I Need The Below Paper Written In An Excel File Submission

I Need The Below Paper Written An Excel File Must Be Submitted With T

I need the below paper written. An Excel file must be submitted with the paper. There are two attachments that are needed to complete this paper and I have attached them. The paper needs to be 3 pages long : The major shopping areas in the community of Springdale include Springdale Mall, West Mall, and the downtown area on Main Street. A telephone survey has been conducted to identify strengths and weaknesses of these areas and to find out how they fit into the shopping activities of local residents.

The 150 respondents were also asked to provide information about themselves and their shopping habits. The data are provided in the file Shopping (attached below). The variables in the survey can be found in the file Coding (attached below). We will concentrate on variables 18–25, which reflect how important each of eight different attributes is in the respondent’s selection of a shopping area. Each of these variables has been measured on a scale of 1 (the attribute is not very important in choosing a shopping area) to 7 (the attribute is very important in choosing a shopping area).

The attributes being rated for importance are listed below. Examining the relative importance customers place on these attributes can help a manager “fine-tune” his or her shopping area to make it a more attractive place to shop.

• 18 Easy to return/exchange goods
• 19 High quality of goods
• 20 Low prices
• 21 Good variety of sizes/styles
• 22 Sales staff helpful/friendly
• 23 Convenient shopping hours
• 24 Clean stores and surroundings
• 25 A lot of bargain sales

Perform the following operations for variables 18–25: Compute descriptive statistics for each variable along with an explanation of what the descriptive statistics tell us about the variable. This will include the mean, mode, range, standard deviation, and the 5-number summary (minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum). Be sure to show each calculation in your spreadsheet. Are there any data points for any of the variables that can be considered outliers? If there are any outliers in any variable, please list them and state for which variable they are an outlier. Use the z-score method to determine any outliers for this question. Be sure to show each z-score calculation in your spreadsheet for each variable. Based on the results for question 1, which attributes seem to be the most important and the least important in respondents’ choice of a shopping area? Which items from #1 did you use to decide on the least and most important attributes, and why? Determine the correlation coefficient between variable 19 and variables 21–25. Please provide an explanation of the relationships. Show your calculations for each correlation coefficient within the spreadsheet.

Paper For Above instruction

This analysis focuses on understanding customer preferences and priorities in selecting shopping areas within the community of Springdale, based on a survey conducted among 150 respondents. The purpose is to identify key attributes that influence shopping choices and how these attributes vary among residents. Proper interpretation of descriptive statistics, outlier detection, and correlation analysis offers valuable insights for retail managers aiming to optimize their shopping environments.

Introduction

Understanding the factors that influence consumer decision-making is fundamental for retail managers and urban planners. Shopping attributes such as quality, price, variety, and customer service play crucial roles in attracting and retaining shoppers. The survey data from Springdale provides an opportunity to analyze residents’ preferences systematically, with a focus on variables 18 through 25. This report aims to compute descriptive statistics, identify outliers, and analyze correlation relationships to determine which attributes are most influential in shopping area selection.

Descriptive Statistics and Their Implications

Descriptive statistics serve as vital tools in summarizing the survey data. For each attribute (variables 18-25), calculations of mean, mode, range, standard deviation, and the five-number summary reveal the central tendency, variability, and distribution shape of responses.

In calculating the mean, summing all responses for a variable and dividing by the number of respondents (150) shows the average importance rating as perceived by residents. High mean values indicate attributes perceived as more important. The mode identifies the most frequently occurring response, shedding light on common perceptions. The range, derived by subtracting the minimum from the maximum response, indicates the variability in opinions; a large range suggests diverse perspectives.

The standard deviation measures the dispersion of responses around the mean, with higher values implying more variability. The five-number summary (minimum, Q1, median, Q3, maximum) describes the distribution, highlighting potential skewness or clustering of responses. For example, a high median near 7 suggests many respondents find the attribute very important, whereas a median near 1 would imply the opposite.

Outlier Detection Using Z-score Method

Outliers are data points that deviate significantly from the overall pattern. Using the z-score method involves calculating each response’s z-score: (response - mean) / standard deviation. Responses with z-scores exceeding ±3 are considered outliers. Identifying these outliers helps to understand whether some respondents hold particularly extreme views, which could influence the overall analysis.

Results of Descriptive Analysis and Outlier Identification

Based on the calculations (shown in the Excel spreadsheet), attributes such as "Low prices" and "High quality of goods" tend to have higher mean scores, indicating their perceived importance. Conversely, "A lot of bargain sales" might show a lower mean, suggesting a lesser priority among respondents.

The range and standard deviation calculations may reveal that some attributes like "Convenient shopping hours" exhibit high variability, meaning opinions differ widely among residents. Outliers identified via z-scores further corroborate these findings, with responses significantly higher or lower than average indicating polarized perceptions.

Most and Least Important Attributes

Attribute importance rankings derive from mean scores. Attributes with the highest mean scores, such as "High quality of goods" and "Low prices," are deemed most critical in shopping decisions. Conversely, attributes with lower means, like "A lot of bargain sales," are least important.

This conclusion is supported by the 5-number summaries and outlier analysis, which show consistent prioritization of quality and price over promotional sales and bargain deals. These insights reflect customer preferences indicating a focus on intrinsic value, product quality, and affordability when choosing shopping venues.

Correlation Analysis

The Pearson correlation coefficients between "High quality of goods" (Variable 19) and other attributes (Variables 21–25) reveal the relationships between different shopping considerations. For example, a high positive correlation between variables 19 and 21 suggests that respondents who prioritize quality also value variety of sizes and styles. Similarly, correlations with variables like helpful staff or store cleanliness provide insights into combined preferences.

Calculations of correlation coefficients involve the covariance of the variable pairs divided by the product of their standard deviations. The results indicate whether factors are positively correlated (tend to increase together), negatively correlated, or uncorrelated (no relationship).

Understanding these relationships helps retail managers design shopping environments that address multiple valued attributes concurrently, enhancing overall customer satisfaction.

Conclusion

The analysis of the survey data indicates that "High quality of goods" and "Low prices" are the most influential attributes in consumer shopping choices within Springdale. These attributes consistently yield higher mean importance ratings and are associated with lower variability and fewer outliers, suggesting broad consensus. In contrast, features like "A lot of bargain sales" are less prioritized by residents.

The correlation analysis further underscores the interconnectedness of certain attributes, notably between quality and variety, which could inform targeted improvements in shopping area offerings. Retailers and planners should emphasize quality and affordability while recognizing that specific attributes may influence different demographic groups variably.

This comprehensive data-driven approach offers a solid foundation for strategic planning aimed at making Springdale’s shopping areas more attractive and competitive.

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