Home Uncategorized Lab Assignment Using R Programming Basics ( Harrisburg Uni Business Analytics Class)

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Lab Assignment Using R Programming Basics ( Harrisburg Uni Business Analytics Class)

have to write small chunks of code for questions using R Studio ( open source software) .

Professor has given a .rmd file to import and .csv file

—
title: “Introduction to Data Analytics 2”
author: “Enter Your Name”
date: “`r Sys.Date()`”
output: word_document
—
“`{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
“`
# Part 1: Experiment Design
*Title*: Consumer Pseudo-Showrooming and Omni-Channel Product Placement Strategies
*Abstract*: Recent advances in information technologies (IT) have powered the merger of online and offline retail channels into one single platform. Modern consumers frequently switch between online and offline channels when they navigate through various stages of the decision journey, motivating multichannel sellers to develop omni-channel strategies that optimize their overall profit. This study examines consumers’ cross-channel search behavior of “pseudo-showrooming,” or the consumer behavior of inspecting one product at a seller’s physical store before buying a related but different product at the same seller’s online store, and investigates how such consumer behavior allows a multichannel seller to achieve better coordination between its online and offline arms through optimal product placement strategies.
Participants in the study were grouped into the following categories:
– Where_bought: Where they ended up purchasing an item: bought at the store, bought online.
– Who_bought: If they bought from the same or a different retailer.
Each participant was then measured on:
– Money: how much money they spent in dollars on the product.
– Time: how much time (in minutes) they spent looking at the product online.
1) What would be one possible null hypothesis based on this study?
2) What would be one possible alternative hypothesis based on this study?
3) Who are they sampling in this study?
4) Who is the intended population in this study?
5) Give an example of type 1 error based on this study (do not just define, explain in context how it might have happened here).
6) Give an example of type 2 error based on this study (do not just define, explain in context how it might have happened here).
# Part 2: Use the 04_data.csv to complete this portion.
“`{r import-data}
“`
1) For each IV list the levels (next to a, b):
a. Where bought:
b. Who bought:

2) What are the conditions in this experiment?
3) For each condition list the means, standard deviations, and standard error for the conditions for time and money spent. Please note that means you should have several sets of M, SD, and SE. Be sure you name the sets of means, sd, and se different things so you can use them later.
“`{r descriptives}
“`
4) Which condition appears to have the best model fit using the mean as the model (i.e. smallest error) for time?
5) What are the df for each condition?
“`{r df}
“`
6) What is the confidence interval (95%) for the means?
“`{r conf-interval}
##money
##time
“`
7) Use the MOTE library to calculate the effect size for the difference between money spent for the following comparisons (that means you’ll have to do this twice):
“`{r MOTE}
##Store versus online when bought at the same retailer
##Store versus online when bought at a different retailer
“`
8) What can you determine about the effect size in the experiment – is it small, medium or large?
9) How many people did we need in the study for each comparison?
“`{r pwr}
##Store versus online when bought at the same retailer
##Store versus online when bought at a different retailer
“`

online different

online

online same

different

store different

store same

id	where_bought	who_bought	money	time
S001					online			different	26.6338578858899	9.13437382034485
S002	26.6980645802869	10.7110187543102
S003	21.7915430797955	7.54917763500253
S004	24.3197520351232	11.4854992711761
S005	26.8652444027408	6.06806714548163
S006	28.3211467192245	10.7960205325475
S007	24.3952341506648	9.25649724044805
S008	27.7438852463786	9.7425548953096
S009	29.7510137284262	12.1437270548306
S010	23.9763453501744	7.82114435503473
S011	33.7041187642229	12.6752639251869
S012	27.0313957127428	8.12923224221272
S013	24.2555290443709	8.99583269697184
S014	27.4755844430037	4.39182079933195
S015	20.9451657756667	4.70408039295258
S016	24.1237425831501	9.030125215727
S017	24.621189055815	5.76476816919555
S018	22.9196035153465	9.13177807139844
S019	26.2640994348853	11.6840316039784
S020	26.9167810546957	9.60777740899513
S021	23.7600578918229	16.257933837999
S022	27.0932637744839	8.01252918191938
S023	20.9488972142606	11.3657374095602
S024	25.0461720272518	14.1150103513042
S025	28.1840630558848	11.9834126239295
S026	19.6153423272067	7.73821785301041
S027	24.7230202962023	7.20428920075425
S028	21.0494604309436	14.2021335317519
S029	25.4744982321302	9.15202725664325
S030	24.5377557537475	14.3244707853614
S031	22.7769070469431	9.87850307801882
S032	28.0209115954334	12.4321056078688
S033	22.1492283839824	8.21991152729358
S034	25.1146386897319	10.9161750849965
S035	23.2735059227742	12.9306723549629
S036	26.5127128586694	9.19841910396691
S037	23.9717482846685	7.23163623906855
S038	24.6278154837364	9.85899494362701
S039	24.8108724200641	12.4133379765899
S040	27.1236314272374	12.1712522428627
S041	20.6979665373221	9.75742122078014
S042	30.1889675955877	5.41058003116128
S043	29.20637505502	13.1339311416397
S044	27.4442437594662	9.21383361294197
S045	28.5336733885215	12.1484183709461
S046	25.9333067129415	9.05999006695183
S047	21.5898321679393	16.5540894452336
S048	23.567808635248	7.74778955985242
S049	26.5133317310444	11.4179393402066
S050	27.7946535153421	9.49881234612035
S051					same	29.5915123727636	12.0160862169985
S052	22.8239002368424	8.76323130541632
S053	18.9185340392224	8.12197878303725
S054	27.2257448099936	13.489742903678
S055	28.1122895821991	8.4126662873122
S056	28.717077256496	8.88356237294456
S057	27.8331782158118	9.41999239359871
S058	27.6185084353857	13.3653258978984
S059	28.1970658298529	5.72559021425552
S060	25.7031004091875	15.233586147897
S061	24.1608388042578	6.92486102233899
S062	28.0915976998618	7.76595879205889
S063	25.468025040953	11.3556349321265
S064	19.0283604922526	6.05857591043916
S065	26.4921225992758	10.9750239090419
S066	25.0032675543396	8.52393376259764
S067	18.8047378559369	9.89352908980559
S068	20.0000777475355	14.2912825478376
S069	25.9746084510792	13.3067328060332
S070	24.4448728841351	14.0857100997382
S071	26.4115712954471	9.81650932661332
S072	20.8019691710934	5.44219063887768
S073	27.6206910593667	8.63460069115352
S074	28.7903439228519	11.7526838677719
S075	25.7368765435678	14.9603070915376
S076	20.8361157378062	10.4422714528152
S077	24.8390616786572	10.0339271906695
S078	25.0087827107313	4.48041618427022
S079	29.4022908428194	7.48972422008042
S080	25.3595821247205	11.0715817399273
S081	23.2432067192493	11.6505848554331
S082	26.3352381070632	10.754123824925
S083	22.8942603330265	12.3452708343052
S084	28.5595366279711	10.3338699496935
S085	19.2868335202358	7.25759722794056
S086	23.1410330531046	8.88563560697443
S087	31.5083711785091	10.1064005244241
S088	30.6559701535217	11.5548410571131
S089	24.4452013385284	14.4956913083719
S090	25.8567638885433	11.938905028563
S091	22.3623958288935	15.3068239114011
S092	30.7310901243284	9.19925565771227
S093	23.8036142764023	5.57155612418023
S094	24.8642080171031	14.0956396591484
S095	26.8293262412639	12.0925690817223
S096	25.2886807349224	5.3086296945967
S097	21.6651200073533	11.5482916682272
S098	24.5624412212258	10.8437919536752
S099	22.739648860705	14.7784136199065
S100	20.7767263554032	17.0476416366789
S101					store	36.5674292085614	19.3128063997977
S102	36.4860768468645	15.9852133047303
S103	32.3934039906625	16.5037179466346
S104	30.1082308364647	17.6413921224069
S105	35.9709927975974	9.47594360738458
S106	38.2017380904325	18.0234560091018
S107	33.586021748648	14.3395622276681
S108	36.6110250241306	14.7204063272586
S109	30.5214762172327	11.2640811814601
S110	37.5103141157352	14.2891667894745
S111	31.0104062685241	13.2694186549699
S112	35.8494709257341	18.1205345124146
S113	30.5560175227265	13.0127642789752
S114	39.4603188531771	13.8368217711644
S115	37.4985483502724	14.0019484048962
S116	35.1295728875228	12.3271840549955
S117	34.0580216804068	13.5099120845554
S118	36.2397607623393	18.7084571936982
S119	34.9352706637836	15.0763986318552
S120	34.9200912647306	16.7547641989366
S121	32.802897980648	18.552353314574
S122	36.2961516051492	20.4566206144267
S123	35.5499827268131	12.2646751204554
S124	34.8014769922444	14.7192145470593
S125	33.118645413426	17.1967452743653
S126	32.8178068171523	17.0477592925946
S127	32.4383196426593	12.2332018757
S128	36.7628634686191	14.8920183483645
S129	35.7042352323444	12.895153579495
S130	34.4532529399774	11.5469519836704
S131	30.0875376292386	19.9435881066943
S132	32.6396703330719	13.1069456446454
S133	34.3227273177621	16.4186416476158
S134	37.1975955729826	17.8316427387481
S135	33.8921187285311	16.5838484113569
S136	43.372639694729	13.7049089600222
S137	40.4335949853929	17.8786553665543
S138	33.3689513681429	17.3287968988538
S139	33.7586404315292	15.4457619216326
S140	33.9962197641272	12.8410613260913
S141	40.5956045064899	13.3703847219204
S142	38.3731901018128	15.8703323740339
S143	31.9851247911933	22.5297203237507
S144	34.7534109945876	11.9559284892922
S145	34.5291048039468	14.8109846626243
S146	37.8825055508877	14.2443562869663
S147	28.5664557177061	17.7320298141791
S148	32.1053090501446	13.8159447190145
S149	30.7480583909562	14.7684284673541
S150	30.1709092327641	14.583468192486
S151	38.3145992766622	25.6843251871811
S152	35.977533528364	19.9098295212768
S153	31.9983668624314	21.4904732898637
S154	35.5338465082163	19.5569308618033
S155	38.9884643532336	28.8988196686792
S156	37.1394206939286	21.8493115882129
S157	37.3376480932058	19.7097841172254
S158	36.1103897409019	19.4398508081636
S159	31.5922916679136	18.9811893645558
S160	36.5671137576897	21.5195894100332
S161	31.3229515844468	10.649392881633
S162	35.955864224037	17.3438920622332
S163	33.5842932186157	17.8374120189582
S164	33.0876377902142	21.4280662237454
S165	32.4504115467907	16.3124737540879
S166	35.2972883649505	17.7245495189159
S167	42.8043005858908	21.8826923121375
S168	33.1116300316291	16.88571333919
S169	36.4418890040389	22.0262251786266
S170	30.8194290808603	16.4351274910385
S171	35.3272134520367	20.5349922334547
S172	35.7448624232384	19.9459324027589
S173	32.8455721404874	22.0459937781166
S174	33.8146220009309	20.3149925595881
S175	32.5489841820772	19.113709775439
S176	37.8507933598695	19.9849872299324
S177	35.5827942263307	22.9944429682435
S178	33.0945266160901	24.8744517225429
S179	32.0129640713813	22.9533295087531
S180	38.5865172310977	21.0435263429519
S181	36.1481773647223	20.1735210525272
S182	37.2632605414916	20.9339901556896
S183	33.030384239598	18.4348267964221
S184	30.1682965471319	15.2203849458589
S185	31.8691900221278	21.3759873382721
S186	33.0503235942542	20.5950715606904
S187	34.5334655343599	20.1321860843052
S188	35.5523248827376	20.6386734486269
S189	28.3008167540472	21.8995617103346
S190	35.8801187573836	17.0449986815216
S191	36.6502704511443	23.0971659675701
S192	36.0754768942651	19.2128796695169
S193	34.0607784966997	22.8580003064515
S194	35.051269684341	19.0315321858253
S195	39.3389995342696	18.4331264478453
S196	34.8187950204155	23.6842505550202
S197	33.1248598899597	17.9525878654782
S198	32.2057503022078	19.9947913999799
S199	39.4276752726642	16.2605388479024
S200	35.6509862427144	18.6548151817379

—
title: “Statistical Modeling and Graphing”
author: “Enter Your Name”
date: “`r Sys.Date()`”
output: word_document
—
“`{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
“`
*Citation*: Trends in Internet-based business-to-business marketing
*Abstract*: The Internet is changing the transactional paradigms under which businesses-to-business marketers operate. Business-to-business marketers that take advantage of the operational efficiencies and effectiveness that emerge from utilizing the Internet in transactions are out performing firms that utilize traditional transactional processes. As an example, Dell computers, by utilizing business-to-business processes that take advantage of the Internet, has gained the largest market share in the PC business when compared to traditional manufacturers such as Compaq. This paper first examines the genesis of the Internet movement in business-to-business markets. The long-term impact of the increase of business-to-business utilization of the Internet on the marketing theory and marketing process is then discussed. Finally, managerial implications and directions for future research are highlighted.
Dataset includes:
1) Business marketing focus – traditional or forward thinking.

2) Internet use – low, medium, or high levels of business marketing use on the internet.

3) Time _ 1 – sales scores at the first measurement time.

4) Time _ 2 – sales scores at the second measurement time
On all of these questions, be sure to include a coherent label for the X and Y axes. You should change them to be “professional looking” (i.e. Proper Case, explain the variable listed, and could be printed in a journal). The following will be assessed:
1) Is it readable?

2) Is X-axis labeled appropriately?

3) Is Y-axis labeled appropriately?

4) Is it the right graph?

5) Do the labels in the legend look appropriate?

6) Are there error bars when appropriate?
We won’t grade for color of bars or background color, but you should consider that these things are usually printed in black/white – so be sure you know how to change those values as well as get rid of that grey background.
Please note that each subpoint (i.e. a, b) indicates a different chart.
“`{r starting}
“`

1) Make a simple histogram using ggplot:
a. Sales at time 1
“`{r hist1}
“`
b. Sales at time 2
“`{r hist2}
“`
2) Make a bar chart with two independent variables:
a. Business focus, internet, DV: sales at time 2
“`{r bar1}
“`
3) Make a bar chart with two independent variables:
a. Time (time 1, time 2), Business focus, DV: is sales from time 1 and 2
“`{r bar2}
“`
4) Make a simple line graph:
a. Time (time 1, time 2), DV: is sales from time 1 and 2
“`{r line}
“`
5) Make a simple scatterplot:

a. Sales at Time 1, Time 2
“`{r scatter1}
“`
6) Make a grouped scatterplot:
a. Sales at time 1 and 2, Business focus
“`{r scatter2}
“`

forward high

forward

forward medium

forward

forward low

high

traditional high

traditional medium

traditional low

id	biz_focus	internet	time.1	time.2
S001								forward			high	7.54461929529665	9.71145794011495
S002	7.56602152676232	10.2370062514367
S003	5.93051435993184	9.18305921166751
S004	6.7732506783744	10.4951664237254
S005	7.62174813424695	8.68935571516054
S006	8.10704890640817	10.2653401775158
S007	6.79841138355494	9.75216574681602
S008	7.91462841545952	9.9141849651032
S009	8.58367124280874	10.7145756849435
S010	6.65878178339148	9.27371478501158
S011	9.90137292140763	10.891754641729
S012	7.67713190424759	9.37641074740424
S013	6.75184301479029	9.66527756565728
S014	7.82519481433456	8.13060693311065
S015	5.6483885918889	8.23469346431753
S016	6.70791419438336	9.67670840524234
S017	6.87372968527167	8.58825605639852
S018	6.3065345051155	9.71059269046615
S019	7.4213664782951	10.5613438679928
S020	7.63892701823191	9.86925913633171
S021	6.58668596394098	12.0859779459997
S022	7.69775459149464	9.33750972730646
S023	5.64963240475352	10.4552458031867
S024	7.01539067575059	11.3716701171014
S025	8.06135435196159	10.6611375413098
S026	5.20511410906889	9.24607261767014
S027	6.90767343206744	9.06809640025142
S028	5.6831534769812	11.4007111772506
S029	7.15816607737674	9.71734241888108
S030	6.84591858458249	11.4414902617871
S031	6.25896901564771	9.95950102600627
S032	8.00697053181114	10.8107018692896
S033	6.04974279466079	9.40663717576452
S034	7.03821289657728	10.3053916949988
S035	6.42450197425806	10.9768907849876
S036	7.50423761955648	9.73280636798897
S037	6.65724942822282	9.07721207968952
S038	6.87593849457879	9.95299831454234
S039	6.93695747335472	10.8044459921966
S040	7.70787714241247	10.7237507476209
S041	5.56598884577403	9.91914040692672
S042	8.7296558651959	8.47019334372043
S043	8.40212501833999	11.0446437138799
S044	7.81474791982205	9.73794453764732
S045	8.17789112950716	10.716139456982
S046	7.31110223764717	9.68666335565061
S047	5.8632773893131	12.1846964817445
S048	6.52260287841601	9.24926318661747
S049	7.50444391034815	10.4726464467355
S050	7.93155117178069	9.83293744870678
S051					medium	5.53050412425453	7.67202873899949
S052	3.27463341228081	6.58774376847211
S053	1.97284467974081	6.37399292767908
S054	4.74191493666454	8.16324763455933
S055	5.03742986073303	6.4708887624374
S056	5.23902575216534	6.62785412431485
S057	4.94439273860393	6.80666413119957
S058	4.87283614512856	8.12177529929948
S059	5.06568860995097	5.57519673808517
S060	4.2343668030625	8.74452871596565
S061	3.72027960141926	5.974953674113
S062	5.03053256662061	6.25531959735296
S063	4.15600834698432	7.45187831070885
S064	2.00945349741752	5.68619197014639
S065	4.49737419975861	7.32500796968062
S066	4.00108918477985	6.50797792086588
S067	1.93491261864564	6.96450969660186
S068	2.33335924917849	8.43042751594587
S069	4.32486948369307	8.10224426867773
S070	3.81495762804504	8.36190336657939
S071	4.47052376514903	6.93883644220444
S072	2.60065639036447	5.48073021295923
S073	4.87356368645555	6.54486689705117
S074	5.26344797428397	7.58422795592395
S075	4.24562551452259	8.65343569717921
S076	2.61203857926874	7.14742381760506
S077	3.94635389288572	7.01130906355649
S078	4.00292757024377	5.16013872809007
S079	5.4674302809398	6.16324140669347
S080	4.11986070824017	7.3571939133091
S081	3.41440223974978	7.55019495181102
S082	4.44507936902107	7.25137460830833
S083	3.29808677767549	7.78175694476839
S084	5.18651220932369	7.11128998323117
S085	2.09561117341194	6.08586574264685
S086	3.38034435103487	6.62854520232481
S087	6.16945705950303	7.0354668414747
S088	5.88532338450724	7.51828035237105
S089	3.81506711284279	8.49856376945731
S090	4.28558796284775	7.64630167618766
S091	3.12079860963116	8.76894130380037
S092	5.91036337477615	6.73308521923742
S093	3.60120475880078	5.52385204139341
S094	3.95473600570103	8.36521321971612
S095	4.60977541375463	7.69752302724078
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