# Model BuildingIntroductionIn data analytics, model building refers to assembling the needed data and analyzing it to address your identified problem.For this second course project assignment, you will

Model BuildingIntroductionIn data analytics, model building refers to assembling the needed data and analyzing it to address your identified problem.For this second course project assignment, you will completeone statistical analysis for your quantitative data and one content analysis for your qualitative data from the previous project assignment.Statistical and Content AnalysesWork with your group on developing the followinganalyses based on the data from your previous project assignment. You can schedule a time to work together in real time,or work on the analyses individually and provide each other with feedback. Statistical analysis: Identify the specific analysis type (such as an independent samples t test, paired sample ttest, or one-way ANOVA) and explain why it is appropriate. Assume that you have a sufficient sample size for your analysis (normally, you would need to conduct a power analysis to ensure this). Conduct the analysis in MicrosoftExcelor comparable software.Note:If you use software other than Excel, make sure the files that the program generates can be read in the courseroom (PDF, JPEG, XLS, and PNG formats should be acceptable). Present the results and explain if they are statistically significant or not. Content analysis: Explain the purpose of content analysis. Identify the qualitative data that you are analyzing and present the analysis. Identify at least one theme from your data, present quotes from the transcripts as examples, and explain the theme and how it helps you better understand the experiences of the teens. InstructionsOnce you have completed your groupanalyses, prepare this assignment individually.While most of this assignment should be written in paragraph format, it is appropriate to present some information (such as the results of the analyses) in tables. You should also present exploratory analyses and descriptive data (for example, a description of the group included in your analysis by race, gender, etcetera) in visual formats such as charts, graphs, and tables.This papershould include the following sections: Identification of the Problem:This should just be a brief recap from the first assignment. Quantitative Analyses: Identify the data and variable type (continuous orcategorical). Identify the specific type of statistical analysis. Present your results. Present the significance testing. Discuss possible implications of these findings. Qualitative Analyses: Identify the data. Present the results of your content analysis. Identify theme(s). Provide illustrative quotations for the theme(s). Discuss possible implications of these findings. This assignment should be 3–5 pages long. It should be written in narrative format with tables, charts, graphs, and so forth included as well. Writing should be well organized, free of mechanical errors (in grammar and punctuation), and in correct APA format.Examine the assignment scoring guide to be sure you have addressed all of the evaluation criteria.

Model BuildingIntroductionIn data analytics, model building refers to assembling the needed data and analyzing it to address your identified problem.For this second course project assignment, you will
5/30/2020 Model Building Scoring Guide https://courseroomc.capella.edu/bbcswebdav/institution/HMSV/HMSV5316/191000/Scoring_Guides/u07a1_scoring_guide.html 1/1 Model Building Scoring Guide Due D ate : U nit 7 P erc en ta g e o f C ours e G ra d e: 1 5% . CRIT E R IA N O N-P ER FO RM ANCE BASIC P R O FIC IE N T DIS TIN G UIS H ED P re sen t d ata in a d escrip tiv e f o rm at (fo r e xam ple , t a b le s, ch arts a n d g ra p hs). 2 0% D oes n ot p re se nt d ata in a ny d escrip tiv e fo rm ats . P re se nts s o m e d ata in a d escrip tiv e fo rm at, b ut n ot all o f it . P re se nts d ata in a d escrip tiv e fo rm at ( fo r exa m ple , ta ble s, c h arts a nd g ra phs). P re se nts d ata in a d escrip tiv e fo rm at a nd pro vid es e xp la natio n o f th e ta ble , c h art, g ra ph, or o th er fo rm at. Id en tif y a n aly ses co nducte d o n b oth q uan tit a tiv e a n d qualit a tiv e d ata . 20% D oes n ot id entif y a ny analy se s c o nducte d o n data . Id entiﬁ e s o ne ty p e o f analy sis c o nducte d o n data ( e it h er q ualit a tiv e o r quantit a tiv e ), b ut n ot b oth . Id entiﬁ e s analy se s co nducte d o n both q ualit a tiv e a nd quantit a tiv e d ata . Id entiﬁ e s a naly se s co nducte d o n b oth q ualit a tiv e a nd quantit a tiv e d ata a nd exp la in s w hy th e analy se s w ere s e le cte d fo r b oth . P re sen t c o nte n t an aly ses o f qualit a tiv e d ata . 20% D oes n ot d is cu ss co nte nt a naly se s o f qualit a tiv e d ata . D is cu sse s c o nte nt analy se s o f q ualit a tiv e d ata b ut d oes n ot p re se nt th e analy se s in a c le ar o r w ell- o rg aniz e d m anner. P re se nts c o nte nt analy se s o f qualit a tiv e d ata . P re se nts c o nte nt analy se s o f q ualit a tiv e d ata , a nd e xp la in s th e analy se s a nd p ossib le im plic a tio ns o f th e re su lt s o f th e a naly se s. P re sen t s ta tis tic al an aly ses o f quan tit a tiv e d ata . 20% D oes n ot d is cu ss sta tis tic a l a naly se s o f quantit a tiv e d ata . D is cu sse s s ta tis tic a l analy se s o f q uantit a tiv e d ata , b ut th e r e su lt s a re n ot c le ar. P re se nts s ta tis tic a l analy se s o f quantit a tiv e d ata . P re se nts s ta tis tic a l analy se s o f quantit a tiv e d ata , in te rp re ts th e r e su lt s o f th e s ta tis tic a l a naly se s, a nd d is cu sse s th e im plic a tio ns o f th e re su lt s . C om munic ate in a m an ner t h at is c le ar an d w ell o rg an iz e d . 20% C om munic a te s in a m anner th at is s o d is o rg aniz e d a nd/o r h as so m any e rro rs in w rit in g m ech anic s th at th e id eas c a nnot b e unders to od. C om munic a te s in a m anner th at is n ot w ell- o rg aniz e d o r h as e nough erro rs in w rit in g m ech anic s th at it in te rfe re s w it h u nders ta ndin g th e id eas pre se nte d. C om munic a te s in a m anner th at is c le ar and w ell o rg aniz e d. C om munic a te s in a c le ar, w ell- o rg aniz e d m anner th at is fr e e o f erro rs in w rit in g m ech anic s a nd A PA fo rm at.
Model BuildingIntroductionIn data analytics, model building refers to assembling the needed data and analyzing it to address your identified problem.For this second course project assignment, you will
5/30/2020 Riverbend City: Data Modeling https://media.capella.edu/CourseMedia/HMSV5316/data-modeling/transcript.asp 1/3 Riv e rb end C it y ® A ctiv it y D ata M od elin g In tro d uctio n M ento r T a lk C onclu sio n I n tro d uctio n Welc o m e b ack t o y o ur v ir tu al in te rn sh ip a t t h e R iv e rb end C om munit y A ctio n C ente r! S o f a r, y o u h ave b een in tro d uce d t o y o ur o ve ra rc h in g p ro je ct o f u sin g d ata a n aly tic s t o h elp R C AC eva lu ate t h e e ff e ctiv e ness o f t h eir R ub y La ke T e en H om ele ssn ess T a sk F o rc e , a n d t h en t a lk e d t o s ta ff m em bers t o g et a s e nse o f th e t y p es o f q uestio ns y o u s h o uld b e t r y in g t o a n sw er w it h d ata . The n ext s te p w il l b e t o t a ke a c lo se r lo ok a t d ata m od elin g . It ‘s t im e f o r a n o th er m eetin g w it h y o ur m ento r, B re nd a. M ento r T a lk R ive rb end C it y Com munit y Act io n C ente r: M ento r’s O ff ice C heck in w it h y o ur C AC M ento r, B re nd a. Com e o n in ! I h o p e y o u’r e f in d in g y o ur in te rn sh ip in te re stin g a n d ch alle ng in g s o f a r. S o , s in ce t h e la st t im e w e t a lk e d , y o u s h o uld h ave a m ore s o lid s e nse o f w hat t h e o ve ra ll p la n is h ere , a n d w hat k in d o f q uestio ns w e n eed t o b e an sw erin g t h ro ug h d ata a n aly tic s. T hat’s t h e f ir s t s te p f o r a n y p ro je ct lik e 5/30/2020 Riverbend City: Data Modeling https://media.capella.edu/CourseMedia/HMSV5316/data-modeling/transcript.asp 2/3 th is , a n d n o w it ’s t im e t o m ove f o rw ard . The n ext t h in g I’d lik e t o d o is t o t a lk t o y o u a lit t le a b out d ata m od elin g . it ’s r e ally im porta n t; it ’s a f o und atio nal t h in g , a n d w e h ave t o m ake s u re e ve ry th in g ’s s tra ig ht b efo re w e c a n p ro ce ed . Li ke , if y o u g et y o ur m od elin g d one c o rre ctly , s u b se q uent s te p s a re t h at m uch e asie r a n d m ore lo g ic a l. G et it w ro ng , a n d t h e w ho le t h in g is lia b le t o b lo w u p o n yo ur f a ce w hen y o u’r e h alf w ay t h ro ug h t h e p ro ce ss a n d y o u r e aliz e y o u ca n ’t a ctu ally a n sw er t h e q uestio ns y o u’r e t ry in g t o a n sw er. F ir s t o ff , w hat is t h is d ata m od elin g t h in g ? It ’s a lit t le a b stra ct. A d ata m od el is a c o nce p tu al r e p re se nta tio n o f t h e s tru ctu re t h at’s g oin g t o g uid e y o ur d ata b ase . O ne w ay t o t h in k o f it is t h at it ’s a n a tte m pt t o p ro p erly r e p re se nt r e alit y t h ro ug h d ata . M ayb e “ co nce p tu al b lu ep rin t” is a g ood w ay t o t h in k o f it . Y o ur d ata m od el n eed s t o a cco unt f o r t h e n atu re o f t h e d ata y o u’r e g ath erin g , t h e in stit u tio nal r u le s a t p la y in u sin g it , a n d t h e o rg an iz a tio n o f th e d ata it s e lf . T hin k t a b le s, c o lu m ns, r e la tio nsh ip s, c o nstra in ts , a n d t h at so rt o f t h in g . There a re t h re e t y p es o f d ata m od els w e’r e g oin g t o t h in k a b out: re la tio nal, s ta tis tic a l, a n d p re d ic tiv e . T hese a re a ll d if f e re nt a p p ro ach es t o s tru ctu rin g a n d h an d lin g d ata , d ep end in g o n w hat k in d o f in fo rm atio n yo u’r e c o lle ctin g a n d w hat y o u w an t t o d o w it h it . If y o u h ave e xp erie nce u sin g d ata b ase s, a r e la tio nal d ata m od el m ig ht se em lik e t h e m ost in tu it iv e a n d f a m il ia r a p p ro ach . In t h is s e tu p , d ata is – o f c o urs e – s to re d in a r e la tio nal d ata b ase . B asic a lly , y o ur c la ssic d ata b ase se tu p : a s e rie s o f in d exe d t a b le s w it h o ne-to -o ne a n d o ne-to -m an y re la tio ns s e t b etw een t h em , g ove rn e d b y k e ys. Y o u c a n m an ip ula te t h e d ata a n d r e p ort o n it u sin g s o m eth in g lik e S Q L. Next, s ta tis tic a l d ata m od elin g . It liv e s u p t o it s n am e, m ore o r le ss- y o u’r e a m assin g a n d s to rin g la rg e a m ounts o f d ata a lo ng s o m e p re – id entif ie d v a ria b le s, w it h t h e id ea t h at y o u c a n a g gre g ate t h ese d ata p oin ts a n d s u b je ct t h em t o s ta tis tic a l a n aly sis . T his a llo w s y o u t o id entif y p atte rn s a n d c o rre la tio ns, a n d p ossib ly id entif y t re nd s t h at m ay co ntin ue in to t h e f u tu re . A nd f in ally , I’d lik e t o t a lk a b out p re d ic tiv e d ata m od ellin g . Y o u c a n t h in k o f it a s a m od ellin g a p pro ach t h at’s k in d o f a m ean s t o a n e nd … where t h e e nd is b ein g a b le t o p re d ic t f u tu re o utc o m es b ase d o n t h e d ata y o u’r e g ath erin g . In t h is c a se , y o u s tru ctu re y o ur d ata m od el a ro und a s e rie s o f p re d ic to rs t h at y o u’v e id entif ie d ; in o th er w ord s, v a ria b le s t h at are lik e ly t o h ave a n e ff e ct o n t h e o utc o m es t h at y o u’r e c o nce rn ed w it h . T here ’s a n in te rp la y h ere b etw een p re d ic tiv e a n d s ta tis tic a l d ata 5/30/2020 Riverbend City: Data Modeling https://media.capella.edu/CourseMedia/HMSV5316/data-modeling/transcript.asp 3/3 mod ellin g in t h at o ne in fo rm s t h e o th er; a s t h in g s m ove f o rw ard , f o r in sta n ce , y o u m ig ht u se a s ta tis tic a l d ata m od el t o e va lu ate t h e eff e ctiv e ness o f y o ur p re d ic tiv e m od el. I h o p e t h is h elp s! N ext, w e’ll b e t a lk in g a b out p utt in g s o m e o f t h is s tu ff in to p ra ctic e . C onclu sio n Yo u h ave c o m ple te d t h e R iv e rb end C it y : D ata M od elin g a ctiv it y . L ic e nse d u nd er a C re ativ e C om mons A ttrib utio n 3 .0 L ic e nse (h ttp s:/ /c re ativ e co m mons.o rg /lic e nse s/b y-n c-n d /3 .0 /)

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