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TABLE WITH INFORMATION IS ATTACHED. Thank you
The below table shows the current and potential presence of H&M Group and its related brands. The categorical value, and identifier, is represented in the “Brand†column- the purpose of this data is to identify which brand of H&M the variables are associated with. The three data points included in this table are 1) the number of brick-and-mortar store markets, 2) the number of online markets, and 3) potential expansion markets. The store and online market data sets are quantitative variables measured by the number of countries that each brand currently has a presence. The expansion/new markets data is categorical, nominal data which indicates the markets which H&M has expanded into or plans on expanding to (Sharpe at al., 2019).
A time series is an ordered sequence of quantitative variables measured at regular intervals over time whereas cross-sectional data measures several variables at the same time point but not at regular intervals (Sharpe et al., 2019). This variable set is cross-sectional because it measures multiple variables at one point in time.
The important factor in the data set is the new markets variable. This report has missing data because it does not explain the justification for opening the new stores or the decisions to open a brick-and-mortar store versus an online store. This dataset would be better analyzed with a relational database that includes store performance for each of the brands (Sharp et al., 2019). This data would provide the reasoning and justification for such choices.
Investors may find this useful because the table broadly displays the health of the various H&M brands. New Markets and store openings generally indicate growth and investors can expect that an increase in markets will correlate to an increase in sales and revenue. H&M clearly has a large physical and digital footprint that is continuing to grow. This table also indicates the addition of new brands (brands are listed in order of established date), showing that H&M is actively adapting to consumer demand.
References:
Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.). Retrieved from https://www.redshelf.com