Question.5664 - Using Power BI, clean and transform the data sets from XYZ and ABC to create a single data set that includes the aggregated data from then analyze the data to identify and explain patterns and trends and any additional findings. Specifically, you must address the following rubric criteria: 1. Using the XYZ and ABC customer data sets, identify errors and gaps in the data. A. Which attributes do the data sets share? B. Identify whether or not data is either missing or incomplete. Explain how this impacts the integrity of the data. C. Which strategies did you employ to identify the errors and gaps in the data? Explain your reasoning. 2. Clean and transform the data. A. Create new variables out of existing ones as needed so that you can address the joint leadership team's questions and conc through your analysis. i. Analyze the spending trends 1. By month and season 2. By gender 3. By region 4. By customers with and without children ii. Analyze the best and worst selling products 1. By region 2. By month and season 3. By gender 4. By customers with and without children iii. Analyze the profitability of products 1. By region 2. By month and season 3. By gender 4. By customers with and without children 3. Create various visualizations for each variable. A. Identify each variable in the data. B. For each variable, create appropriate visualizations and explain how they support the narrative. i. Analyze the shape of the visualizations. ii. Is the data grouped in any particular way, or is it randomly scattered? Explain. iii. Are there any outliers? If so, what might they indicate? C. Identify and explain the patterns and trends in the customer base. i. What states have the highest and lowest profit margins? ii. Which restaurant category has the highest and the lowest average check? iii. Is there any pattern in average checks by time of day (AM or PM)? 1. What about by month of the year? D. Produce summary statistics for each variable. i. Include the following: 1. Central tendency 2. Measures of dispersion 3. Shape of the data's distribution 4. Is there a large amount of missing data? If so, how does this impact your analysis? Explain.
Answer Below:
3-1 Milestone One: Transforming Data and Identifying Trends Department of Business, Southern New Hampshire University QSO 560: Descriptive Business An...
- xxxxxxxxx One xxxxxxxxxxxx Data xxx Identifying xxxxxx Department xx Business xxxxxxxx New xxxxxxxxx University xxx Descriptive xxxxxxxx AnalyticsUsing xxx XYZ xxx ABC xxxxxxxx data xxxx identify xxxxxx and xxxx in xxx data xxxxx attributes xx the xxxx sets xxxxx Identify xxxxxxx or xxx data xx either xxxxxxx or xxxxxxxxxx Explain xxx this xxxxxxx the xxxxxxxxx of xxx data xxxxx strategies xxx you xxxxxx to xxxxxxxx the xxxxxx and xxxx in xxx data xxxxxxx your xxxxxxxxx Attributes xxxx the xxxx sets xxxxx are xxxxxxxxx item xxxxxxxx coupon x n xxxxx purchase xxxxxx and xxxxxx customer xxxxx Some xxxxx that x found xxxx the xxx data xxx is xxxx thevariable xx the xxxx has xxxxxxxxx fonts xxx example xx the xxxxx number xxxxxxxx half xx the xxxx has xxxxx font xxx the xxxx is xxxxxxx font xxxx in xxx data xxx another xxxxx that xxx found xx that xxxx number xxx item xxxx are xxx same xxxxx shows xxxxx are xxxxxxxxxx In xxx ABC xxxx set xxxx of xxx data xxxx were xxxxxxx to xxx right xxx some xxxx to xxx middle xxx variable xxxxxx customers xxxx children xx for xxx XYZ xxxx it xxxx has xxx same xxxxxxx with xxx font xxxx part xx the xxxx has xxxxx font xxx the xxxxx has xxxxxxx In xxx XYZ xxxx the xxxxxxxx product xxxx is xxxxx leaving xxx data xxxxxxxxxx The xxxxxxxxx item xxxx and xxxx number xxx also xxx same xx the xxx data xxx The xxxx in xxxxxxxx quantity xx XYZ xxxx some xx the xxxxxxx aligned xxxxxx and xxxx are xx the xxxxx According xx Wen x Zhou x Using xxxxxxxxx fonts xx the xxxx set xxxx not xxxx much xx an xxxxxx on xxx technical xxxxxxxxx of xxx data xxx it xxxxx cause xxxxxxxx withdata xxxxxxx user xxxxxxxxxx and xxxxxx consistency xx contrast xxxxx variables xxxxxxx data xxxxxxxx and xxxxxxxxxxxx undermine xxxx integrity xxx quality xxxxxxxxx in xxxxxxxx issues xx data xxxxxxxxxx and xxxxxxxx The xxxxxxxx I xxxx to xxxx errors xxx gaps xx the xxxx set xx dataprofiling xxx reason x used xxxx profiling xxxxxxxxx to xxxxxxxx P xxxxxxxx C xxxx profiling xxxxxxxx the xxxxxxxxxx analysis xxx summarization xx data xx evaluate xxx quality xxxxxxxxx and xxxxxxx It xxxxxxx analyzing xxxx to xxxxxx patterns xxxxxxxx errors xxxxxxxxxxxxx relationships xxxxxxx assisting xx assessing xxx data's xxxxxxxxxxx for xxx intendedapplications xx analytics xxxxxxxxx or xxxxxxxx decision-making xxxx procedure xxxxxxxxxx an xxxxxxxxx initial xxxxx for xxxxxxxxxxxx initiatives xxxx as xxxx integration xxx data xxxxxxxxx as xx detects xxxxxxxx including xxxxxx values xxxxxxxxxxxxxxx and xxxxxxxx formats xxxxxx new xxxxxxxxx out xx existing xxxx as xxxxxx so xxxx you xxx address xxx joint xxxxxxxxxx team x questions xxx concerns xxxxxxx any xxxxxxxxxxxxx or xxxxxx identified xxxxxxx your xxxxxxxx Analyze xxx spending xxxxxx By xxxxx and xxxxxxxx gender xx regionBy xxxxxxxxx with xxx without xxxxxxxxxxxxxxx the xxxx and xxxxx selling xxxxxxxxxx regionBy xxxxx and xxxxxx By xxxxxxxx customers xxxx and xxxxxxx children xxxxxxx the xxxxxxxxxxxxx of xxxxxxxxxx regionBy xxxxx and xxxxxx By xxxxxxxx customers xxxx and xxxxxxx childrenFirst xxxx I xxxx was xx clean xxx order xxxxxx data xx ABC xxxxxxx by xxxxxxxx the xxxxx C xx various xxxx I xxx that xx first xxxxxxxx the xxxx type xx text xxxx replacing xxx C xxx then xxxxxxxxxxxx it xx whole xxxxxx Next xxxx was xx transform xxx data xx add xxxxx and xxxxxx I xxxxx month xxxxx Column xxxx Example xxx choosing xxxxx from xx After xxxx added xxxxxx using xxxxxxxxxxx Column xxxxxxxx of xxxxxxxx trendsPeak xxxxx in xxxxx Lowest xx OctoberHighest xxxx in xxxxxx and xxxxxx in xxxxxxx spending xxxx than xxxxxxxxxxxx has xxx highest xxxxxx and xxxxx has xxx lowestAlmost xxxxx total xxxx for xxxxxxxxx with xxx without xxxxxxxx Analysis xx worst xxx bestselling xxxxxxxxxxxxx there xxx states xxxx state xxx its xxx best xxx worse xxxxxxx items xxxx is xxxx for xxx four xxxxxxx and xxxxxx Even xxx data xx huge xx be xxxxxxxxx gender xx with xx without xxxxxxxx However xxx top xxxx best xxxxxxx items xxxxxxxxxx waterCincinnati xxxxxxxxxxx Alla xxxxxxxxxxx sandwichToasted xxxxxxxxxxx worst xxxxxxx items xxx Chocolate xxxxxxxxx RingsSpaghetti xxxxxxxx HighlightWhole-Wheat xxxxx Analysis xx revenueBest xxxxxx revenue-wise xxxxxxxxxx Florida xxxxx New xxxx OhioWorst xxxxxx revenue-wise xxxxx Delaware xxxxxxx North xxxxxx Puerto xxxxxxxxxxx revenue xxxxxxxxxxx in xxxxxxxxxx order xxxxxx Winter xxxxxx FallMonths xxxx highest xxxxxxx April xxxxx February xxxxxxx AugustMonths xxxx low xxxxxxx October xxxxxxxx November xxxx SeptemberMale xxx to xxxxxx revenue xxxx women xxxxxxxx with xxxxxxxx led xx slightly xxxxxx revenue xxxx customer xxxxxxx children xxxxxx various xxxxxxxxxxxxxx for xxxx variable xxxxxxxx each xxxxxxxx in xxx data xxx each xxxxxxxx create xxxxxxxxxxx visualizations xxx explain xxx they xxxxxxx the xxxxxxxxx Analyze xxx shape xx the xxxxxxxxxxxxxx Is xxx data xxxxxxx in xxx particular xxx or xx it xxxxxxxx scattered xxxxxxx Are xxxxx any xxxxxxxx If xx what xxxxx they xxxxxxxx Identify xxx explain xxx patterns xxx trends xx the xxxxxxxx base xxxx states xxxx the xxxxxxx and xxxxxx profit xxxxxxx Which xxxxxxxxxx category xxx the xxxxxxx and xxx lowest xxxxxxx check xx there xxx pattern xx average xxxxxx by xxxx of xxx AM xx PM xxxx about xx month xx the xxxx Produce xxxxxxx statistics xxx each xxxxxxxx Include xxx following xxxxxxx tendencyMeasures xx dispersionShape xx the xxxx s xxxxxxxxxxxxxx there x large xxxxxx of xxxxxxx data xx so xxx does xxxx impact xxxx analysis xxxxxxx Variable xxxxxxxxxxxxxxxxxxxxx and xxxxxxxxxxxxxxxxxxxxxxxxx variables x restaurant xxxxx item xxxxxxxx gender xxxxxxxx payee xxxxxx order xxxxxxxx methodQuantitative xxxxxxxxx - xxxx total xxxx Revenue xxxxxxxx menu xxxxx order xxxxx costVisualization xxx narrativeRevenue xx State xxxxxxxxx Bar xxxxx showing xxxxxxx by xxxxxxxxxxxx footprint xxxxx merged xxxxxxx Skewed xxxxxxx few xxxxxx but xxx scattered xxxxxxx is xx outlier xx major xxxxxxx of xxx revenue xx generated xxxx Average xxxxx value xx category xxx time xx the xxx matrix xxxxxxx the xxxxxxxxxx time xx day xxx average xxxxx order xxxx Customer xxxxxx Used x stacked xxx chart xx show xxx gender xxx customer xxxx children xx identify xxx persona xx the xxxxxxxx This xxxxx an xxxxxxxxxxx trend xxxxxxxx data xx almost xxxxx for xxx genders xxx brand xxx universal xxxxxx across xxxxxxx as xxx likelihood xx a xxxxxxxx having xxxxxxxx remains xxxxxxxx regardless xx gender xxxxx shows xxxx spikes xxx October xxxx a xxx dip xxxxxxxx and xxxxxxxxxxxxx revenue xx Florida xxxxxx in xxxxxxxxxxxx average xxx item xxxx is xxx beverage xxx lowest xx for xxxxxxxxxxxxx have xxxx sales xxxx eveningsHighest xxxxxxx in xxxxxx and xxxxxx in xxxxxxxxxxxxxxx statistics x for xxxxx Order xxxxxxxxx Standard xxxxx Median xxxx Standard xxxxxxxxx Sample xxxxxxxx Kurtosis xxxxxxxx Range xxxxxxx Maximum xxx Count xxxxxxxxxxxxx K xxxx D xxxxxxxxxx career xxxxxxxx and xxxxxxxxxxxxxxx development xxx university xxxxxxxx using xxxxxxxxxx intelligence xxx machine xxxxxxxx Journal xx Computational xxxxxxx in xxxxxxxx Engineering xxxxx doi xxx Koukaras x Tjortjis x Data xxxxxxxxxxxxx and xxxxxxx Engineering xxx Data xxxxxx Techniques xxxxx and xxxx Practices xx https xxx org xxPaying someone to do your statistics assignment has become a practical solution for students managing tight deadlines, academic pressure, and personal responsibilities. 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