BUSI650 - Business Analytics Descriptive Report Assignment

Assignment Task

Introduction

The following report will consist of a detailed examination of the profits of the entity for the years 2013 and 2014 in various segments like channel partners, enterprise, government, midmarket and small business in which the entity operates. Furthermore, statistical analysis of the profits using statistical tools is also undertaken in the report. The data types and their cause-and-effect relationship will also be explained in the report.

Purpose and focus

The main motive of the report is to examine the profit of the entity in different countries like Canada, France, Mexico, Germany, and the United States of America in the years 2013 and 2014. The models of the car offered by the company are Amarilla, Carretera, Montana, Paseo, Velo, and VTT. The company operates in various segments such as Channel Partners, Enterprise, Government, Midmarket, and Small businesses. The profit analysis helps the entity in knowing the profits earned by the business in various segments in the given years. It provides an entity with a way of segmenting its profits earned during a financial year. Several measures impact the profit of the entity and knowing these factors will assist in determining the financial position of the entity. The report focuses on forecasting the profit potential of the organization in various segments of the entity and also recognizing the potential customers of the organization (Cooke, 2020).

It is being examined that the profits of the entity get decrease due to the rising prices for fuel and increased number of competitors as the fuel prices are rising at a rapid pace in the countries where the entity operates. Inflation is the main cause of the rise in fuel prices and decreased profits of the entity. People prefer to use public transport instead of buying cars for themselves.

Hypothesis

Hypothesis question: If the organization produces a car that runs on electricity, will this increase the profit of the entity?

Research: It is examined from the profit analysis of the entity that due to increased competitors and increased fuel prices the company faces a decrease in the profit potential.

Variables: The dependent variable in this hypothesis is the profits of the car and the independent variable is the number of competitors and fuel prices.

Conclusion of hypothesis: It can be concluded from the hypothesis that if the organization introduces electric cars then its profits can increase. On the contrary, as the number of competitors is increasing at a rapid pace and this factor is beyond the control of the entity hence this will affect the profitability of the organization (Gegic et al., 2019).

Analysis

Measures of Central Tendency

The average of the data set is referred to as the mean and is calculated by dividing the aggregate of the given data by the total number of data set. The calculated Mean of the profits of products for the given years 2013 and 2014 is 14316.85 . It is accurate to utilize the mean as a measure of central tendency when there is a balanced distribution of data. The central value of the data set is organized in ascending or descending order known as the median. The median of the profits of the company amounts to 8298.95 . In statistics, the value which is occurring at the highest frequency in a data set is determined using mode. The mode of the profits of the entity amounts to 10890.

Measures of Spread

Standard Deviation, Variance, Kurtosis, and Z-score percentiles are the four measures of spread used in this report to analyze the profit of the entity in different segments in the year 2013 and 2014. The estimation of the amount of dispersion about the mean of the given data set is referred to as the standard deviation of the data. The estimated standard deviation of the given data set is 21755.91956 . The variance can be estimated by squaring the results of the difference between each point and the mean of the data set. The calculated variance of the given data set is 473320036.1 . Kurtosis is utilized to estimate the financial risk prevailing in the entity. In this prepared report, kurtosis amounts to 3.361862188 . Z-score percentiles in used to determine the actual cutoff for the data. 38% is the estimated Z-score percentile (Mattioli et al., 2020).

Pivot Table Analysis

A pivot table is a statistical tool that is used to recapitulate detailed data to achieve the desired results. It is used to examine the data present in numerical form in detail and provide answers for the given data. In the prepared report, the result of the pivot table analysis shows that the country Canada, France, Germany, Mexico, and the United States of America each had 20 segments of channel partner, enterprise, mid-market and small business, and 60 segments of government respectively.

Linear Regression, Correlation coefficient

Linear regression is utilized to identify the element and the relation between a dependent variable and an independent variable and assists in creating strategies to forecast the profits or sales or the stock price of the entity. It is also used to identify the cause-and-effect relationship that subsists among the variables. The least-square technique is the procedure of identifying the appropriate curve or best-fitted line for the given data set by deducting the total of the squares of the other points of the given data set. The correlation coefficient is estimated as 0.068774 in the following report. Furthermore, a scatter plot of the profits of the organization is also developed to show the linear regression using the least square method (Tangngisalu et al., 2020).

Variables

The dataset is categorized into three kinds of data types namely discrete, continuous, and categorical variables. The date is a discrete as well as a continuous variable, Month number is a categorical variable, profit is a discrete variable, and year, sales, and country all are continuous variables

Cause and Effect

Categorical variables consist of a limited number of groups. It may not have a logical order. On the contrary, discrete variables have countable values and are numeric variables. Sometimes, discrete data can also be termed categorical variables as the data can be accumulated in the form of categories as well.

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