupancy rates? Is this what you expected?

Presentation: 

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 the results.
  • Utilize box plots showing the occupancy rates for each parking lot. Include your interpretation of the results.
  • Provide scatter plots showing occupancy rate against the time of day of your selected four parking lots. Include your interpretation of the results. 
  • Make a recommendation about continuing with the implementation of this project. 

DAT 565 University of Phoenix

Purpose

This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.

Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_File

Instructions:

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:

  • FloorArea: square feet of floor space
  • Offices: number of offices in the building
  • Entrances: number of customer entrances
  • Age: age of the building (years)
  • AssessedValue: 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 FloorArea as the independent variable and AssessmentValue 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 ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue?
  • Construct a scatter plot in Excel with Age as the independent variable and AssessmentValue 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 ToolPak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of AssessmentValue?

Construct a multiple regression model.

  • Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, 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 FloorArea and Offices as predictors?
  • Suppose our final model is:
  • AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices
  • What wouldbe 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?

DAT 565 University of Phoenix

Please let me know if you are not able to access the resource link.

Assignment Content


  1. Purpose This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.
    Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_File
    Instructions: Write the answers to the questions below in the spreadsheet.
    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:

    • FloorArea: square feet of floor space
    • Offices: number of offices in the building
    • Entrances: number of customer entrances
    • Age: age of the building (years)
    • AssessedValue: 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 FloorArea as the independent variable and AssessmentValue 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 ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue? Why?
    • Construct a scatter plot in Excel with Age as the independent variable and AssessmentValue 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 ToolPak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of AssessmentValue? Why?

    Construct a multiple regression model.

    • Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, 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? Why?
    • What is the final model if we only use FloorArea and Offices as predictors?
    • Suppose our final model is:
    • AssessedValue = 115.9 + 0.26 x FloorArea + 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? If so, which observations?

    Submit your spreadsheet with the responses included directly on the spreadsheet. Do not submit a Word document.

    Resources

DAT 565 University of Phoenix

  1. Purpose This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.
    Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_File
    Instructions: 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:

    • FloorArea: square feet of floor space
    • Offices: number of offices in the building
    • Entrances: number of customer entrances
    • Age: age of the building (years)
    • AssessedValue: 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 FloorArea as the independent variable and AssessmentValue 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 ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue?
    • Construct a scatter plot in Excel with Age as the independent variable and AssessmentValue 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 ToolPak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of AssessmentValue?
    • Construct a multiple regression model.
    • Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, 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 ?=0.05? Which predictors can be eliminated?
    • What is the final model if we only use FloorArea and Offices as predictors?
    • Suppose our final model is:
    • AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices
    • What wouldbe 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?

DAT 565 University of Phoenix


  1. Purpose  This assignment is intended to give you an opportunity to strengthen your skills in gathering and analyzing business-related information. It provides a deeper understanding of how companies can look at globalization as part of their strategic and operational plans. The assignment has two parts: one focused on information research and analysis, and the other is on applied analytics. Resources: 

    Part 1: Globalization and Information Research Context: Companies that perform well in their country of origin usually consider expanding operations in new international markets. Deciding where, how, and when to expand is not an easy task, though.
    Many issues need to be considered before crafting an expansion strategy and investing significant resources to this end, including:

    • the level of demand to be expected for the company’s products/services
    • presence of local competitors
    • the regulatory, economic, demographic, and political environments

    Carefully researching and analyzing these and other factors can help mitigate the inherent risk associated with an overseas expansion strategy, thus increasing the likelihood of success.
    As a data analyst in your company’s business development department, you’ve been tasked with the responsibility of recommending countries for international expansion. You’ll write a report to the company’s executive team with your research, analysis, and recommendations. Instructions: Write a 525-word summary covering the following items:

    • According to the article listed above, what were the most important strategic moves that propelled Netflix’s successful international expansion?
    • The article mentions investments in big data and analytics as one of the elements accompanying the second phase of overseas expansion. Why was this investment important? What type of information did Netflix derive from the data collected?
    • According to the article, what is exponential globalization?
    • Not all international expansion strategies are a resounding success, however. Research an article or video that discusses an instance in which an American company’s expansion efforts in another country failed. According to the article/video you selected, what were the main reasons for this failure? Do you agree with this assessment?
    • Explain some of the reasons why certain companies’ expansion plans have failed in the past.

    Part 2: Hypothesis testing Context: Your organization is evaluating the quality of its call center operations. One of the most important metrics in a call center is Time in Queue (TiQ), which is the time a customer has to wait before he/she is serviced by a Customer Service Representative (CSR). If a customer has to wait for too long, he/she is more likely to get discouraged and hang up. Furthermore, customers who have to wait too long in the queue typically report a negative overall experience with the call. You’ve conducted an exhaustive literature review and found that the average TiQ in your industry is 2.5 minutes (150 seconds). Another important metric is Service Time (ST), also known as Handle Time, which is the time a CSR spends servicing the customer. CSR’s with more experience and deeper knowledge tend to resolve customer calls faster. Companies can improve average ST by providing more training to their CSR’s or even by channeling calls according to area of expertise. Last month your company had an average ST of approximately 3.5 minutes (210 seconds). In an effort to improve this metric, the company has implemented a new protocol that channels calls to CSR’s based on area of expertise. The new protocol (PE) is being tested side-by-side with the traditional (PT) protocol. Instructions: Access the Call Center Waiting Time file. Each row in the database corresponds to a different call. The column variables are as follows:

    • ProtocolType: indicates protocol type, either PT or PE
    • QueueTime: Time in Queue, in seconds
    • ServiceTime: Service Time, in seconds
    • Perform a test of hypothesis to determine whether the average TiQ is lower than the industry standard of 2.5 minutes (150 seconds). Use a significance level of α=0.05.
    • Evaluate if the company should allocate more resources to improve its average TiQ.
    • Perform a test of hypothesis to determine whether the average ST with service protocol PE is lower than with the PT protocol. Use a significance level of α=0.05.
    • Assess if the new protocol served its purpose. (Hint: this should be a model of means for 2 independent groups.)
    • Submit your calculations and a 175-word summary of your conclusions.

DAT 565 University of Phoenix

I’m working on a management question and need support to help me understand better.

omplete the following IN YOUR OWN WORDS. It will run thru a checker, thanks.

Purpose 

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 (attached)

Scenario: A city’s administration isn’t driven by the goal of maximizing revenues or profits but instead looks at improving the quality of life of its residents. Many American cities are confronted with high traffic and congestion. Finding parking spaces, whether in the street or a parking lot can be time-consuming and contribute to congestion. Some cities have rolled out data-driven parking space management to reduce congestion and make traffic more fluid.

You’re a data analyst working for a mid-size city that has anticipated significant increments in population and car traffic. The city is evaluating whether it makes sense to invest in infrastructure to count and report the number of parking spaces available at the different parking lots downtown. This data would be collected and processed in real-time, feeding an app that motorists can access to find parking space availability in different parking lots throughout the city.

Instructions: Work with the provided Excel database. This database has the following columns:

  • LotCode: A unique code that identifies the parking lot
  • LotCapacity: A number with the respective parking lot capacity
  • LotOccupancy: A number with the current number of cars in the parking lot
  • TimeStamp: A day/time combination indicating the moment when occupancy was measured
  • Day: The day of the week corresponding to the TimeStamp
  • Insert a new column, OccupancyRate, recording occupancy rate as a percentage with one decimal. For instance, if the current LotOccupancy is 61 and LotCapacity is 577, then the OccupancyRate would be reported as 10.6 (or 10.6%).
  • Using the OccupancyRate 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 OccupancyRate and LotCode 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 scatter plot showing occupancy rate against TimeStamp 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?

Presentation:

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.

DAT 565 University of Phoenix

  1. Purpose This assignment will help you learn how to apply statistical methods when analyzing operational data, evaluate current marketing strategies’ performance, and recommend actionable business decisions. This opportunity will build critical thinking and problem-solving skills within the context of data analysis and interpretation. In addition, you’ll gain a first-hand understanding of how data analytics supports decision-making and adds value to an organization.Scenario: Pasta’s R Us, Inc. is a fast-casual restaurant chain specializing in noodle-based dishes, soups, and salads. Since its inception, the business development team has favored opening new restaurants in areas (within a 3-mile radius) that satisfy the following demographic conditions:
    • Median age between 25 – 45 years old
    • Household median income above the national average
    • At least 15% college-educated adult population
    • Last year, the marketing department rolled out a Loyalty Card strategy to increase sales. Under this program, customers present their Loyalty Card when paying for their orders and receive free food after making ten purchases.
      The company has collected data from its 74 restaurants to track important variables such as average sales per customer, year-on-year sales growth, sales per sqft, Loyalty Card usage as a percentage of sales, etc. A key metric of financial performance in the restaurant industry is annual sales per sqft. For example, if a 1200 sqft restaurant recorded $2 million in sales last year, it sold $2,000,000/1200 = $1,667 per sqft.
      Executive management wants to know whether they can improve their current expansion criteria. In addition, they want to evaluate the effectiveness of the Loyalty Card marketing strategy and identify feasible, actionable opportunities for improvement. As a member of the analytics department, you are responsible for conducting a thorough statistical analysis of the company’s available database to answer executive management’s questions.
      Review your Week 1 Pasta’s R Us.xlsx with Instructor’s feedback
      Report: Use the APA formatted Week 2 Signature Assignment Statistical Report.docx (below) as it is APA formatted.
      Write about a 750-word statistical report that includes the following sections:·Section 1: Scope and descriptive statistics·Section 2: Analysis·Section 3: Recommendations and Implementation
      Section 1 – Scope and descriptive statistics·State the report’s objective.·Discuss the nature of the current database. What variables were analyzed?·Summarize your descriptive statistics findings from Excel. Use a table and insert appropriate graphs.
      Section 2 – Analysis Use Excel, Insert/Scatter plots with a Trendline that displays the regression equation and R2 for each of the following pairs of variables. Include x- and y-axis labels:·Bach Deg% versus Sales/Sqft·Med Income versus Sales/Sqft·Med Age versus Sales/Sqft·Loyalty Card% versus Sales Growth%·In your report, include the scatter plots. For each scatter plot, designate the type of relationship observed (increasing/positive, decreasing/negative, or no relationship) and determine what you can conclude from these relationships.
  2. Section 3: Recommendations and implementation
    • Based on your findings above, assess which expansion criteria seem to be more effective. Could any expansion criterion be changed or eliminated? If so, which one and why?
    • Based on your findings above, does it appear as if the Loyalty Card has a positive correlation with sales growth? Would you recommend changing this marketing strategy?
    • Based on your previous findings, recommend a marketing positioning that targets a specific demographic. (Hint: Are younger people patronizing the restaurants more than older people?)
    • Indicate what information should be collected to track and evaluate the effectiveness of your recommendations. How can this data be collected? (Hint: Would you use survey/samples or census?)

DAT 565 University of Phoenix

Purpose? 

This assignment is intended to give you an opportunity to strengthen your skills in gathering and analyzing business-related information. It provides a deeper understanding of how companies can look at globalization as part of their strategic and operational plans. The assignment has two parts: one focused on information research and analysis, and the other is on applied analytics.  

Resources:?

Part 1: Globalization and Information Research 

Context:?Companies that perform well in their country of origin usually consider expanding operations in new international markets. Deciding where, how, and when to expand is not an easy task, though.  

Many issues need to be considered before crafting an expansion strategy and investing significant resources to this end, including:  

  • the level of demand to be expected for the company’s products/services 
  • presence of local competitors 
  • the regulatory, economic, demographic, and political environments 

Carefully researching and analyzing these and other factors can help mitigate the inherent risk associated with an overseas expansion strategy, thus increasing the likelihood of success. 

As a data analyst in your company’s business development department, you’ve been tasked with the responsibility of recommending countries for international expansion. You’ll write a report to the company’s executive team with your research, analysis, and recommendations. 

Instructions:  

Write a 525-word summary covering the following items: 

  • According to the article listed above, what were the most important strategic moves that propelled Netflix’s successful international expansion? 
  • The article mentions investments in big data and analytics as one of the elements accompanying the second phase of overseas expansion. Why was this investment important? What type of information did Netflix derive from the data collected? 
  • According to the article, what is exponential globalization? 
  • Not all international expansion strategies are a resounding success, however. Research an article or video that discusses an instance in which an American company’s expansion efforts in another country failed. According to the article/video you selected, what were the main reasons for this failure? Do you agree with this assessment? 
  • Explain some of the reasons why certain companies’ expansion plans have failed in the past. 

Part 2: Hypothesis testing  

Context:?Your organization is evaluating the quality of its call center operations. One of the most important metrics in a call center is Time in Queue (TiQ), which is the time a customer has to wait before he/she is serviced by a Customer Service Representative (CSR). If a customer has to wait for too long, he/she is more likely to get discouraged and hang up. Furthermore, customers who have to wait too long in the queue typically report a negative overall experience with the call. You’ve conducted an exhaustive literature review and found that the average TiQ in your industry is 2.5 minutes (150 seconds). 

Another important metric is Service Time (ST), also known as Handle Time, which is the time a CSR spends servicing the customer. CSR’s with more experience and deeper knowledge tend to resolve customer calls faster. Companies can improve average ST by providing more training to their CSR’s or even by channeling calls according to area of expertise. Last month your company had an average ST of approximately 3.5 minutes (210 seconds). In an effort to improve this metric, the company has implemented a new protocol that channels calls to CSR’s based on area of expertise. The new protocol (PE) is being tested side-by-side with the traditional (PT) protocol.  

Instructions: 

Access the Call Center Waiting Time file. Each row in the database corresponds to a different call. The column variables are as follows: 

  • ProtocolType: indicates protocol type, either PT or PE
  • QueueTime: Time in Queue, in seconds
  • ServiceTime: Service Time, in seconds
  • Perform a test of hypothesis to determine whether the average TiQ is lower than the industry standard of 2.5 minutes (150 seconds). Use a significance level of ?=0.05.  
  • Evaluate if the company should allocate more resources to improve its average TiQ. 
  • Perform a test of hypothesis to determine whether the average ST with service protocol PE is lower than with the PT protocol. Use a significance level of ?=0.05.  
  • Assess if the new protocol served its purpose. (Hint: this should be a test of means for 2 independent groups.) 

DAT 565 University of Phoenix

Purpose

This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.

Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_File (attached)

Instructions:

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:

  • FloorArea: square feet of floor space
  • Offices: number of offices in the building
  • Entrances: number of customer entrances
  • Age: age of the building (years)
  • AssessedValue: 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 FloorArea as the independent variable and AssessmentValue 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 ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue?
  • Construct a scatter plot in Excel with Age as the independent variable and AssessmentValue 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 ToolPak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of AssessmentValue?

Construct a multiple regression model.

  • Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, 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 FloorArea and Offices as predictors?
  • Suppose our final model is:
  • AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices
  • What wouldbe 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?

DAT 565 University of Phoenix

  1. Purpose? 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. 
  2. Resource: Microsoft Excel®, DAT565_v3_Wk6_Data_File
  3. Scenario: A city’s administration isn’t driven by the goal of maximizing revenues or profits but instead looks at improving the quality of life of its residents. Many American cities are confronted with high traffic and congestion. Finding parking spaces, whether in the street or a parking lot, can be time-consuming and contribute to congestion. Some cities have rolled out data-driven parking space management to reduce congestion and make traffic more fluid. 
  4. You’re a data analyst working for a mid-size city that has anticipated significant increments in population and car traffic. The city is evaluating whether it makes sense to invest in infrastructure to count and report the number of parking spaces available at the different parking lots downtown. This data would be collected and processed in real-time, feeding an app that motorists can access to find parking space availability in different parking lots throughout the city. 
  5. Instructions: Work with the provided Excel database. This database has the following columns:
    • LotCode: A unique code that identifies the parking lot
    • LotCapacity: A number with the respective parking lot capacity
    • LotOccupancy: A number with the current number of cars in the parking lot
    • TimeStamp: A day/time combination indicating the moment when occupancy was measured
    • Day: The day of the week corresponding to the TimeStamp
    • Insert a new column, OccupancyRate, recording occupancy rate as a percentage with one decimal. For instance, if the current LotOccupancy is 61 and LotCapacity is 577, then the OccupancyRate would be reported as 10.6 (or 10.6%).
    • Using the OccupancyRate 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 OccupancyRate and LotCode 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 a scatter plot showing the occupancy rate against TimeStamp for the week 11/20/2016 –11/26/2016. Are occupancy rates time-dependent? If so, which times seem to experience the highest occupancy rates? Is this what you expected?
    • Presentation: 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. 
  6. 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 the results.
    • Utilize box plots showing the occupancy rates for each parking lot. Include your interpretation of the results.
    • Provide scatter plots showing occupancy rate against the time of day of your selected four parking lots. Include your interpretation of the results. 
    • Make a recommendation about continuing with the implementation of this project. 
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