Description

Decision Science

September 2024 Examination

 

 

 

1. Consider the following data on the categories of YouTube Shorts and their views. The table shows the number of YouTube Shorts in each category that have been classified as either “High Views” (more than 1,000,000 views) or “Low Views” (1,000,000 views or less).

Category	High Views	Low Views
Music	50	100
Comedy	30	170
Education	20	180
Gaming	40	160
Travel	10	190

 

1. What is the probability that a randomly selected YouTube Short is in the Music category?
2. What is the probability that a randomly selected YouTube Short has High Views?

iii.What is the probability that a randomly selected YouTube Short has Low Views?

1. If a YouTube Short is known to be in the Comedy category, what is the probability that it has High Views?
2. If a YouTube Short is known to have High Views, what is the probability that it is in the Gaming category? (10 Marks)

Ans 1.

Introduction

In today’s digital landscape, YouTube Shorts have become a significant medium for content creation and consumption, offering a quick and engaging way to reach viewers. This analysis explores the probabilistic relationships between different categories of YouTube Shorts and their viewership levels—classified into “High Views” and “Low Views.” Understanding these probabilities is crucial for content creators and marketers as they strategize to maximize reach and engagement on the platform. By examining the distribution of views across various content categories such as Music, Comedy, Education, Gaming, and Travel, we can gain insights into viewer preferences and the potential success rates for different types of content. This analytical approach not only aids in content strategy but also enhances our comprehension of the dynamics within digital media consumption.

Concept and Application

Understanding Probability in YouTube Content Analysis

Probability helps digital content analysts make data-driven decisions. In YouTube Shorts analysis, probabilities help content creators, marketers, and platform analytics predict outcomes

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2. Use 4 month moving average, and 5 month moving average too. For which moving average Mean Square Error is less? (10 Marks)

Month	Total views on YouTube channel
Jan-23	10484
Feb-23	10884
Mar-23	13372
Apr-23	14742
May-23	16141
Jun-23	17210
Jul-23	17303
Aug-23	18132
Sep-23	18208
Oct-23	19099
Nov-23	20936
Dec-23	21914
Jan-24	22892
Feb-24	23870
Mar-24	24848
Apr-24	25826
May-24	26803
Jun-24	27781

 

Ans 2.

Introduction

In the study of time series data, moving averages are a fundamental statistical tool used to smooth out short-term fluctuations and highlight longer-term trends in data. This method is particularly useful in analyzing data like monthly YouTube views to understand viewership trends better without the noise of monthly variability. This report explores the application of two moving average techniques: the 4-month and 5-month moving averages. By comparing these methods through the Mean Square Error (MSE), a measure of prediction accuracy, we aim to identify which moving average provides a more accurate reflection of the underlying trend in YouTube viewership. The goal is to determine the optimal smoothing parameter that offers the clearest insight into future trends, essential for strategic planning and decision-making.

Concept and Application

The Essence of Moving Averages

Moving averages are a pivotal tool in time series analysis, offering a way to smooth out data series and mitigate the effects of short-term fluctuations, thus emphasizing underlying longer-

 

 

3a. Use an appropriate chart to show the contribution of each category of sales with conclusion. Rahul has this channel and offers a variety of content to users.  (5 Marks)

category	Average Views
Music	1,200,000
Comedy	800,000
Education	600,000
Gaming	900,000
Travel	700,000

 

Ans 3a.

Introduction

In the realm of digital content creation, analyzing viewer engagement across various categories is crucial for content strategy development. Rahul’s channel, which spans multiple genres, provides an interesting case study to understand which categories resonate most with his audience. This analysis focuses on the average views per category, such as Music, Comedy, Education, Gaming, and Travel. By utilizing a pie chart, we can visually depict the contribution

 

 

3b. Suppose we have the duration (in seconds) of 10 YouTube Shorts videos: 120, 130, 140, 125, 135, 150, 128, 132, 142, and 155. Calculate the mean (average) duration of these videos. (5 Marks)

Ans 3b.

Introduction

YouTube Shorts offers content creators a platform to engage audiences with brief, compelling videos. Analyzing the duration of such videos can provide insights into optimal content length for maximizing viewer engagement and adherence to platform norms. This analysis involves calculating the mean duration of a sample of 10 YouTube Shorts videos. The mean duration, or average, is a fundamental statistical measure that gives a central value around which the durations of these videos vary. Understanding this average can help in strategizing content