Introduction
Welcome to our latest blog post, where we will shared a new project requirement which titled is "Data Insights: Exploring Probabilities for Informed Decision Making." In this project, we tackle various statistical inquiries to derive meaningful insights. Our first task involves answering questions regarding purchasing behaviors based on a provided dataset, calculating joint probabilities to understand consumer tendencies accurately. Moving forward, we delve into scenarios involving quality checks in manufacturing processes and sales projections in a car dealership, employing Poisson distribution modeling to make informed predictions. Furthermore, we explore the importance of accuracy in speech-based order recognition systems for contactless delivery, calculating probabilities of correct order recognition for multiple orders. Our solution approach encompasses probability calculations, Poisson distribution modeling, statistical analysis, and practical applications in real-world scenarios. By employing these techniques, we aim to extract actionable insights for informed decision-making processes.
Project Requirements :
Part A :
Please answer the following questions with all relevant assumptions, explanations and details.
Please refer the table below to answer below questions:
Planned to purchase Product A | Actually placed order for Product A - Yes | Actually placed order for Product A - No | Total |
Yes | 400 | 100 | 500 |
No | 200 | 1300 | 1500 |
Total | 600 | 1400 | 2000 |
A. Refer above table and find the joint probability of the people who planned to purchase and actually placed an order.
B. Refer to the above table and find the joint probability of the people who planned to purchase and actually placed an order, given that people planned to purchase. [1 Mark]
An electrical manufacturing company conducts quality checks at specified periods on the products it manufactures. Historically, the failure rate for the manufactured item is 5%. Suppose a random sample of 10 manufactured items is selected. Answer the following questions.
Probability that none of the items are defective?
Probability that exactly one of the items is defective?
Probability that two or fewer of the items are defective?
Probability that three or more of the items are defective ?
A car salesman sells on an average 3 cars per week.
What is Probability that in a given week he will sell some cars?
What is Probability that in a given week he will sell 2 or more but less than 5 cars?
Plot the poisson distribution function for cumulative probability of cars sold per-week vs number of cars sold per week.
Accuracy in understanding orders for a speech based bot at a restaurant is important for the Company X which has designed, marketed and launched the product for a contactless delivery due to the COVID-19 pandemic. Recognition accuracy that measures the percentage of orders that are taken correctly is 86.8%. Suppose that you place an order with the bot and two friends of yours independently place orders with the same bot. Answer the following questions.
What is the probability that all three orders will be recognised correctly?
What is the probability that none of the three orders will be recognised correctly?
What is the probability that at least two of the three orders will be recognised correctly?
Explain 1 real life industry scenario (other than the ones mentioned above) where you can use the concepts learnt in this module of Applied Statistics to get data driven business solutions.
Solution Approach
In this project, we employed various statistical methods and techniques to analyze and derive insights from our data. Let's delve into the key components of our approach:
Probability Calculation:
We began by calculating probabilities to understand different aspects of our data. For instance, we computed joint probabilities to determine the likelihood of individuals both planning to purchase a product and actually placing an order. This helped us gauge consumer behavior accurately.
Poisson Distribution Modeling:
To model certain scenarios, such as the number of cars sold per week or the occurrence of defective items in a batch, we utilized the Poisson distribution. By understanding the distribution of events over a fixed interval of time or space, we could make informed predictions and decisions.
Statistical Analysis:
Our analysis wasn't just about crunching numbers; it was about extracting meaningful insights. We utilized statistical techniques like hypothesis testing and probability distributions to understand patterns, trends, and anomalies within our data.
Practical Applications:
Finally, we discussed the practical applications of statistics, particularly in the financial sector. From risk assessment to investment decision-making, statistics play a crucial role in shaping financial strategies at both individual and organizational levels.
Some Output :
Here are some output of the above project.
CodersArts embarks on a transformative journey titled "Data Insights: Exploring Probabilities for Informed Decision Making." Through this project, we're not only aiming to unravel the complexities of statistical analysis but also to provide actionable insights for informed decision-making processes. Our first task involves delving into a comprehensive dataset, dissecting the probabilities associated with purchasing behaviors. By computing joint probabilities, we gain a deeper understanding of consumer tendencies, thereby empowering organizations with the knowledge to optimize strategies effectively.
Moving forward, our project extends its reach into various domains, including quality assurance in electrical manufacturing and sales projections in the automotive industry. Leveraging the power of Poisson distribution modeling, we navigate through scenarios such as defect rates in manufactured items and weekly sales figures. These methodologies allow us to make accurate predictions and strategic decisions based on statistical analysis, thus enhancing operational efficiency and performance across industries.
Moreover, our commitment to driving tangible outcomes doesn't end with data analysis. CodersArts envisions a future where data-driven insights pave the way for equitable access to resources, particularly in urban connectivity. By harnessing advanced data analytics and visualization techniques, we aim to bridge the digital divide and foster community engagement. With CodersArts as your partner, navigating the complexities of statistical analysis and deriving actionable insights has never been more seamless, ultimately leading to transformative impacts in various sectors.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.