Evaluate the impact of several different factors (i.e., regressors) on customer satisfaction

Final Project Description

Essential Information:

• The project consists of applying statistical analyses based on a real-world dataset using statistical software (IBM SPSS Statistics). The expected level of statistical analyses will be based on the lectures and lab sessions.

• IBM SPSS Statistics is available on All FASS labs and ALL central labs (including the library) computers.

Context and Dataset:

The dataset contains data related to customer visits to restaurants, including demographic details, visit-specific metrics, and customer satisfaction. Overall, the objective of the report is to evaluate the impact of several different factors (i.e., regressors) on customer satisfaction. In other words, the objective of this report is to estimate the effect of food rating, service rating, spend, wait time and income on customer satisfaction.

Please note, as per the confidentiality of the data some numbers have been amended; therefore, this dataset is not suitable for research purposes.

Below follows the description of the variables in the dataset.

• CustomerID: Unique identifier for each customer.
• Age: Age of the customer.
• Gender: Gender of the customer (Male/Female).
• Income: Annual income of the customer in USD.
• VisitFrequency: How often the customer visits the restaurant (Daily, Weekly, Monthly, Rarely).
• Spend: amount spent by the customer per visit in USD.
• PreferredCuisine: The type of cuisine preferred by the customer (Italian, Chinese, Indian, Mexican, American).
• TimeOfVisit: The time of day the customer usually visits (Breakfast, Lunch, Dinner).
• GroupSize: Number of people in the customer`s group during the visit.
• DiningOccasion: The occasion for dining (Casual, Business, Celebration).
• MealType: Type of meal (Dine-in, Takeaway).
• OnlineReservation: Whether the customer made an online reservation (0: No, 1: Yes).
• DeliveryOrder: Whether the customer ordered delivery (0: No, 1: Yes).
• LoyaltyProgramMember: Whether the customer is a member of the restaurant`s loyalty program (0: No, 1: Yes).
• WaitTime: Average wait time for the customer in minutes.
• ServiceRating: Customer`s rating of the service (Likert scale 1 to 5).
• FoodRating: Customer`s rating of the food (Likert scale 1 to 5).
• AmbienceRating: Customer`s rating of the restaurant ambiance (Likert scale 1 to 5).
• Satisfaction: Customer`s rating of their overall satisfaction (Likert scale 1 to 5)

Note: Likert scale variables can be treated as scale variables in statistical analysis of questionnaires, as although the categories are ordinal (1-5, low/high, bad/good), it still makes sense to compute central measures of tendency and dispersion.

Content and Structure of the Assignment:

Introduction

1. Please report the word count, your name and student number at the beginning of your project.
2. Provide a brief explanation of the methodology, such as data, the definition of dependent and independent variables, the objective of the analyses (i.e., purpose), and the regression model.

Descriptive Analysis

1. Provide a table showing summary statistics of the variables for the entire sample. Discuss the results.
2. Provide a classification of customers in two groups according to their waiting time. Recode the WaitTime variable into two groups and assign relevant labels:

• 1 = Short wait: less than 30.97
• 2 = Long wait: 31 and higher

Provide a table showing summary statistics for the new variable. Briefly discuss the results.

Provide a classification of customers in three groups based on spend. Recode the spend variable in three groups and assign relevant labels:

• 1 = Low spend: less than 66
• 2 = Normal spend: 66 to 100
• 3 = High spend: 100 and higher

Provide a table showing summary statistics for the new variable. Briefly discuss the results.

Based on the classifications you have just developed, please answer the following:

• Does the mean ambience rating of customers increase as wait time increases? Explain briefly, providing relevant tables.
• Do customers who dine-in have higher mean satisfaction than customers who takeaway? Explain briefly, providing relevant tables. o Do male customers have a higher mean satisfaction than female customers? Explain briefly, providing relevant tables.

Create relevant graphs to answer the following questions:

• How does food rating of customers compare across different wait time levels (using the newly recoded wait time variable) and preferred cuisine? Discuss your results. What conclusions can you derive from the visualisation?
• How does service rating compare across dining occassions and genders? Discuss your results. What conclusions can you derive from the visualisation?

Exploratory Analysis

1. Inspect the dataset graphically, such as checking the distribution of all variables, checking the possibility of outliers, and pre-checking the relationship/association between the dependent and independent variables. The details and types of graphs are your decision - the objective is to provide a concise yet informative inspection of the data before running the regression. You may select any graphs that we have produced in the labs, which efficiently describe various aspects of the data. Make sure to provide adequate discussion and explanation.
2. Evaluate the skewness and kurtosis of any relevant variables in the dataset. Compute descriptive statistics (showing mean, standard deviation, min, max). Produce relevant graphs. Discuss your results.
3. Estimate the correlation between satisfaction and service rating. Produce a relevant graph to inspect the relationship visually. What is the strength and direction of the relationship? Discuss your results.

Inferential Statistics:

1. Apply a statistical test and evaluate if there are any significant differences across visit frequency regarding food rating. If there are significant differences, evaluate which groups differ significantly with each other. Justify the selection of the statistical test(s). Discuss your results.
2. Apply a statistical test and evaluate if there are any significant differences between genders regarding service rating. You may use graphical illustration to enhance the interpretation of your results. Justify the selection of the statistical test. Discuss your results.
3. Apply a statistical test to evaluate whether there are any significant differences across time of visit levels regarding satisfaction. Justify the selection of the statistical test. Produce a boxplot graph and explain the results. Discuss your overall results.

Regression Analysis:

1. Conduct a simple regression to estimate the effect of food rating on satisfaction. Carefully interpret and discuss the results (e.g., R-squared, the statistical significance of coefficients and the effect size of the independent variables).
2. Conduct a multiple regression to estimate the effect of food rating, service rating, spend, wait time and income on satisfaction. This is the baseline model. Carefully interpret and discuss the results (e.g., R-squared, the statistical significance of coefficients and the effect size of independent variables). Compare your results with the previous model you produced (using relevant goodness of fit indices to compare the two models).
3. Apply diagnostic analyses on the baseline model to check for potential multicollinearity and suggest potential appropriate remedies that could be applied if needed. Briefly discuss the results. 
4. Apply diagnostic analyses on the baseline model to check for potential heteroscedasticity. Briefly discuss your results.
5. Overall, what other variables that are included in the dataset but not in the baseline model, may be affecting customers’ satisfaction at a restaurant? As an analyst, what additional data would you collect in order to improve the prediction of customers’ satisfaction at a restaurant and how would you do it? Why is customer satisfaction an important outcome for a hospitality business? Discuss briefly.

Appendix

Include your IBM SPSS Statistics output results in your submission. You need to upload the

output file (spv)

Please do not copy the output results as screenshots in the report.

Please make sure that the output file you will submit includes only the correct answers to the questions given to you.

Format

• The project file should be in Microsoft word format in Times New Roman 12-point font double spaced. The word count of the project should be no more than 3500 words.

• The word count includes everything from the first word of the introduction to the last word of the conclusion. The word count does not include tables, figures or images, and appendices. It does not include abstract, table of contents, abbreviation pages, or references (though these are not mandatory in this project). You should report the word count, your name and student number at the beginning of your project. According to the university policy, exceeding the word count limit is subject to a 10- point penalty.

Guidelines

• Apply the analyses required as explained orderly, section by section (from Introduction to Descriptives and Regression Analysis).
• The report should stand as a self-sufficient and stand-alone document for readers, who do not have access to the project description. Thus, the report, including the writing, explanations, tables and graphs, should be clear and informative.
• In the introduction, you need to explain the aim of this empirical report, the sample and data, providing the definition of all variables incorporated in the dataset. Some of this information (such as sample and variables definition) has been provided to you, but you need to summarise them in your report concisely.
• All tables and graphs should be numbered and titled (with captions if an additional explanation is required) and should be referred to in the report accordingly. The label of the variables in tables and graphs should be informative.
• Graphs should be visually clear (axis title, colour, legend, axis scale, etc.). You can use image format for your graphs. Please try not to populate the report with lots of graphs; be selective and use the most informative ones for your purposes.
• Tables can be exported from the statistical software to a Word format or image format.
• In the regression tables, coefficients should be reported along with the significance level of the coefficient. The R-squared and number of observations for each model should be reported too.
• The output SPSS results for all tables, graphs, and regressions etc should be provided in a clear, and readable format as a separate spv file.
• You don’t need to cite any reference but use a proper citation style and provide the reference list in the appendix if you intend to do so.
• Overall, the project’s quality (i.e., clarity, rigour, precision, and depth) is more important than the length.