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RESEARCH

Multiple Regression Analysis (MRA)

Multiple Regression Analysis (MRA)

Multiple Regression Analysis (MRA) is a statistical technique seeks to understand the relationship between two interest variables. The predictor variables are called independent variables and the outcome variable are called dependent variable. For instance, we wish to know what variables (e.g assets, debt and etc) had significant positive/negative relationship with sales. Understanding such relationship is pertinent for management to know if they have invested money to the right place or to understand which of the variables have large/small or positive/negative effects on sales. This is important for future planning of appropriation of resources and general formulation of organizational strategy. MRA is widely used in various operation management applications especially in HR, Marketing and Finance department. Performing such analysis requires in-depth statistical knowledge to ensure validity of the results. As such, it is of importance to ensure robustness and all statistical assumptions are met. Failure to do so would result in bias estimates and invalid results.

Increase Consumer Confidence Today With T-Test

What Is T-Test?

T-Test is a statistical techniques that usually used to test the difference of two or more things. For more detailed explanation, you can google T-Test on the internet.

Is It A New Thing?

No. T-Test has been widely known and accepted by statistician for many years.

How Does It Benefits Businesses?

T-Test can be used to substantiate your claims of your products merits. For instance, a manufacturer claims that the new whitening cream products can whiten skins in 7 days and they wish to test it using T-test to proof it. The results of the T-Test can be used to increase consumer confidence of your products.

How Does IT Benefits The Consumer?

Consumer can buy the products with confidence as the products are proven to works as described. Performing such analysis requires in-depth statistical knowledge to ensure validity of the results. As such, it is of importance to ensure robustness and all statistical assumptions are met. Failure to do so would result in bias estimates and invalid results

How It Works?

Here’s how it works.

1) Businesses will conduct a simple experiment of their products and give us the BEFORE and AFTER results. For instance, using the whitening
cream example, the measure of the skin fairness after using it for 7 days and before using it.

2) We will then statistically test it using T-Test to proof it.

3) A test report will be given to certified it. Businesses can then used it to increase consumers confidence of their products.

 

certified

Cluster Analysis

Cluster Analysis as its names suggests is a statistical data reduction technique used to cluster a large population respondent into smaller groups based on similarity/dissimilarity in their response. Cluster analysis seeks to minimize within-group variance and maximize between-group variance. The result of cluster analysis is a number of heterogeneous groups with homogeneous contents. Clustering can be done via Hierarchical clustering, K-means clustering and two step clustering. The choice of a hierarchical or nonhierarchical technique often depends on the research problem at hand. Typically used by marketing managers to cluster customers into groups of similar value propositions of the products/services offered. This analysis gave managers the insight of their customer characteristics as well as the value propositions that is important to them. It helps to discover customers clusters and spot changing trends. Conducting such analysis is important for understanding the strengths and weaknesses of a product being offered as well as for future product innovation and marketing purposes. This analysis saves managers a lot of time of clustering it manually and more importantly the results are back by proven statistical technique and evidence.

In brief, this analysis helps:

1)Discover Customers inherent Clusters

2)Spot Customers Trend

3)Know what values are important to them

4)Know who are the intended Target market

5)Helps devise Marketing Strategy / CRM

6)Uncover hidden opportunities

Logitics and Discrimination Regression

Logistic regression is multiple regression but with an outcome variable that is non-metric and predictor variables that are either metric or non-metric variables. Discriminant Regression on the other hand is a multiple regression with predictor variable that is in metric form. In its simplest, this means that we can predict which of two categories of a certain interest subject is likely to belong to given certain other information. Logistic and Discriminant Regression is used widely in business applications. For example, in hospital, logistic regression is used to generate model from which predictions can be made about the likelihood about a tumour is cancerous or benign of a patient. In a nutshell, this analysis is used to predict the likelihood of an interest subject falls under which categories based on the other information. A trivial example is to look at what predict a person is male or female. We might measure laziness, pig-headedness, alcohol consumption and number of burps that a person does in a day. Using logistic regression, we might find that all of these variables predict the gender of the person, but the technique will also allow us to predict whether a new person is likely to be male or female. So, if we picked a random person and discovered they scored highly on laziness, pig-headedness, alcohol consumption and the number of burps, then the regression model might tell us that, based on this information, this person is likely to be male. Performing such analysis requires in-depth statistical knowledge to ensure validity of the results. As such, it is of importance to ensure robustness and all statistical assumptions are met. Failure to do so would result in bias estimates and invalid results.

Financial Analysis

  ChiSquares financial analysis provides companies with a holistic view of their financial position. It enables companies to understand the nuance of the financial changes starting from structure of the asset, liabilities and equities, financial sustainability’s ( e.g.,Debt ratio, capitalization ratio and etc), working capital, Liquidity, Business Activities ( e.g. turnover analysis) and so on. Such analysis is important in understanding the company financial strengths and weaknesses in order to make strategic decision. In addition, the financial analysis report can be very useful in presenting your company financial position to potential investors or financial institution like banks in obtaining further capital injection.
The full analyses that are included in the report are as follows:

1)Asset, liabilities and Equities structure analysis

2) Financial Sustainability Analysis

3)Working Capital Analysis

4)Financial Performance Analysis

5)Profitability Analysis

6)Business Activity Analysis (Turnover Ratios)

7)Key Indicator Analysis

8)Financial Position Rating

Company and Industry Beta Analysis

The company Beta is the major component in determining the cost of equity under the CAPM model. Understanding the company Beta is pertinent as it affects firm values or the market capitalization of the company outstanding shares. Traditionally, companies used this information to calculate the Weighted Average Cost of Capital (WACC) in assessing merits of a project. Prudent should be taken not to use company WACC in assessing NPV of all projects as especially projects reside outside the company industry since some industries are riskier and therefore required higher return. Hence, when investing in different industries, the relevant industry Beta should be use and not the company Beta. Understanding Beta helps company to assess the systematic risks of the company shares compare to the market risk. A diversified investor would require higher return when the company Beta is higher than the market risk for bearing the additional risk (a.k.a risk premium). ChiSquares offers the expertise to help companies determine the company or industry Beta for general planning and operational purposes. We adhere to the stringent statistical analysis in determining the Beta and while providing companies the independency of needed in assuring the integrity of the result. 

Sales Predictor Analysis

Which Marketing Channels Is More Effective ?

So we have put lots of efforts and resources on advertising and have tried advertising on TV,Newspaper, YouTube and etc. The bottom line question now is – Which Marketing Channels Is More Effective?. Advertisement today is costly,  as such it is important to know the effectiveness of our marketing efforts.Maybe our advertisement on newspaper is more effective compared to others channel without even we realized it. Common problems with traditional method of measuring marketing effectiveness was that the impact of each marketing channel were difficult to trace overtime especially with overlapping marketing campaign.

Multiple Regression is a statistical technique that is widely used by academicians in the field of marketing to measure marketing effectiveness. Using Regression statistical  technique, the effectiveness of each individual marketing channel  can be obtained over time. Such information enables managers to make necessary changes and improvements to their marketing effort. Numerous real life case studies have been conducted by various academicians to measure marketing effectiveness using regression statistical technique

What Sales Predictor Analysis (SPA) Can Do :

1) Improve  Marketing Effectiveness

2) Knows the effectiveness of each marketing channel  

3)Knows the effectiveness of all marketing channel 

4) Knows How Changes In Price, Features and etc affects Sales 

4)Provides management an understanding of the return of each marketing channel over time

Performing such analysis requires in-depth statistical knowledge to ensure validity of the results. As such, it is of importance to ensure robustness and all statistical assumptions are met. Failure to do so would result in bias estimates and invalid results.