Foundations of Quantitative Data Analysis

The world has become data-driven today. Every decision is data-oriented and organizations are spending millions of dollars to carve out the best possible strategies to develop a solid data framework. The topic touches on the basic topics of quantitative data such as  – quantitative designs, level of analysis, a measure of analysis which all will add to your knowledge of quantitative data analysis.

Quantitative Data Analysis explained

Introduction of new lines (e.g., Coca-Cola light, Colgate toothpaste for children) or brands (e.g., financial
services by Sainsbury, men’s grooming by Harley Davidson) are widely adopted extension strategies. As compared to single brand strategies, collaborations between two or more brands represent an alternative strategy whose design aims to take advantage of complementarities and inter-brand synergies.

Probably the most widely quoted example is between Dell and Intel while recent efforts include Apple and Nike (Apple Watch Nike+) and GoPro and Red Bull (GoPro: Red Bull Stratos). According to Simonin and Ruth (1988, p. 30), brand alliances represent ‘the short- or long-term association or combination of two or more individual brands, products, and/or other distinctive proprietary assets.’


• Part 1

Begin by Commenting on summary statistics, including the main distribution characteristics, of the Evaluation_AB variable.

• Part 2

Determine whether the expectation that female respondents have evaluated the brand alliance between htc and Swatch higher than male respondents is supported.

Examine whether the data provides evidence of significant differences in the evaluations of the four brand
alliances. Additionally, comment on the pattern of any identified significant differences.

• Part 3

Using attitudes towards the two-parent brands, product fit, and the sixe types of brand fit as predictors
develop the ‘best’ model to explain/predict evaluation of the brand alliance.

The focus must be on the interpretation of results as well as stating and explaining any underlying assumptions.
Avoid including irrelevant and unnecessary information in your assignment; you may include supporting information in appendices.

Part 1

First, are the implications of the summary statistics explained? Is there any necessary remedial action (e.g. identifying and removing outliers) and explained? The assessor will also use this area to leave comments that relate to this criterion.

Part 2

Furthermore, are the tests appropriate? Are hypotheses correct? Is the process of testing hypotheses and reaching a conclusion clear? Are we drawing any conclusions from the results of the hypothesis tests (i.e. does the answer go beyond reject / retain the null hypothesis)? This assessor will further use this area to leave comments that relate to this criterion.

Part 3

Lastly, have there been any tests on regression’s underpinning assumptions or any necessary remedial action taken? Are the
regression results reported and explained clearly? Are the results drawing clear conclusions?

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