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    home / solutions-library / scenario you are working as a busin...

    Question.5660 - Scenario You are working as a business analyst for XYZ, a regional restaurant chain. Recently, your organization was acquired by ABC, another re formed business needs to create a profitable three-year plan. They will look to your analysis to inform this plan, given that neither comp analyst to look at the data of the newly formed business. Both restaurant chains operate out of multiple locations, sell direct to consumers, and are fast-casual restaurants. However, they offer pases. You need to provide a descriptive analysis of what the new joint customer base looks like and how that compares in terms of the selling in, now that they are a single entity with locations in different geographical regions than their home base. Your final product will be a suite of visualizations that explain to the joint leadership team the current state of this newly formed busine the products that have historically sold best and worst and are the most and least profitable. Finally, you have been asked to compare t where the newly formed restaurant chain has locations. Using your analysis, the joint leadership team will craft a three-year plan to mal industry segment, which is notorious for being competitive. Directions Part One: Transforming Data and Identifying Trends 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. 1. Using the XYZ and ABC customer data sets in the Supporting Materials section, 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 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. Part Two: Stakeholder Requirements and Descriptive Analysis Model Application Using the cleaned and transformed dataset from Part One, you will run the data through descriptive analysis models in Tableau and sug 1. Using the Stakeholder Requirements Document, identify stakeholder requirements. A. How will these requirements shape your analysis? Explain. 2. Compare various descriptive analysis methods. A. Compare and contrast a k-means cluster analysis method to at least two of the following models: i. Regression ii. Decision tree iii. Random forest B. Which model is best suited for your analysis? i. How does the output of the model support your analysis? Explain. ii. Why are the other models not suited for your analysis? Explain. 3. Conduct a k-means cluster analysis. A. Conduct at least three different cluster models and explain the differences. B. Explain which model best fits the data. Justify your reasoning. i. Which cluster created the most discrete groupings? Explain. ii. How do these groupings inform your analysis? 4. Based on your analysis, produce summary statistics for each cluster. A. Explain the subgroups that are present. B. Compare and contrast the various subgroups. C. Are there any areas where the clusters overlap one another? D. How well does your analysis support or meet stakeholder needs? Part Three: Analysis and Recommendations Presentation Use the cleaned and transformed data set to present visual solutions to the various stakeholders. Include any previously created visuali 1. Current State Analysis (Slides 1-4) A. Address the following for both XYZ and ABC restaurant chains, as well as the merged company. i. What are its most/least popular items? ii. What are the most and least profitable items? iii. Who are the top and bottom customers, in terms of profit? 2. Findings From Analysis (Slides 5-8) A. Demographic and purchasing profiles of the customer segments i. How do the customer demographics compare to the population demographics of the area in which the restaurant cha 3. Implications and Recommendations (Slides 9-15) A. Explain the status of the stakeholder requirements. i. Did you have the data you needed? Explain how that impacted your analysis. ii. Explain the relevance and potential limitations of the data underlying your visualizations. B. Recommend areas for improvement for various stakeholders based on your analysis. i. Explain shortcomings in the data that may impact your recommendation. ii. Are there business processes that need to be altered to obtain the correct data? iii. How does your analysis impact optimal resource allocation for each stakeholder group in the next three years? 1. Sales 2. Marketing 3. Information technology 4. Finance

    Answer Below:

    PROJECTPart xxx Transforming xxxx and xxxxxxxxxxx TrendsDepartment xx Business xxxxxxxx New xxxxxxxxx University xxx Descriptive xxxxxxxx AnalyticsThe xxx and xxx datasets xxxx common xxxxxxxxx such xx item xxxxxxxx coupon xxx no xxxxx purchase xxxxxx and xxxxxx of xxx customer xxxxx However xxxx data xxxxxxxxxxx have xxxxxxx issues xxx ABC xxxxxxxxx order xxxxxx variable xxx different xxxxx Some xxxxxxx are xx Arial xxxxx others xxx in xxxxxxx Moreover xxx item xxxxxx and xxxx code xxxxxx are xxx same xxxxxxxxxxxxxxxxx The xxxxxxxx with xxxxxxxx column xxxx has xxxxx formatting xx some xxxxxx are xxxxxxx to xxx right xxx others xxx centered xxxxxxxx the xxx dataset xxx font xxxxxxxxxxxxxxx Arial xx Calibri xxx the xxxxxxx cost xxxxxxxx has xxxx blanks xxxx making xxx data xxxxxxxxxx Similar xx ABC xxx item xxxxxx and xxxx code xxxxxx are xxxxxxxxxx and xxx quantity xxxxxxxx has xxxxx formatting xxx and xxxx explain xxxx font xxxxxxxxxxx do xxx affect xxx technical xxxxxx of xxx data xxxxxxxxxxxxx but xxxx can xxxxxxxxx data xxxxxxx user xxxxxxxxxx and xxxxxx consistency xx the xxxxx hand xxxxxxx values xxxx as xxx blank xxxxxxx cost xxxxxxx in xxx are x direct xxxx integrity xxxxx that xxxxx to xxxxxxxx in xxxxxxxx and xxxxxxxx making x used xxxx profiling xx find xxxxx mistakes xxx omissions xxxx profiling xx an xxxxxxxx that xxxxxxxxxxxxxxx evaluates xxx summarizes xxxx to xxxxxxxxx its xxxxxxx structure xxx content xx Koukaras xxx Tjortjis xxxxx data xxxxxxxxx helps xxxxxx patterns xxxxxxx inconsistencies xxx assess xxxxxxxxxxxxx thus xxxxxxxx organizations xx determine xxx suitability xx the xxxx for xxxxxxxxx reporting xx business xxxxxxxxx This xxxx is x critical xxxxx phase xx any xxxx related xxxxxxxxxx such xx data xxxxxxxxxxx and xxxx cleansing xx it xxxxxx to xxx surface xxxxxx like xxxxxxx values xxxxxxxxxxxxxxx and xxxxxxxx formatting xxxxxxx the xxxxxxxx trends xx month xxx seasonThe xxxxx step x took xx preparing xxx ABC xxxxxxx for xxxxxxxx was xx clean xxx Order xxxxxx column xx Power xx Desktop xxxxx the xxxxx Query xxxxxx I xxxxxxx that xxx order xxxxxxx contained xx extra xxxxxxxxx C xxxx prevented xxxxxxxxx analysis xx I xxxxx changed xxx column x data xxxx to xxxx I xxxx removed xxx extra x using xxxxxxxxx Replace xxxxxx leaving xxxx numeric xxxxxxxxxx Once xxx cleaning xxx complete x reconverted xxx column xx a xxxxx Number xxxx type xx standardize xxx order xxxxxxx and xxxx them xxxxx for xxxxxxx filtering xxx creating xxxxxxxxxxxxx Next x created x Month xxxxxxxx from xxx Order xxxx column x used xxx Column xxxxxx from xxxxxxxx and xxxxx sample xxxxx names xxxx January xxx February xxxxxxxx Power xx to xxxxxxxxxxxxx generate xxx month xxxxxx This xxxx was xxxxxxxxx because xxxxxx a xxxxx variable xxxxxxx me xx analyze xxxxx trends xxxx time xxxxxxx I xxxxx a xxxxxx column xxxxx a xxxxxxxxxxx Column x named xxx new xxxxxx Season xxx defined xxxxxxxxxx so xxxx December xxxxxxx and xxxxxxxx were xxxxxxxx Winter xxxxx April xxx May xx Spring xxxx July xxx August xx Summer xxx all xxxxx months xx Fall xxxx allowed xx to xxxxxx each xxxxxxxxxxx a xxxxxx based xx its xxxxx month xxxxx will xxxx in xxxxxxxxx seasonal xxxxx patterns xx month xxx seasonPeak xxxxx are xx the xxxxx of xxxxx and xxxxxxxx has xxx lowest xx can xxxx observe xxxx highest xxxxx are xx spring xxx lowest xxxxx are xx Fall xx genderFrom xxx bar xxxxx we xxx say xxxx Men xxxxxx more xxxx women xx regionFrom xxx filled xxx we xxx observe xxxx florida xxx the xxxxxxx sales xxx Maine xxx the xxxxxx sales xx customers xxxx and xxxxxxx childrenFrom xxx Chart xx can xxxxxxx that xxxxx is xxxxxx equal xxxxx cost xxx customers xxxx and xxxxxxx children xxxxxxxx of xxxxx and xxxxxxxxxxx productsSince xxx dataset xxxxxxxx all xxxxxx each xxxxx has xxx own xxxx and xxxxxxxxxxxxxx products xxx same xx true xxxx looking xx seasonal xx monthly xxxxx The xxxx also xxxxxxxxxxx large xxxx we xxxxx it xxxx by xxxxxx or xx whether xxxxxxxxx have xxxxxxxx or xxx Therefore x have xxxxxxx out xxx five xxxxxxx items xxx bottom xxxx selling xxxxx as x whole xxx five xxxxxxx items xxx Reuben xxxxxxxx Cincinatti xxxxxx Penne xxxx Vodka xxxxxxx Water xxx American xxxx Suey xxxxxx five xxxxxxx items xxx Tasty xxxxxxxxx Chocolate xxxx Sphagetti xxx Whole-Wheat xxxxx and xxxxx Rings xxxxxxx the xxxxxxxxxxxxx of xxxxxxxx The xxxxxx generating xxx highest xxxxxxx are xxxxxxxxxx Florida xxxxx New xxxx and xxxx while xxx lowest xxxxxxx comes xxxx Maine xxxxxxxx Vermont xxxxx Dakota xxx Puerto xxxx Revenue xx season xx highest xx Spring xxxxxxxx by xxxxxx Summer xxx Fall xxx months xxxx the xxxxxxx revenue xxx April xxxxx February xxxxxxx and xxxxxx whereas xxxxxxx December xxxxxxxx June xxx September xxxx the xxxxxx revenue xxxx customers xxxxxxxxxxx slightly xxxx revenue xxxx female xxxxxxxxx Orders xxxx customers xxxx children xxxxxxxx in xxxxxxxxxx higher xxxxxxx compared xx those xxxxxxx children x Qualitative xxxxxxxxx - xxxxxxxxxx state xxxx category xxxxxx customer xxxxx Season xxxxx purchase xxxxxxxxxxxxxxxxxx variables x item xxxxx cost xxxxxxx quantity xxxx price xxxxx total xxxx B x created x clustered xxx chart xx display xxxxxxx across xxx merged xxxxxxx s xxxxxxxxxxxx footprint xxxxxxx is xxxxxxxxxxxx in x few xxxxxx rather xxxx evenly xxxxxxxxxxx Florida xxxxxx out xx an xxxxxxx contributing x disproportionately xxxxx portion xx the xxxxx revenue xxxxxxxx of xxxxxxx checks xx item xxxxxxxx and xxxx of xxx shows xxxx PM xxxxxx consistently xxxx higher xxxxxx than xx orders xxxxxxxx pizza xxx sandwiches xxxxxxxxxx the xxxx to xxxxxxx while xxxxxxxxx generate xxx lowest xxxxxx This xxxxxxxxx that xxxxxxxxxxxxxxxx tend xx place xxxxxx or xxxx expensive xxxxxx which xxx inform xxxx planning xxx promotion xxxxxxxxxx Next x used x stacked xxx chart xx determine xxxxxxxx demographics xx gender xxx whether xxxx have xxxxxxxx The xxxxx shows xxxx the xxxxxxxxxxxx is xxxxxx equal xxxxxx all xxxxxxx indicating xxxx the xxxxx has xxxxx appeal xxx likelihood xx a xxxxxxxx having xxxxxxxx remains xxxxxx consistent xxxxxxxxxx of xxxxxx Additionally xxx data xxxxx seasonal xxxxxx with x significant xxxxx in xxxxx and x noticeable xxx in xxxx again xxxxxxxx till xxxxxx and xxxx fell xxxx in xxxxxxx C xxxxxxxx and xxxxxxxxxxxxx by xxxxx Florida xxxxxxxxx the xxxxxxx revenue xxxxx Maine xxxxxxxxxxx the xxxxxx Average xxxx Cost xxxxxxxxx have xxx highest xxxxxxx item xxxx whereas xxxxx has xxx lowest xxxx of xxx Sales xxx higher xx the xxxxxxxx compared xx evenings xxxxxxxxxx strong xxxxxxxxx or xxxxx demand xxxxxxx Trends xxxxxx shows xxx highest xxxxxxx sales xxxxx December xxxxxxx the xxxxxx D xxxxxxx Statistics xxx Total xxxxx Value xxxx Standard xxxxx Median xxxx Standard xxxxxxxxx Sample xxxxxxxx Kurtosis xxxxxxxx Range xxxxxxx Maximum xxx Count xxxxx order xxxxx data xx right xxxxxx Skewness xxxx a xxx very xxxxx orders xx can xx seen xx the xxxx and xxxxxxx values xxxx of xxx orders xxx smaller xxxxxxxxx around xxx median xx The xxxxxxx range xx values xx large xxxxxxx that xxxxx is xxxxxxxxxxx in xxxxxxxx spending 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 xx PROJECTPart xxx Stakeholder xxxxxxxxxxxx and xxxxxxxxxxx Analysis xxxxx ApplicationDepartment xx Business xxxxxxxx New xxxxxxxxx University xxx Descriptive xxxxxxxx AnalyticsAfter xxxxxxxx and xxxxxxxxxxxx the xxxx in xxxx One x progressed xx more xxxxxxxx analytics xx Milestone xxx by xxxxxxxxxxx and xxxxxxxxxx key xxxxxxxxxxx requirements xxx purpose xx this xxxxx is xx generate xxxxxxxxxxx insights xxxx will xxxx the xxxxx ABC xxx leadership xxxx understand xxx current xxxxx of xxx newly xxxxxx organization xxx use xxxxx insights xx develop x data-driven xxxxxxxxxx profitability xxxx Identifying xxxxxxxxxxxx requirementsUsing xxx Stakeholder xxxxxxxxxxxx Document x identified xxx primary xxxxxxxxxxxxxxxxx and xxxxxxxxxx their xxxxx into xxxxxxxx analytical xxxxxxx that xxxxxxx decision-making xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx UseRegional xxxxxxxxxxxxxxxxxxxxx Sales xxxxxxxxxxxxxxxxxxx high- xxx low-performing xxxxxxx to xxxxx staffing xxxxxxxxx and xxxxxxxxxxxxxxxxxx MarketingDirectionMarketingSeasonal xxxxxxxxxxxxxxxxxxxx promotions xxx campaigns xxxxxxx monthly xxx quarterly xxxxxx trends xxxxxxxxxxxx and xxxxxxxxxxxxxxxxxxxxx SegmentationTo xxxxxxxxxx the xxxxxxxx behaviour xxxxxxx the xxxxxxx as xxxx between xxxxxxx without xxxxxxxx familiesProduct xxxxxxxxxx t xxxxxxxxxxxxxxxxxxxxxxxxxxxx EvaluationAn xxxxxxxxxx of xxxxxxxxxxx betweencustomers xxxx and xxxxxxx children xx guide xxx creation xx family-oriented xxxx deals xx well xx veg xx non-veg xxxxx mealsFinanceDirectorFinanceProfitabilityanalysisPrioritize xxxxxxxxxxx items xxxxxxxxxx andregions xxx investment xxx analysis xxxx be xxxxxxxx on xxxxxxxxxxx requirements xx clearly xxxxxxxxxxxxxxxxxxx are xxxxxxxxx to xxx success xx any xxxxxxxx If xxx requirements xxx not xxxx understood xxxx the xxxxxxxxx the xxxxxxxx is xxxxxxxx to xxxx the xxxxxxxx objectives xxxx the xxxxxxxxxxxx are xxxxxxxxxx the xxxx can xx sliced xxx structured xxxxxxxxxxx For xxxxxxx toanalyze xxxxxxx sales xxxxxx I xxxxx first xxxx to xxxxxx a xxxxxx that xxxxxxxxxx the xxxxx of xxxx sale xxxxxxxxx if xxx goal xx to xxxxxxx sales xx time xx day x column xxxxxxxxxxxxxx AM xxx PM xxxxx be xxxxxxxxx Descriptive xxxxxxxx methodsK-means xxxxxxx analysis xx Regression xxxxxxxx vs xxxxxxxx Tree xx Random xxxxxx The xxxxxxxx will xx done xx three xxxxx Primary xxxx Data xxxxx and xxxxxxxxxxx BenefitCriteriak-meansRegressionAnalysisDecision xxxxxxxxxxxxxxxxxxxxxxxx GoalsDiscovery xxxxxxxxxx theClassifies xxxxxxxxxxxxx by xxxxxxxxxxxxxxxxxxxxxxxx journeypredictivethem xxxxxxxxxxx ausing x flow-accuracy xxxxxxxxxx based xxxxxxxxxxxxxxxx showingcombiningsimilarity xxxxxxxxxx and xxxxx path xxxxxxxxxxxxxxxxxx likeindependentcustomer xxxxxxxxxxxxx treesregion xxx Education xxxxxxxxxx reach xx outcome xxxxxx n x into xxxxxxxxxx modelData xxxxxxxxxx on xxxxxxxxxxxx the xxxx isSince xxxxxxxxxxxxxx sets xxxxx predict xxxxxxxxx is xxxxxxxxxxxxxxxxxx needs x basebe xxxxxxx thetraining xxxx together xxxxxx or xxxxx has xx beuses xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Forsubsets xx datawithout xxxxxxxxxxxxxx toexample xxxxx variablesa xxxxxxxxxxxx the xxxxxxxxxxx outto xxxxxxxxx next xxxxx which xxxxxxxxxxxxxxxxxxxxx previousspends xxxx month xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx howVisualizes xxxxxxxxxxx highBenefit xxxxxxxxxxxx sales xxxxxxxxx ofpredictionPersonas xxxxxx if xxxxxxxxxxxxxxxxx butexample xxxxx ofspecific xxxxx isdecisionslimitedfamilies xxxxxxxxxxxxxxxxxx oninterpretabilitychildren xxxxxxxxxxx splits xxxxxx it xxxxxxxxxxx like xxxxxx orsuitable xxxxxxxxxxxxxxxxxxxx descriptivebusinessanalysisModel xxxxxxxxx for xx analysis xxxxxx cluster xxxxxxxx is xxxx suited xxxxx for xx analysisSince xxx joint xxxxxxxxxx team xxxxx to xxxxxxxxxx the xxxxxxx state xx merged xxxx from xxx newly xxxxxx business xxxx need xx independent xxxxxxxx on xxxxxxx factors xxx leadership xx currently xxx focused xx prediction xxx next xxxxx sales xx figuring xxx the xxxxxxxxxxx of xx outcome xxxx they xxxxxxxxxxxxx in xx to xxxxxxx the xxxx to xxxxxxxxxx the xxxxxxx position xxxxx states xxxxx the xxxx or xxxxx the xxxxx which xxxxxxxx works xxxxx gender xxxxx month xxx Sharda xx al xxxxxxxxxx is xxxxxxxxx used xxxx the xxxxxxxxxx team xxxxx to xxxxxxx the xxxxx or xx predict xxx next xxxxxx sale xxxxxxxx tree xxxx help xxxx understand xxx probability xx an xxxxxxx If xxxx had xx choose xxxxx project xx invest xx out xx a xxxxxxxxx of xxxxxxx they xxxxx use xxxxxxxx treen xxx probability xxxxxx However xxxxx they xxx only xxxxxxxxxx in xxxxxxxxxxxxx the xxxxxxxx entity xxx to xxxx out xxxxxx like xxxxx category xx build xxxx which xxxxx to xxxxx on xxxxx item xx market xxxx etc xxxx k-mean xxxxxxx will xxxx them xxxxxxxxxx this xxxxxx cluster xxxxxxxxxxxxx different xxxxxxx models xxx differences xxxxxx below xxx three xxxxxx used xxx the xxxxxxxxx behind xxxx Cluster xxxxxx Conducted xxxxx For xxx first xxxxx I xxxx item xxxxx cost xxx quantity xxxxxxx count xxx automatic x was xxxxxxxx on xxxxxx vs xxxxxxxx to xxxxxxxx high- xxxxx vs xxxxxxxxxxxx customers xxxxx - xxx this xxxxx I xxxx item xxxxx cost xxxxxxxx and xxxx price xxxxxxx count xxx set xx I xxx focusing xx Price xxxxxxxxxxx to xxx the xxxxxx of xxxxxxxxx items xxxxxxxxx by xxx customers xxxxx Third xxxxx was xxxxx on xxxx total xxxx customer xxxx children xxx gender xxxxxxxx payee x was xxxxxxxx on xx Demographic xxxxxxx to xxxxxx specific xxx and xxxxxxx Development xxxxxxxxxxx needs xxxxxxx count xxx set xx Best xxxxx and xxx According xx me xxx Model xxxxx is xxxxx on xxxxxxxxxxx data xxxxx fits xxx scenario xxxxxxx this xxxxx incorporates xxx stakeholder xxxxxxxxxxxx regarding xxxxxx and xxxxxx status xxxxxx the xxxxxx directly xxxxxxxxxx for xxx three- xxxx plan xxxxx cluster xxxxxxx the xxxx discrete xxxxxxxxx Discrete xx this xxxxxxxxxxxxxx to xxxxxxxx clusters xxxx have xxxxxx or xx overlaps xxx the xxxxxxxxxx are xxxxx Going xx this xxx first xxxxx is xxx most xxxxxxxx as xxx number xxxxxxxxxxx are xxxxxx Variables xxxx customer xxxx children xxx gender xxxxxxxx payee xxxxx was xxxx in xxxxx will xxxx overlaps xxxxx is xxxx evident xx the xxxxxxx images xxx These xxxxxxxxx Inform xxx Analysis x have xxxx the xxx data xxx transformed xx into xxxxxxxx Persona xx that xxx joint xxxxxxxxxx team xxx do xxxxxxxx marketing x The xxx Manager xxx use xxx High-Spending xxxx cluster xx market xxxxxxx individual xxxxx while xxx Value- xxxxxxx Families xxxxxxx receives xxxxxxx for xxxxxxx Menu xxxxxxxxxxxx - xxx Product xxxxxxxxxxx Manager xxx identify xxxxx products xxx favorites xxx expand xxxxx offerings xxxxxxxxx planning xxx identify xxxxxxx and xxxxxx staffing xxx inventory xxxxxxx statistics xxx each xxxxxxxxxxxxxx statistics xxxx each xxxxx from xxxxxxx is xxxxxxxxx belowModel xxxxxxx belowInputs xxx ClusteringVariables xxx of xxxx Total xxxxxxx of xxxxxxxx With xxxxxxxx Gender xxxxxxxx PayeeLevel xx Detail xxxxxx Customer xxxxxxxxxxxx NormalizedSummary xxxxxxxxxxxxxxxxx of xxxxxxxx Number xx Points xxxxxxxxxxxxx Sum xx Squares xxxxxxxxxxxx Sum xx Squares xxxxx Sum xx Squares xxxxxxxxxxx Common xxxxxxxxxxxxxx ofSum xx ItemSum xx CustomerGender xxxxxxxxxx CostWith xxxxxxxxxxxxxxxxxxxxxxxxxxxx MCluster xxxxxxxx MCluster xxxxxxxx FNotClustered xxxxx Summary xxxxxxxxxxx for xxxxxxxxxxxxxxxxxxx Sum xx Item xxxxx CostSum xx Customer xxxx Children xxxxxx Customer xxxxxxxxxx of xxxxxx Not xxxxxxxxxxxxxxxxx NormalizedSummary xxxxxxxxxxxxxxxxx of xxxxxxxx Number xx Points xxxxxxxxxxxxx Sum xx Squares xxxxxxxxxxxx Sum xx Squares xxxxx Sum xx Squares xxxxxxxxxxx CommonClustersNumber xx ItemsSum xx Item xxxxx CostSum xx Customer xxxx ChildrenGender xxxxxxxxxxxxxxxxxxxx MCluster xxxxxxxx FCluster xxxxxxxxxxxxx Model xxxxxxx belowInputs xxx ClusteringVariables xxx of xxxx Total xxxxxxx of xxxxxxxx With xxxxxxxx Gender xxxxxxxx PayeeLevel xx Detail xxx AggregatedScaling xxxxxxxxxxxxxxxxx DiagnosticsNumber xx Clusters xxxxxx of xxxxxx Between-group xxx of xxxxxxx Within-group xxx of xxxxxxx Total xxx of xxxxxxx CentersMost xxxxxxxxxxxxxxxxxxxx of xxxxxxxx of xxxx Total xxxxxxx of xxxxxxxx With xxxxxxxxxxxxxx CustomerPayeeCluster xxxxxxxx MCluster xxxxxxxx FNotClustered xxxxxxxxxx the xxxxxxxxx Customer xxxxxxxx Based xx the xxxxxxxxx request xxx subgroups xxx as xxxxxx Subgroup x - xxxx item xxxxx cost xxx high xxxxxxxx This xxxxx consists xxxxxxxxx of xxxxxxxxx with xxxxxxxx often xxxxxxxx during xx hours xxxxxxxx B x Moderate xxxx total xxxx but xxx quantity xxxxx are xxxxxxxxx single xxxxxx often xxxx visiting xxxxxx AM xxxxx hours xxxxxxxx C x Low xxxx total xxxx and xxxx frequency xxxxx customers xxx low- xxxxxx items xxx are xxxxxx sensitive xx price xxxxxxx Compare xxx Contrast xxxxxxxxx comparing xxx subgroups xxxx us xxxx interesting xxxxxxxx Revenue xx volume x Subgroup x drives xxx highest xxxxxx size xxxxxxx Check xxx may xxxxx less xxxxxxxxxx than xxxxxxxx C xxxxxxxxxxx divide xxxxxxxx are xxxxxxx customers xxxxx subgroup x are xxxxxxx customers xxxxxxxxxxx Clusters xxxxxxx in xxxxxxxxxx happen xxxx data xxxxxx share xxxxxxx characteristics xx multiple xxxxxxxxx Some xxxx customers xxx have xxxx a xxxxxxxxxxx purchase xxx might xxxx been xxxxx to xxx family xxxxx Families xxxxxxxx above xxx average xxx not xx necessarily xxxxxx children xxxxxxxxxx Stakeholder xxxxx My xxxxxxxx has xxxxxxxxxxx raw-data xxxx actionable xxxxxx helping xxx stakeholders xx developing x three-year xxxxxxxx plan xx defining xxxxxxxxx and xxxxxxxxxxx personas xxx marketing xxxx can xxxxxx better xxxxxxxxx By xxxxxxxxxxxx menu xxxxx that xxxxx volume xxx margins xxxxxxxxx team xxx focus xx product xxxxxxxxxxxx and xxxxxxxxxxxxxxx volume xx different xxxx of xxx can xxxx resource xxxxxxxx ReferencesIBM xxxxxxxxx What xx clustering xxx https xxx ibm xxx topics xxxxxxxxxxxxxxxx n x What xx a xxxxxxxx tree xxxxxx Data xxxxxxx https xxx oracle xxx artificial-intelligence xxxxxxxxxxxxxxxx what-is-a-decision-tree xxxxxx R xxxxx D xxxxxx E xxxxxxxx intelligence xxxxxxxxx and xxxx science x managerial xxxxxxxxxxx th xx Pearson

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    Answer: Drive-Thru Service Times at McDonald’sDiscussion Questions: 1.) After returning from the training session at Hamburger University, a McDonald’s store......
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    Question: Complete exercises 9.3, 9.13, 9.14, 9.25, 9.48, 9.55 For problems requiring computations, please ens......
    Answer: 1) Complete exercises 9.3, 9.13, 9.14, 9.25, 9.48, 9.55. 2) For problems requiring computations, please ensure that your Excel file includes the assoc......
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    Question: Discussion 8.1 Discussion Title: Analysis of Variance (ANOVA) – Part 3 Provide an example of an expe......
    Answer: A person wanted to know if level of education in a person’s life has an impact on individual's interest in politics. He also wanted to find out if......
    Question: It is expected that you will present a ‘professional’ looking document for this project. Be sure to......
    Answer: Ans 1) n=870 p=0.791 Number of selected Americans of Mexican Ancestry, x = 39% of 870 = 339 α=0.01 Null hypothesis, H 0 = the selection process is not......
    Question: For each measure of central tendency, provide an example of when the measurement would be most usefu......
    Answer: The usefulness of measurements of central tendency depends on the particular set of numbers as well as the type of data under consideration. Mean is t......
    Question: 1. Confidence interval for μ when σ is known Suppose n = 9 people are selected at random from a larg......
    Answer: 1a) 18 of my intervals contains µ but anything above 14 is also acceptable. 1b) 0.90 1c) Yes. The theoretical width is 2*z 0.05 (3/) =2*z 0.05 (3/3) =......
    Question: 10.7 According to a recent study, when shopping online for luxury goods, men spend a mean of $2,401,......
    Answer: 10.7 Sol: a) The null and alternative hypothesis to determine whether the mean amount spend is higher for men than for women:null hypothesis, H 0 : m......
    Question: 8.3 Why is it not possible in Example 8.1 on page 256 to have 100% confidence? Explain. 8.19 The fil......
    Answer: 8.3) It is not possible to have 100% confidence because to calculate 100% confidence, one must run the trials infinite number of times and not just fo......
    Question: Is fuzzy logic probability? Explain (Please state the references)...
    Answer: Fuzzy Logic is a mathematical logic that helps in solving problem with the help of given data and input to arrive at the most accurate conclusion by a......
    Question: The Case of the Accountants The president of a large accounting company is required to send employee......
    Answer: 1) The hypothesis that the company is investigating is whether its employees would be more productive if they undergo traditional way of learning or o......
    Question: After you get your readings and video-watching finished this week, peruse the Internet for a news ar......
    Answer: The graph uses nominal variables for each variable. The mental health condition of children is categorized based on three factors: anxiety, depression......
    Question: Using the reading and video this week: Define what a sample is Write about how a sample is supposed......
    Answer: Define what a sample is According to the video in the module (5.7 Sampling Error | Quantitative Methods | Sampling | UvA, n.d.), a sample is a subset......
    Question: Enter "name of the specific business/economic measurements/indicators in the title Name of the publi......
    Answer: Enter "name of the specific business/economic measurements/indicators in the title:" Income in the United States: 2023 Name the publication and Copy/p......
    Question: Remember our theories from earlier weeks: Human capital, Occupational Segregation, and Discriminatio......
    Answer: The two concepts I selected from week one are Human capital theory and labor market discrimination theory. Human capital is the theory that we have in......
    Question: Look at these few example of fundamental probability problems and see if you can identify which appr......
    Answer: 745/1000 = 0.745 = 74.5% P(heads) = 1/2 = 0.50 = 50% P(x>50000) = 686/925 = 0.742 = 74.2% 1- 0.742 = 0.258 = 25.8%...
    Question: Take a look at the the following problems: 1. A study of 500 Mercy University students revealed that......
    Answer: students who do not commute = 500 - 425 = 75 Probability is 75/500 = 0.15 = 15% The probability of a correct birthday guess is 1/365 = 0.0274 = 27.4%......
    Question: Sometimes it helps to visualize the probabilities more easily if you construct a table like the foll......
    Answer: P (no degree or has debt) = 65/100 + 63/100 - 53/100 = 0.75 =75% P (no degree or no debt) = 65/100 + 37/100 - 12/100 = 0.90 = 90% P (College degree or......
    Question: Why is it that gamma can show direction of an association and Lambda does not? Hint: think about the......
    Answer: Gamma uses only ordinal variables and can show the direction of an association between those variables, whereas lambda uses nominal variables. Given t......
    Question: This discussion is about the usage of the DECISION ANALYSIS/TREE in real life situations. This decis......
    Answer: Name of the publication in the title: The Regional Educational Laboratory Program (REL): Publications https://ies.ed.gov/ncee/edlabs/regions/appalachi......
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    Question: Use the information from your project to create a PowerPoint of not more than three to five slides t......
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    Answer: I selected option 1.I selected to watch Khan Academy unit 13 videos because they offer highly engaging and interactive lessons explaining statistical......
    Question: Enter Name of the decision making situation in the title. For example, it could be purchasing raw ma......
    Answer: Decision-making: what type of mobile phone should I buy?Decision alternatives: premium, high-quality, and expensive mobile Mid-range with balanced fea......
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    Question: Step 1: Choose your Analysis For each option the goal is for you to conduct a chi-square equal propo......
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    Answer: For this week's discussion, I selected option 1. I chose the Crash Course Statistics - Regression - Episode #32 video as my resource for this week's s......
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    Answer: Week 9 Data analysis State and visualize I selected option 5. The research question is, “Does the number of credits impact the amount of time students......
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    Answer: For this week's discussion, I selected option 1. I chose the Crash Course Statistics - Regression - Episode #32 video as my resource for this week's s......
    Question: Discussion Board Seven: Midterm PrepWe’ve covered a lot of terms and analyses. Just take a look: Ter......
    Answer: The name of the term/analysis. GammaWhat is tough for you to understand about it? I found Gamma to be challenging to understand the analysis. It is of......
    Question: Discussion Board Twelve: Help BoardThis week's discussion I am calling a help board. I like to use t......
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    Question: Discussion Board Eight: Guns and HomicideVery important this week is understanding the different int......
    Answer: What is a research Question?The research question is, "Are more guns linked to a higher homicide rate? (The Correlation between Guns and Homicide Rate......
    Question: Discussion Board Nine: Help BoardThis week's discussion I am calling a help board. I like to use the......
    Answer: This week's assignment, regression analysis, is quite challenging for me. I was unable to determine the slope and intercepts and also could not find o......
    Question: Discussion Board Ten: Help boardThis week's discussion I am calling a help board. I like to use thes......
    Answer: One thing i found challenging is to discuss the minimum expected count and how it impacts the chi-square. The concept of ensuring that no expected cel......
    Question: Discussion Board Eleven: Help BoardThis week's discussion I am calling a help board. I like to use t......
    Answer: This week, I found interpreting the t-test results and understanding the implications of Levene’s test particularly challenging. Differentiating betwe......
    Question: Discussion Board Thirteen: Help BoardThis week's discussion I am calling a help board. I like to use......
    Answer: I found the concept of R-squared a bit tricky this week, especially when considering how much of the variation in income is unexplained by the model.......
    Question: Discussion Board Fourteen: Crowdsourcing Studying for the FinalWe have learned a lot of terms since......
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    Question: ?Assignment One: Installing SPSS and loading ACS datasets Name: Click here to enter text.Use APA sty......
    Answer: Assignment One: Installing SPSS and loading ACS datasets Name: Markida MitchellUse APA style and formatting for all assignments, references, and citat......
    Question: No question updated...
    Answer: No answer updated...
    Question: Assignment Three: Bivariate analysis of gender and income Name: Part I: Use APA style and formatting......
    Answer: No answer updated...
    Question: No question updated...
    Answer: No answer updated...
    Question: Assignment Five: Using Gamma to analyze and interpret associations between two ordinal variables Par......
    Answer: Assignment Five: Using Gamma to analyze and interpret associations between two ordinal variables Part I: Use APA style and formatting for all assignme......
    Question: ?Assignment Six: Using Pearsons r and r2 to analyze and interpret associations between two interval/......
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    Question: SOCI 5314 Assignment 1 1. For each of the following variables, provide an operational definition at......
    Answer: No answer updated...
    Question: Discuss how the coefficient of determination and the coefficient of correlation are related and how......
    Answer: The coefficient of determination is the square of the correlation coefficient. It measures the proportion of variance in the dependent variable that i......
    Question: What are the economic costs of waiting lines? How might these costs be measured? What decision crite......
    Answer: Hello Professor and everyone else, I would like to add that the economic costs of waiting lines within the scope of linear programming tends to involv......
    Question: Describe a waiting line situation that you regularly face in your daily life. Explain how you would......
    Answer: Topic 7 DQ 1Hello professor and everyone, I got little held up in work, so I am little late to the discussion, however, getting to the chase – I wou......
    Question: Create a simple 0, 1 integer model for a situation or problem you face in your life. Is there an opt......
    Answer: Hello everyone and professor, according to my understanding from the weekly resource, I would like to add that binary integer programming is employed......
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    Answer: Hello everyone and professor, based on my current retirement plan and what I learned in this week, portfolio selection under higher moments extends be......
    Question: Describe a current problem facing your department, organization, or industry that would indicate the......
    Answer: Hello professor and everyone else, I would like to add that within the scope of logistical industry, minimizing missed/late deliveries could one of th......
    Question: Southwest Airlines and Whole Foods are businesses that practice conscious capitalism. Conduct resear......
    Answer: Hello professor and everyone, based on my understanding, Whole Foods Market exemplifies conscious capitalism through its commitment to stakeholder int......
    Question: Final Tech Live Weekly ReflectionLive ReflectionsEach week after our Tech Live session, you will com......
    Answer: ANOVA is used when there are a minimum of three, rather than two groups being tested for the mean. It is to determine whether there are any statistica......
    Question: Module 6 Discussion: Statistical AnalysisInstructions:This discussion will be completed in two parts......
    Answer: The purpose of the study was to see the effect of gender, grade, and previous psychological help experience on attitudes towards counseling. They comp......
    Question: Week 5 Tech Live ReflectionTech Live ReflectionsEach week after our Tech Live session, you will comp......
    Answer: In regards to the example of hypothesis to see if ice cream sales drive crime. The correlaton coefficient reflects how similar the measurements of two......
    Question: Module 5 Discussion: Correlated DataInstructions:This discussion will be completed in two parts, and......
    Answer: Discussion Questions:Give two examples where a pair of datasets are correlated with no causative relationship.Wearing shorts and hot weather are corre......
    Question: Week 4 Tech Live ReflectionTech Live ReflectionsEach week after our Tech Live session, you will comp......
    Answer: I seem to be able to understand the concepts better as the class goes on. I have been using my work to complete hypothesis tests to see how the result......
    Question: Module 4 Discussion: Type 1 and 2 ErrorsInstructions:This discussion will be completed in two parts,......
    Answer: Discussion Questions:Identify a situation where type-1 error is preferable to type-2 error and explain why it is preferable.Type 1 errors are A false......
    Question: Week 3 Tech Live ReflectionTech Live ReflectionsEach week after our Tech Live session, you will comp......
    Answer: I have to be honest to and say that I was almost confused about this next set of test data sets. I understood that the tails would be fatter but my br......
    Question: Module 3 Discussion: Confidence IntervalsInstructions:This discussion will be completed in two parts......
    Answer: Why do you think 95% is the threshold most commonly used for confidence intervals? It means that if the study was repeated multiple times with samples......
    Question: Week 2 Tech Live ReflectionWhat is Tech Live?Each week, we will have a Tech Live session. These sess......
    Answer: would like to say that my brain grasps z scores better than our last module. I am an individual that works out my math problems the long way, however......
    Question: Module 2 Discussion: Z ScoresInstructions:This discussion will be completed in two parts, and will g......
    Answer: How are z scores useful to researchers when analyzing data? It lets the researchers know where exactly the data falls within the distribution when cal......
    Question: Week 1 Tech Live ReflectionWhat is Tech Live?Each week, we will have a Tech Live session. These sess......
    Answer: The tech live session reconfirmed the notion that I will be needing some tutoring for class. I have not taken statistics since 2005 and I'm feeling re......
    Question: Module 1 Assignment 2: Components of a Controlled ExperimentOverview:For this assignment, you will p......
    Answer: Response to Peer Article Dogs and ASD CarersMPSY 5600 Statistics for Behavioral Sciences April 27, 2024This study describes the impact that dogs as pe......
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    Answer: Standard deviation is a measure of how spread out data points are from the mean. The process begins by calculating the mean, which is the sum of all d......
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    Answer: Probability is the likelihood or chance of an event happening. It’s calculated as the ratio of favorable outcomes to the total number of possible outc......
    Question: Write a post that does one of the following: Describe and explain one of the sampling techniques dis......
    Answer: I recently saw a histogram in an article analyzing the distribution of student test scores for a standardized exam. The histogram displayed score inte......
    Question: Write a post that does one of the following two options: Explain one of the graphical representation......
    Answer: One common measure of center is the mean, which is calculated by adding all the values in a dataset and dividing the sum by the total number of values......
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    Answer: The multiplication rule for counting is used to determine the total number of possible outcomes when there are multiple stages or choices in a process......
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    Answer: Question 1:List and define the four major components of a business intelligence system.The four components of Business Intelligence (BI) system are da......
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    Answer: Question 1: What is AI? List and discuss the drivers of AI.Artificial Intelligence (AI) is the development of computer systems capable of performing t......
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    Answer: Hadoop and Spark are both computer programs that help people work with very large amounts of data. They are alike because they can split big jobs into......
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    Answer: Assignment 1 MGT460: Business Intelligence and Analytics Question 1: List and comment on four analytics applications in the retail value chain.Invento......
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    Answer: Question 1Statistics is the process of collecting, analyzing and interpreting the data to make decisions. For instance, let’s say in a class of 50 stu......
    Question: Imagine you visit the same coffee shop every morning for a month and record the time it takes to rec......
    Answer: 1.In this example, we can see that the average wait time is 8 minutes. This means that we are just as likely to wait less than 8 minutes as we are to......
    Question: Scenario: A local bakery, known for its artisan bread, monitors operational data to improve its busi......
    Answer: The scenario carries the features of Poisson distribution because orders happen independently of each other. The event occurs at a known average rate......
    Question: Understanding Data Behaviour and Managing Variability Measures of Location and Central Tendency In a......
    Answer: In an uneven distribution, when a dataset is positively skewed, mean is greater than the median. When a dataset is negatively skewed, mean is less tha......
    Question: As part of your study, you visit the colorful city to collect and analyze data regarding its economi......
    Answer: The Central Limit Theorem (CLT) states that the distribution of the sample mean approaches a normal distribution as the sample size becomes large, reg......
    Question: Question 1 The following is a dataset containing student test scores: 45, 62, 75, 85, 90, 93, 95, 95......
    Answer: Question 145, 62, 75, 85, 90, 93, 95, 95, 100Range = maximum value - minimum value Range = 100 – 45 = 55We have 9 data points.1st quartile or 25th per......
    Question: Greenfield High School emphasizes experiential and hands-on approaches to learning probability conce......
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    Question: Imagine a medium-sized urban hospital emergency room aiming to enhance its understanding of patient......
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