Question

1. Imagine you were shown prior student feedback on the course and one comment was “I am doing Business major X, why do I have to do a statistics course? It is not relevant”. Unfortunately, you do not know which major, X the student was referring to. Select the number of Business Majors at RMIT equal to the number of people in your group and illustrate an example of a simple or multiple regression model relevant to each of the selected majors. Explain and interpret the relevance of the regression model to the discipline major.

Answer 1

ANSWER::

Group 3

SELECTED BUSINESS MAJORS FROM RMIT

• Digital Marketing
• Global Trade Operations
• Financial Markets

DIGITAL MARKETING

Regression analysis is a statistical method for modeling relationships between different variables (dependent and independent). It is used to describe and analyze relationships in data. Predictions can also be made using regression analyses, whereby the relationships in the data would be used as a basis for the forecast and generated by a prediction model. Regression and correlation analyses are considered to be part of multivariate analytical methods and are used in many different areas, including science, statistics, finance, and now also online marketing, in order to analyze and partly predict the costs and turnover of products, campaigns, channels, and advertising media.

Regression analyzes are used in online marketing, for example, to understand the customer journey using web analytics data or to support multi-channel marketing with reliable data. In practice, such analyses are complex and require professional know-how and knowledge. But the results can be very clear and tangible, depending on the model. For example, if attribution modeling is used for checking multiple channels like direct sales, display ads, affiliates, social media, email, or referrals, regression analyzes can clearly show which of these channels have a good balance between investments and sales. At corporate levels and with specific partners who can realize such analyses, the results are likely to be extremely helpful and could significantly increase the ROI of individual digital assets. Time series analysis comprises methods for analyzing time-series data in order to extract meaningful statistics from it. This analysis can be divided into two categories: frequency-domain methods and time-domain methods. In digital marketing, we mainly use the latter in order to predict the trend of different metrics in the future. Methods of time series analysis may be divided into parametric and nonparametric, linear and non-linear, and univariate and multivariate.

Planning regressions can highlight the hypothesis you are looking to prove and establish what support we'll need. The effort transforms the business questions into a framework that can help you decide if a basic regression will suffice or a deeper machine learning initiative is needed to answer the question. The line between machine learning and regression analysis has been debated ad nauseam and the lines become particularly blurry when looking at prediction and forecasting. Machine learning establishes data relationships through the induction of the process of bringing about or giving rise to the relationship. In plain language, it means data is transformed into a model to predict what output data is likely. This approach differs from statistical treatment of a regression, where weights are set to output based on inputs.

GLOBAL TRADE OPERATIONS

Forecasting of the volumes of import/export containers are central to the planning and the operation of seaport organizations and government transportation departments at both micro and macro levels. At the seaport organization level, forecasts of container volumes are needed as the essential inputs to many decision activities in various functional areas such as building new container terminals, operation plans, marketing strategies, as well as finance and accounting.

Some developing countries exhibit the same forecast problems. Because the unit prices of international trade goods become more expensive and the volumes of international trade goods become smaller. These will result in the errors of the non-stationary contribution coefficient. In order to reduce this forecast error produced by the non-stationary contribution coefficient in the developing countries, this modified regression model can be used to forecast the volumes of international trade export/import containers. This modified regression model can not only be used to forecast the volumes of international trade containers from/to Taiwan, but also can be used to forecast the volumes of international trade containers from/to other developing countries.

We consider our modified regression model for forecasting the volumes of import/export containers as an interesting finding and a useful model. As to the future research, more tests should be conducted in developing countries to see whether our model is still providing higher forecast accuracy.

FINANCIAL MARKETS

Stock market forecasting is the act of demanding to conclude the future price of a company stock or other financial instruments traded on an exchange. The successful forecast of a stock's future value might give up important profits. The efficient-market hypothesis suggests that stock prices reveal all currently existing information and any price changes that are not based on newly exposed information thus are intrinsically unpredictable. Stock price prediction is possible by Data mining Algorithms. Data mining techniques have been effectively revealed to produce high forecasting accurateness of the movement of the stock price. Nowadays, as an alternative to a particular method, traders have to use various predicting methods to increase several signals and more information about the market's future. Data mining methods have been introduced for forecasting of movement indication of stock market index. Data mining techniques have a more successful act in predicting various fields such as policy, economy, and engineering compared to usual statistical techniques by discovering unknown information of data.

Trade-in stock market deals with the movement of money of a security or stock from a trader to a buyer. This requires these two parties to have the same opinion on a price. Equities (stocks or shares) present a rights interest in a specific company. Stock market participants range from small individual stock investors to larger traders investors, who can be based wherever in the world, and may contain insurance companies or pension funds, banks, and hedge funds. Their buy or sell orders may execute on their behalf by a stock exchange dealer. Stock trading volume includes the number of lots bought and sold which is expressed on a daily basis. The more trading volume of a stock is higher, the more the stock is active. Trading volume is an appreciative of price patterns in practical testing and it's additional vital than stock price.

Regression analysis refers to a method of mathematically sorting out which variables may have an impact. The importance of regression analysis for a small business is that it helps determine which factors matter most, which it can ignore, and how those factors interact with each other. The importance of regression analysis lies in the fact that it provides a powerful statistical method that allows a business to examine the relationship between two or more variables of interest. The regression method of forecasting involves examining the relationship between two different variables, known as the dependent and independent variables. Suppose that you want to forecast future sales for your firm and you've noticed that sales rise or fall, depending on whether the gross domestic product goes up or down.

Predictive analytics i.e. forecasting future opportunities and risks is the most prominent application of regression analysis in business. Demand analysis, for instance, predicts the number of items that a consumer will probably purchase. However, demand is not the only dependent variable when it comes to business. Regression analysis can go far beyond forecasting impact on direct revenue. For example, we can forecast the number of shoppers who will pass in front of a particular billboard and use that data to estimate the maximum to bid for an advertisement. Insurance companies heavily rely on regression analysis to estimate the credit standing of policyholders and a possible number of claims in a given time period. Data Science understanding is key for predictive analytics.

Regression models can also be used to optimize business processes. A factory manager, for example, can create a statistical model to understand the impact of oven temperature on the shelf life of the cookies baked in those ovens. In a call center, we can analyze the relationship between wait times of callers and the number of complaints. Data-driven decision making eliminates guesswork, hypothesis, and corporate politics from decision making. This improves business performance by highlighting the areas that have the maximum impact on the operational efficiency and revenues.

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Group 2

ANSWER SELECTED BUSINESS MAJORS FROM RMIT Digital Marketing Global Trade Operations Financial Markets DIGITAL MARKETING Regression analysis is a statistical method for modeling relationships between different variables (dependent and independent).

It is used to describe and analyze relationships in data. Predictions can also be made using regression analyses, whereby the relationships in the data would be used as a basis for the forecast and generated by a prediction model. Regression and correlation analyses are considered to be part of multivariate analytical methods and are used in many different areas, including science, statistics, finance, and now also online marketing, in order to analyze and partly predict the costs and turnover of products, campaigns, channels, and advertising media

A regression is based on the idea that a dependent variable is determined by one or more independent variables. Assuming that there is a causal relationship between the two variables, the value of the independent variable affects the value of the dependent variable. For example, if you wanted to find out how your advertising investments impact sales, a regression analysis would be used to examine the relationship between the investments and the sales. If this relationship is clearly represented, it can serve as a prediction.Regression analyses have two central objectives. They are supposed to:

• Quantify relationships and describe them using measured values​​and their graphical representation.
• Provide forecasts and predictions.

Overview of various regression analyses:

• Simple regression: Only one explanatory variable is used to explain the dependent variable.
• Multiple regression: Several explanatory variables are related to a dependent variable.
• Linear regression: There is a linear relationship between several explanatory variables and several dependent variables. The concept also included parameters which are linear and produce a structure.
• Nonlinear regression: If there are no linear relationships between dependent and independent variables, you get non-linear regression. These models can be very complex because the relationships between the variables cannot be mapped using simple mathematical methods.

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