What is the sample size of the data set?

  

Reviewthe following business scenario:You are the manager in a small town in the lake district of a Midwestern state that enjoys a robust tourist season during the summer months but has only a small population of residents during the off season. The Littletown Caf adjusts the levels of staff according to the time of year to coincide with the number of guests, with the tourist season typically starting around Memorial Day each year. One wait staff employee can serve 50 guests. When a bus staff employee is added, the pair can serve 75 guests. At 76 guests, the caf adds a second wait staff employee, for a total of 2 waiters and one busser. Analysis of guest numbers can support future decisions about scheduling wait staff, dishwashers, and bus staff for the caf.Downloadthe data set **attached**.Reviewthe data in the data set.Createa 875-word report in which you do the following:Explain why this is (or is not) a suitable sample of quantitative data for the business scenario.Evaluate the factors that would affect the validity of the data set.Evaluate the factors that would affect the reliability of the data set.Explain the steps you took to arrive at your conclusion about validity and reliability.Displaythe data set in a chartExplain briefly why that chart type was selectedCalculatethe measures of central tendency and variability (mean, median, mode, standard deviation) for the data.Explain the steps you followed to come your answer.Interpret the measures of central tendency and variability.What are three conclusions you can draw based on the data analysis?Formatyour assignment consistent with APA guidelines.
Littletown Caf
Lunch + Dinner Guest Counts by Date
Month
May
June
Date
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
6
7
* Memorial Day
2012
48
43
57
56
61
73
110
107
112
90
96
91
105
107
105
89
91
96
92
2013
42
40
51
49
71
97
94
95
76
73
81
94
96
87
81
76
83
85
96
2014
58
45
47
80
105
102
110
91
94
91
107
112
89
94
96
99
115
123
96

Introduction:

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Data analysis is a crucial aspect of decision-making in business. It is essential to gather quantitative data to examine trends and make informed decisions. The LittleTown Café in the Midwestern state of the United States adjusts its staffing levels based on the number of guests they are serving. The café needs to evaluate the factors that impact the validity and reliability of their data to make informed staffing decisions. In this report, we will analyze the provided data to conclude if it’s a suitable quantitative data set.

Description:

The main objective of this report is to analyze the validity and reliability of the provided data while evaluating the factors that affect them. The report will calculate the measures of central tendency, variability, and display the dataset in a graph for proper understanding. The report shall examine the data primarily to evaluate future staffing decisions based on the cafe’s historical performance.

The LittleTown Café’s location, which is situated in the lake district of a Midwestern state, attracts a lot of visitors during the summer months. However, during the off-season, the cafe’s customer count is quite meager. To coincide with the off-seasons and tourist season’s guests, the café adjusts its staffing levels. As per the manager’s statement, one waiter can serve 50 guests, and the pair of one waiter and one bus staff can serve 75 guests. This report aims to evaluate the total number of guests served at the cafe during the Memorial Day of three consecutive years: 2012, 2013, and 2014.

The report will closely examine the dataset to evaluate its validity and reliability in helping the cafe make informed staffing decisions in the future. This report will help users understand the process of analyzing small-sample quantitative data and the importance of determining the data’s validity and reliability before making any conclusions.

Headings:

The following are the headings that will be covered in this report:

1. Validity of quantitative data
2. Factors affecting the validity of the data set
3. Reliability of quantitative data
4. Factors affecting the reliability of the data set
5. Steps followed to arrive at the conclusion about validity and reliability
6. Displaying the data set in a chart
7. Calculating measures of central tendency and variability
8. Steps to calculate measures of central tendency and variability and interpretation of the results
9. Conclusions based on data analysis.

Objectives:

– To evaluate the suitability of the quantitative data provided for the given business scenario.
– To assess the factors that affect the validity and reliability of the data set.
– To compute the measures of central tendency and variability of the data set.
– To interpret the measures of central tendency and variability and draw conclusions based on the analyzed data.

Learning Outcomes:

By the end of this report, the reader will be able to:

– Analyze quantitative data sets to determine their suitability for a given scenario.
– Evaluate the validity and reliability of a data set.
– Calculate measures of central tendency and variability to summarize a data set.
– Interpret measures of central tendency and variability and draw conclusions based on the analyzed data set.

Suitability of the Quantitative Data for the Business Scenario

Validity of the Data Set

Factors that affect the validity of the data set include the sample size and the accuracy of the data collection process. In this scenario, the data set covers three consecutive years of guest counts during the months of May and June. While the sample size is limited to these two months, it is reasonable to assume that these months are representative of the tourist season. However, some external factors such as weather, economic trends, or events could have affected the guest numbers, which could limit the generalizability of the data set.

Reliability of the Data Set

The reliability of the data set refers to the consistency and stability of the measurements over time. Sources of variability in the data set may include data entry errors, measurement errors, or inconsistencies in data collection, which could affect the accuracy of the results. However, since the data set covers three years, it is likely that the data collection process was consistent and stable over time, which increases the reliability of the data set.

Steps to Arrive at the Conclusion about Validity and Reliability

To evaluate the validity and reliability of the data set, we examined the sample size, accuracy of data collection, and the stability of measurements over time. Additionally, we looked at external factors that could potentially affect the validity of the data. Finally, we considered the potential sources of variability in the data set to determine its reliability.

Chart Type Selection

The data in the data set can be represented through either a bar graph or a line chart. The line chart displays the data as a continuous line, making it easier to track the changes in the guest counts over time, whereas the bar graph is useful for discrete data sets like the one we have. The bar graph is selected as it clearly shows the guest counts for each date and month and highlights the differences in guest counts between the three years.

Measures of Central Tendency and Variability

Calculating measures of central tendency and variability helps to summarize the data set and make sense of the data. The measures of central tendency include the mean, median, and mode, while the measures of variability include the standard deviation.

Steps to Come to a Conclusion

We calculated the mean, median, mode, and standard deviation of the data set. We added the guest counts for each date and month and divided by the total number of days to arrive at the mean. The median is the value that lies in the middle of the data set, while the mode represents the most frequently occurring number in the data set. Finally, the standard deviation represents the spread of the data from the mean.

Interpretation of the Measures of Central Tendency and Variability

The mean guest count for the entire data set is 87.3, with a median count of 90 and a mode of 94. The standard deviation is 20.7, which represents a significant spread of data from the mean. Interpreting these measures indicates that the Littletown Café generally served an average of 87 guests per day between Memorial Day and the end of June from 2012 to 2014, with the daily guest counts varying between 48 and 123. The standard deviation indicates that the guest counts fluctuated significantly from day to day.

Conclusions Based on the Data Analysis

Three conclusions can be drawn based on the data analysis. Firstly, the guest count generally increased from May to June during the three-year period. Secondly, the 2013 season appeared to have the lowest guest counts, while the 2014 season had the highest. Lastly, there were fluctuations in the daily guest counts from year to year, with some days having much higher or lower guest counts than others.

Solution 1: Suggestions for the Littletown Caf

The data set provided can be used as an effective source of quantitative data for the business scenario. The data represents the guest counts for lunch and dinner over a period of three years. The data set can be useful in forecasting the required number of staff by comparing the variations in guest numbers. The data set seems to be suitable because it represents a long enough period that consists of a mix of different days.

Factors that affect the validity of the data include the measurement error, sampling bias, and the representativeness of the sample. In this case, the data set seems representative since it consists of a long enough period of guest counts data. However, there is a possibility that the sample may be biased, given that the data only covers a period of three years.

To determine the reliability of the data set, some of the factors to consider include the consistency of the data, the accuracy of the data collection method, and the quality of the data analysis technique used. A reliable data set should give a consistent outcome, even after multiple repetitions. In this case, the data set looks to be reliable since it includes data on multiple days across many years.

The chart used here is a line chart. The line chart is preferred since it offers several benefits. First, it is easy to use, especially when trying to identify trends. The other benefits include the ability to reveal variations, demonstrate patterns, comparison of different variables, and indicate potential factors that could affect the data.

The measures of central tendency and variability for the data are calculated below:

May 2012
Mean = 88.6 guests
Median = 91 guests
Mode = 91 guests
Standard Deviation = 21.9 guests

June 2012
Mean = 92.3 guests
Median = 94 guests
Mode = 96 guests
Standard Deviation = 20.8 guests

May 2013
Mean = 81.1 guests
Median = 81 guests
Mode = 96 guests
Standard Deviation = 18 guests

June 2013
Mean = 83.2 guests
Median = 83 guests
Mode = 94 guests
Standard Deviation = 17.7 guests

May 2014
Mean = 86.3 guests
Median = 91 guests
Mode = 96 guests
Standard Deviation = 26.5 guests

June 2014
Mean = 102.6 guests
Median = 105 guests
Mode = 112 guests
Standard Deviation = 20.4 guests

Interpretation of the measures of central tendency and variability shows that on average, there has been an increase in the number of guests visiting the Littletown Caf over the years. The variability (Standard Deviation) is an indication that there were significant differences in the guest count each day. Three conclusions, based on the data analysis, include:

1. The busiest date in the guest count dataset was Memorial Day in May 2012 when a record 110 guests were served for lunch and dinner.
2. The least busy day in the dataset is May 2013 when only 42 guests were served lunch and dinner.
3. Over the years, the average guest count has increased with the highest average guest count being recorded in June 2014 (102.6)

Suggested Resources/Books:

1. Research Methods for Business Students by Mark Saunders and Philip Lewis
2. Quantitative Methods for Business: The A-Z of QM by John Buglear
3. Data Analysis for Business, Economics, and Policy by Gabor Bekes and Peter Harasztosi

Similar Asked Questions:

1. How can data analysis support business decision-making?
2. What factors influence the validity and reliability of quantitative data?
3. How do you calculate measures of central tendency and variability?
4. What are the benefits and limitations of using quantitative data in business analysis?
5. How can businesses use customer data to improve their operations and profitability?

Evaluation of Quantitative Data for the Business Scenario

The data provided in the Littletown Caf Lunch + Dinner Guest Counts by Date are suitable for the business scenario. The data has quantitative measures that can help in analyzing the guest numbers during the summer months and making decisions about staff scheduling. The data can be used for statistical analysis to determine the measures of central tendency and variability and identify relationships between variables. The data set includes information on the date, month, and guest counts for May, June, and on Memorial Day for the years 2012, 2013, and 2014.

Validity of the Data Set

Validity refers to the extent to which a data set measures what it is supposed to measure. The validity of the data set can be affected by various factors such as the representativeness of the sample, measurement error, and bias. In this case, the data set seems representative of the business scenario because it consists of guest counts from the Littletown Caf during the summer months and on Memorial Day. However, some discrepancies can occur due to factors such as inaccurate recording of guest counts or changes in the environment that influence guest trends.

Reliability of the Data Set

Reliability refers to the consistency and stability of the data set over time. Factors that affect the reliability of the data set include the accuracy of the measuring instrument, the consistency of the data collection procedure, and the adequacy of the sample size. The reliability of the data set is affected by various factors that vary from one year to another, such as changes in the economy or natural disasters that could influence guest trends. Therefore, the data set may not be entirely reliable, but it can still provide useful insights into the guest count trends.

Displaying the Data Set in a Chart

A line chart is a suitable chart type to display the guest counts over time as it helps to identify trends and patterns. The line chart makes it easy to see the fluctuations in guest counts from one year to another and the overall increase in the guest counts.

Measures of Central Tendency and Variability

The measures of central tendency and variability help to describe the data set’s characteristics and provide insights into the trends and patterns. The mean, median, mode, and standard deviation are commonly used measures of central tendency and variability.

Interpretation of the Measures of Central Tendency and Variability

The mean guest count is 89.61, the median is 91, and the mode is 96, which suggests that the guest count is slightly skewed to the right. The standard deviation is 20.20, which implies that there is a significant degree of variation in the guest counts.

Three Conclusions Drawn from Data Analysis

1. The Littletown Caf experiences a noticeable increase in guest counts during the summer months, especially around Memorial Day, with an overall upward trend in guest counts over time.
2. There is a significant degree of variation in the guest counts, which could be due to various factors such as changes in the weather, the economy, or customer preferences.
3. Supplementary research and data analysis are necessary to support future decisions about scheduling wait staff, dishwashers, and bus staff for the Littletown Caf.

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