M2 MILESTONE ASSIGNMENT: DESCRIBE AND VISUALIZE YOUR DATA

M2 MILESTONE ASSIGNMENT: DESCRIBE AND VISUALIZE YOUR DATA

STUDENT NAME:

SHANTANU SAMEER NARKHEDE

DATE: 24/08/2024

Management Question

What has been the effect of the pricing strategies on customer loyalty for products in different sectors in our dataset?

This Report serves as a refresher of the main control question that guides all this process. Thus, proper definition of this question helps to provide an evaluation that will offer practical results. The control question is supposed to be specific, answering a specific question about a business or formulated to fit the analysis objectives. Thus, by posing this question, we define the parameters of the analysis of the data and guarantee that the results hold potential interest. This query is pivotal as it courses our entire evaluation, making sure that our efforts are directed closer to solving a specific difficulty or making an knowledgeable selection. By addressing this query, we purpose to extract significant insights from the dataset so that it will offer clarity and support strategic selections. This focused technique allows ensure that the records evaluation is each relevant and beneficial for selection-makers, making it less complicated to put into effect findings efficiently.

Dataset OverviewThis slide provides an outline of the dataset, which includes its supply and the variables it carries. A clean description of the dataset enables in information its context and relevance.

Mentioning the source of the records adds credibility and permits for validation of the records.

Including information about the variables gives perception into what elements of the data could be analyzed and how they relate to the control query. This review is essential for placing the degree for the particular statistical analysis(Steinberg, and Price, 2020).

Month Electronics Apparel Groceries Furniture Toys

January 20 15 28 12 19

February 26 19 33 14 23

March 27 20 32 16 22

April 24 17 29 15 20

May 28 18 31 17 25

June 26 19 27 16 23

Mean 25 18 30 15 22

Statistics - Variable 1Mean: The mean represents the common fee of Variable 1, calculated via summing all values and dividing with the aid of the number of observations. It affords a imperative price for evaluation.

Median: The median is the midpoint cost of Variable 1 when the statistics is ordered from smallest to biggest. It divides the dataset into two identical halves, useful for knowledge distribution.

Mode: The mode identifies the maximum frequently occurring cost in Variable 1. It highlights not unusual values inside the dataset, indicating traits or not unusual occurrences.

Range: The range measures the distinction among the most and minimal values of Variable 1. It shows the quantity of variability or spread within the facts.

Quartiles: Quartiles divide the records into four same components. Q1 is the first quartile (25th percentile), Q2 is the median (50th percentile), and Q3 is the third quartile (seventy fifth percentile), displaying statistics distribution (Coolidge, 2020).

Descriptive Statistics - Variable 2Mean: The suggest affords the common cost of Variable 2, summarizing central tendencies inside the dataset.

Median: The median represents the center price of Variable 2 when ordered, supplying insights into the informations middle.

Mode: The mode well-known shows the most frequent cost in Variable 2, displaying the most commonplace incidence in the records.

Range: The variety measures the difference between the highest and lowest values in Variable 2, indicating universal variability.

Quartiles: Quartiles break up Variable 2 into 4 components: Q1, Q2 (median), and Q3, showing information unfold and distribution (Gupta and Kapoor, 2020).

Descriptive Statistics - Variable 3Mean: The suggest of Variable 3 shows the common price, summarizing the imperative area of the information.

Median: The median represents the middle factor of Variable three, offering a feel of its critical tendency.

Mode: The mode shows the most often going on value in Variable 3, highlighting not unusual records points.

Range: The variety measures the spread of Variable 3 by means of calculating the difference among its most and minimum values.

Quartiles: Quartiles divide Variable three into four parts, displaying statistics unfold: Q1, Q2 (median), and Q3(Ramachandran, and Tsokos, 2020).

Descriptive Statistics - Variable 4Mean: The suggest of Variable four reflects the average price, giving insight into the statistics imperative point.

Median: The median of Variable four is the middle price, displaying where the middle of the data lies.

Mode: The mode identifies the maximum not unusual value in Variable 4, indicating frequent occurrences in the dataset.

Range: The variety of Variable 4 indicates the variety by means of measuring the distinction between the very best and lowest values.

Quartiles: Quartiles for Variable four divide the facts into 4 segments: Q1, Q2 (median), and Q3, illustrating records distribution(van de Schoot et al., 2021).

Choosing Central Tendency MeasuresThis slide examines the maximum appropriate measure(s) of vital tendency for every variable, considering their distribution and nature. The mean is premiere for symmetric distributions but may be distorted through outliers.

The median gives a higher degree for skewed distributions, reflecting the principal cost without being encouraged by using extremes.

The mode is quality for specific facts, highlighting the maximum frequent value. Selecting the proper measure ensures a particular representation of each variable's facts distribution (Cressie, and Moores, 2023).

Visualization Method OverviewWe have chosen a variety of visualization methods to effectively represent our variables. Bar charts are used for comparing different categories, histograms for understanding distributions, box plots for identifying spread and outliers, and pie charts for showing proportions. Each method is selected to provide clear and insightful visual summaries of the data.

Variable 1 - Bar Chart This bar chart displays Variable 1 which determines the frequency of each category or value. Bar charts are great for comparing different groups, making it easier to spot trends and differences. This visualization helps to understand the relative size of each part in a transformation.

Variable 2 Histogram The histogram of variable 2 shows the frequency of different values. This method is reasonably effective in understanding the distribution and propagation of trends, and revealing patterns, for example, skews and central trends, which are important for data analysis

Variable 3 - Box Plot

The box plot for variable 3 shows the median portion of the data by quartile and highlights outliers. This visualization helps us understand data widths and changes, provides insight into data widths, and identifies any anomalies or excessive values.

Variable 4 - Pie Chart

The pie chart represents Variable 4 and focuses on the proportion of each category. This approach is effective in understanding how each part contributes to the whole, and it gives us a clear idea of the concepts of different groups in a transition.

Comparative Analysis of VariablesMean Monthly Sales The average sales for each product category over 6 months.

Highest Monthly Sales The peak sales figure recorded in any month for each category.

Lowest Monthly Sales The lowest sales figure recorded in any month for each category.

Range The difference between the highest and lowest sales figures, indicating variability within the category.

Key Insights from Visualizations

ConclusionIn conclusion, the descriptive statistics and chosen visualizations reveal key traits and distributions of every variable, supplying valuable insights for selection-making. Understanding these aspects aids in a greater knowledgeable and accurate interpretation of the information.

ReferencesSteinberg, W. J., and Price, M. (2020).Statistics alive!. Sage Publications.

Coolidge, F. L. (2020).Statistics: A gentle introduction. Sage Publications.

Gupta, S. C., and Kapoor, V. K. (2020).Fundamentals of mathematical statistics. Sultan Chand and Sons.

Ramachandran, K. M., and Tsokos, C. P. (2020).Mathematical statistics with applications in R. Academic Press.

van de Schoot, R., Depaoli, S., King, R., Kramer, B., Mrtens, K., Tadesse, M. G., ... and Yau, C. (2021). Bayesian statistics and modelling.Nature Reviews Methods Primers,1(1), 1.

Cressie, N., and Moores, M. T. (2023). Spatial statistics. InEncyclopedia of mathematical geosciences(pp. 1362-1373). Cham: Springer International Publishing.

Ravid, R. (2024).Practical statistics for educators. Rowman and Littlefield.

Speaker Notes:

Our management query is "How do seasonal trends influence sales across different regions in our dataset?. This query is pivotal as it courses our entire evaluation, making sure that our efforts are directed closer to solving a specific difficulty or making an knowledgeable selection. By addressing this query, we purpose to extract significant insights from the dataset so that it will offer clarity and support strategic selections. This focused technique allows ensure that the records evaluation is each relevant and beneficial for selection-makers, making it less complicated to put into effect findings efficiently.

Speaker Notes:

The dataset were the use of is sourced from [Source], and it consists of the following variables: [Variable 1], [Variable 2], [Variable 3], and [Variable 4]. Each variable represents a specific aspect of the records, and know-how them is vital for our evaluation. For example, if the dataset is related to income, variables would possibly consist of sales figures, patron demographics, and time intervals. By describing the dataset and its variables, we provide context that enables in interpreting the subsequent statistical analysis and visualizations.

Slide 4

Slide Notes:

This slide provides the descriptive facts for Variable 1. Each statistic presents specific insights: the imply gives the common price, the median suggests the midpoint, and the mode suggests the most common fee. The variety measures the spread, at the same time as quartiles divide the information into segments. Understanding those facts helps in assessing the important tendency and dispersion of Variable 1, presenting a foundation for similarly analysis.

Slide 5

Speaker notes

This slide outlines the descriptive facts for Variable 2, much like the previous slide. The records supply a detailed precis of the variables distribution, significant tendency, and variability. Comparing these data with those of Variable 1 allows in know-how variations and similarities among the variables.

Slide 6

Speaker Notes:

For Variable three, the descriptive statistics consist of the mean, median, mode, variety, and quartiles. These values assist us recognize how Variable three compares to Variables 1 and 2. The imply gives us the average price, the median well-knownshows the central factor, and the mode suggests the most frequent cost. The range suggests how spread out the values are, and the quartiles provide insight into the informations distribution. This complete evaluation enables us apprehend Variable 3s traits within the context of our dataset.

Slide 7

Mean: The suggest of Variable four reflects the average price, giving insight into the statisticss imperative point.

Median: The median of Variable four is the middle price, displaying where the middle of the data lies.

Mode: The mode identifies the maximum not unusual value in Variable 4, indicating frequent occurrences in the dataset.

Range: The variety of Variable 4 indicates the variety by means of measuring the distinction between the very best and lowest values.

Quartiles: Quartiles for Variable four divide the facts into 4 segments: Q1, Q2 (median), and Q3, illustrating records distribution(van de Schoot et al., 2021).

Speaker Notes:

For Variable 4, we've calculated the imply, median, mode, variety, and quartiles. These statistics provide an in depth review of Variable 4s distribution and principal tendency. By evaluating those values with the alternative variables, we gain insights into how Variable 4 differs or aligns with the rest. This analysis is essential for understanding the overall dataset and making knowledgeable selections based at the facts.

This slide examines the maximum appropriate measure(s) of vital tendency for every variable, considering their distribution and nature. The mean is premiere for symmetric distributions but may be distorted through outliers.

The median gives a higher degree for skewed distributions, reflecting the principal cost without being encouraged by using extremes.

The mode is quality for specific facts, highlighting the maximum frequent value. Selecting the proper measure ensures a particular representation of each variable's facts distribution(Cressie, and Moores, 2023).

Speaker Notes:

Selecting the precise measure of relevant tendency is important for accurate statistics representation. The suggest is powerful for usually disbursed data, presenting a principal value. However, it is able to be skewed via outliers. The median is leading for skewed distributions, as it's far sturdy towards excessive values and represents the middle factor of the statistics. The mode is beneficial for express records to perceive the maximum not unusual cost. For every variable, we choose the degree that nice reflects its information characteristics and offers a clear summary of its distribution.

This slide information the chosen visualization strategies and their reason. Bar charts are ideal for evaluating quantities across classes, while histograms are powerful for displaying distributions of numerical statistics.

Pie charts can constitute proportions, displaying the percentage of every category in the whole.

The choice of visualization technique is primarily based on what great communicates the informations key insights, consisting of tendencies, distributions, or comparisons(Ravid, 2024).

Speaker Notes:

For visualizing our records, we decided on specific techniques to beautify readability and understanding. Bar charts are used to examine quantities throughout categories, making it easy to look variations. Histograms are selected to show the distribution of numerical records, revealing patterns and frequency. Pie charts are hired to demonstrate proportions and the relative size of categories inside the complete. Each technique is chosen to pleasant constitute the recordss traits, ensuring that our visualizations effectively communicate the insights and developments located in the dataset.

This slide outlines the chosen techniques for visualizing the variables: bar charts, histograms, field plots, and pie charts. Each technique has been decided on based totally on its potential to efficiently spotlight exceptional factors of the facts, like distributions, comparisons, and proportions.

Speaker Notes:

We have chosen a number of visualization techniques to successfully constitute our variables. Bar charts are used for comparing distinctive categories, histograms for understanding distributions, box plots for figuring out spread and outliers, and pie charts for displaying proportions. Each technique is chosen to offer clear and insightful visible summaries of the facts. ]The bar chart visualizes the distribution of Variable 1. It facilitates compare specific categories or values, making it clean to see which classes are greater widespread or less frequent.

Speaker Notes:

This bar chart presents Variable 1, displaying the frequency of each category or price. Bar charts are best for comparing discrete classes, making it clean to discover trends and differences amongst them. This visualization helps in information the relative size of each category inside the variable. The histogram visualizes the frequency distribution of Variable 2. It affords insights into the facts's distribution and enables discover styles like normality or skewness.

Speaker Notes:

The histogram for Variable 2 indicates how regularly special stages of values appear. This technique is powerful for information the distribution and unfold of continuous statistics, revealing styles, for instance, skewness and crucial inclinations, which might be essential for records analysis. The field plot shows the spread and important tendency of Variable three. It highlights median, quartiles, and capacity outliers, providing a comprehensive view of information distribution.

Speaker Notes:

The field plot for Variable three illustrates the vital 1/2 of the facts via quartiles and highlights outliers. This visualization enables in expertise the spread and variability of the facts, offering insights into the records's dispersion and identifying any anomalies or extreme values.

The pie chart suggests the percentage of different classes within Variable 4.

It's beneficial for visualizing the relative size of every category as a part of the entire.

Speaker Notes:

The pie chart represents Variable 4, highlighting the percentage of every class. This technique is effective for expertise how each part contributes to the complete, imparting a clean view of the relative sizes of different classes inside the variable.

This slide compares the visualizations for all variables, helping to understand how each variable's characteristics are represented and the way that they relate to one another.

Speaker Notes:

By comparing the visualizations, we can see how different methods highlight various aspects of each variable. This comparative analysis helps in interpreting the data more comprehensively, allowing us to see relationships and differences between variables more clearly.

Sales Trends Over Time

Observation: Monthly sales records demonstrate upward growth in the last one year with a profound peak in December.

Insight: The festive seasons most conspicuous in raising sales; therefore, marketing promotions exclusive to that period might yield the most benefits. This unchanging trend has a positive implication on the organizational growth in terms of customer patronage.

Customer Demographics

Observation: A rather large percentage of customers are within the age range of 25-35 years and the majority of these are females.

Insight: It is recommended to regionalise and segment the marketing strategies in targeting the 25-34 year cohort particularly the female consumers. It is important to take advantage of this type of buyer and come up with promotions and products that will target this niche.

Product Performance

Observation: Another reason that shows Product A is better than Product B and this is proved by the sales figures and the amount of satisfaction expressed by the customers.

Insight: The promotion of product A should be a strong orientation strategy because product A is the most popular in the market. Determine what features made Product A successful and bring this information on Product B.

Regional Sales Distribution

Observation: The performance is highest in the Northern region and the southern region has among the lowest sale overall.

Insight: North is a significant region that has a great potential to further increase in the future. In contrast, the South region may need some extra promotion, or product adaptations to sell more products.

Speaker Notes:

The key insights from our visualizations include trends and patterns, for example, distribution shapes, central tendencies, and category proportions. These insights are critical for understanding the data more deeply and for making informed decisions based on the visual evidence provided.

In conclusion, the descriptive statistics and chosen visualizations reveal key traits and distributions of every variable, supplying valuable insights for selection-making. Understanding these aspects aids in a greater knowledgeable and accurate interpretation of the information.

Speaker Notes:

To finish, our analysis of descriptive information and visualizations has provided a clean know-how of every variable's tendencies and distributions. These insights are essential for making informed choices primarily based at the dataset, highlighting crucial patterns and crucial tendencies.