This assignment is suppose to be an R studio sheet which is then knited into an R markdown file and word doc. The instructions are attached in greater detail. It is for a linear Regression & Analysis course that uses R studio
-Data Assignment - A Case Study in R, with Communication and Visual Report General Instructions: · This data assignment is due on Wed. April 10 (end of day, 11:59 pm). After this time/date, there is a 50% penalty per day. · This is an individual assignment. You need to complete it independently without help from anyone else. · Once you put your name on it, you are responsible for all answers. · Your statistical report should clearly demonstrate all steps of hypothesis test/confidence intervals. Interpretation of any statistical output is required and must accompany all calculations, especially those involving testing procedures and confidence intervals. · Use R Markdown and knit your document as a word or pdf document. Projects where is code copied and pasted in Word will have a 50% penalty. · Organize your R code script with comments into a report. · Include interpretations of statistical analyses. · Label each question properly, include headings. · Use R/R Studio only. Projects solved in Excel will not be accepted. · There is a page limit of about 10 pages for this report. · START EARLY! Score (out of 100):_____________________________ NAME_____________________ Signature (I worked individually):___________________ USE CreditCard.csv data posted in Brightspace Credit Card Company. A Business Analytics Lab Report. Suppose you work for a credit card company and were assigned an analytics project for which you decided to apply regression analysis methods. Your supervisor asked you to investigate differences in credit card balance between males and females, differences in average balance between different ethnicities Caucasians, Asians and African Americans (AA) and address differences in credit card balances between married and non-married. In addition, you were also asked to investigate the effect of Income, Age, and Education on credit card balances. The following observations for a number of potential customers were recorded and stored in a data file named “CreditCards”: balance (average credit card debt for a number of individuals), as well as quantitative predictors: age, education (years of education), marital Status (married or not), income (thousands of dollars), limit (credit limit). (Data file, “CreditCards.csv” is posted on Brightspace). Please provide a self-contained managerial report to present your research findings. To help you organize your writing, I list some points below. For example, your report should incorporate full work on the items below. 1. Do an exploratory analysis of your data (know your data, variable types, generate visuals). 2. Generate a matrix scatterplot (with interpretation). 3. Propose a simple linear regression model to investigate Credit Card Balances between males and females, ignoring the other variables at the moment. Define any dummy variables that you use, interpret coefficient estimates in context, assess model utility using coefficient of determination, and the standard error. Clearly address the Question: Is there a significant difference between average card balances between males and females? 4. Suppose you want to investigate the relationship between credit card balance and ethnicity. Set up the regression model and thoroughly explain the model output and state conclusions derived from it. Is there any statistical evidence of a real difference in credit card balance between the ethnicities? 5. Suppose you want to investigate the relationship between credit card balance and marital status. Set up the regression model and thoroughly explain the model output and state conclusions. Is there any statistical evidence of a real difference in credit card balance between the married and non-married individuals? 6. Suppose you want to investigate the relationship between credit card balance and Income, Age, Education, Gender, Ethnicity and Marital status. Set up a regression model. Discuss this model and derive conclusions from it. 7. Suppose you would like to predict card balance for a new (unobserved case). Set your particular values for predictors. For example: 57 year old non-married Caucasian female customer with an income of 90,000 and 21 years of education (you do not need to use this example, pick your own). Find the interval designed to hold a fraction (95%) of the values of the response (credit card balance) for particular predictor values in this regression. Interpret. 8. Suppose your supervisor is asking about other possible insights, and a possibility of formulating interaction models, that may potentially contribute to his/her understanding of the market better. By doing quick model explorations with this data set, can you suggest other possibilities that might offer insightful analysis in the credit card business? CreditCards IncomeLimitAgeEducationGenderMarriedEthnicityBalance 14.89136063411MaleYesCaucasian333 106.02566458215FemaleYesAsian903 104.59370757111MaleNoAsian580 148.92495043611FemaleNoAsian964 55.88248976816MaleYesCaucasian331 80.1880477710MaleNoCaucasian1151 20.99633883712FemaleNoAA203 71.4087114879MaleNoAsian872 15.12533006613FemaleNoCaucasian279 71.06168194119FemaleYesAA1350 63.09581173014MaleYesCaucasian1407 15.04513116416MaleNoCaucasian0 80.6165308577FemaleYesAsian204 43.6826922499MaleYesCaucasian1081 19.14432917513FemaleNoAA148 20.08925255715FemaleYesAA0 53.59837147317FemaleYesAA0 36.49643786915FemaleYesAsian368 49.576384289FemaleYesAsian891 42.0796626449MaleNoAsian1048 17.728606316FemaleNoAsian89 37.34863787217FemaleNoCaucasian968 20.10326316110MaleYesAA0 64.0275179488MaleYesAA411 10.74217575715FemaleNoCaucasian0 14.0943232516FemaleYesAA671 42.47136254412FemaleNoCaucasian654 32.79345344416MaleNoAA467 186.634134144114FemaleYesAA1809 26.81356115516FemaleNoCaucasian915 34.1425666475FemaleYesCaucasian863 28.94127334316MaleYesAsian0 134.18178384813FemaleNoCaucasian526 31.36718293010MaleYesCaucasian0 20.1526462514FemaleYesAsian0 23.3525584912FemaleNoCaucasian419 62.41364577111FemaleYesCaucasian762 30.0076481699FemaleYesCaucasian1093 11.79538992510FemaleNoCaucasian531 13.64734614714MaleYesCaucasian344 34.9533275414FemaleNoAA50 113.65976596615MaleYesAA1155 44.15847636613FemaleYesAsian385 36.92962572414FemaleYesAsian976 31.86163752516FemaleYesCaucasian1120 77.3875695012FemaleYesCaucasian997 19.53150436416FemaleYesAsian1241 44.64644314915MaleYesCaucasian797 44.52222527215MaleYesAsian0 43.47945694913MaleYesAA902 36.36251834915MaleYesAA654 39.70539692720MaleYesAA211 44.20554413212MaleYesCaucasian607 16.30454666610MaleYesAsian957 15.3331499479FemaleYesAsian0 32.9161786608FemaleYesAsian0 57.147427918FemaleYesAsian379 76.27347796514FemaleYesCaucasian133 10.35434807017MaleYesCaucasian333 51.87252948117FemaleNoCaucasian531 35.5151983520FemaleNoAsian631 21.23830895910FemaleNoCaucasian108 30.6821671777FemaleNoCaucasian0 14.13229987517MaleNoCaucasian133 32.16429377915FemaleYesAA0 1241602814FemaleYesCaucasian602 113.82997043813FemaleYesAsian1388 11.18750996916FemaleNoAA889 27.84756197815FemaleYesCaucasian822 49.50268195514MaleYesCaucasian1084 24.88939547512MaleYesCaucasian357 58.78174028112FemaleYesAsian1103 22.93949234718FemaleYesAsian663 23.98945233115MaleNoCaucasian601 16.10353904510FemaleYesCaucasian945 33.01731802816MaleYesAA29 30.62232936816MaleNoCaucasian532 20.93632543015FemaleNoAsian145 110.96866624511FemaleYesCaucasian391 15.35421016514MaleNoAsian0 27.3693449409FemaleYesCaucasian162 53.4842638315MaleNoCaucasian99 23.67244336311MaleNoCaucasian503 19.22514333814FemaleNoCaucasian0 43.5429066911MaleNoCaucasian0 152.298120664112FemaleYesAsian1779 55.36763403315MaleYesCaucasian815 11.74122715912FemaleNoAsian0 15.564307578MaleYesAA579 59.537518529FemaleNoAA1176 20.19157674216MaleYesAA1023 48.49860404716MaleYesCaucasian812 30.73328325113MaleNoCaucasian0 16.47954352616MaleNoAA937 38.00930754515FemaleNoAA0 14.0848554617FemaleYesAA0 14.31253825917MaleNoAsian1380 26.06733887417FemaleYesAA155 36.29529636814FemaleNoAA375 83.85184944718MaleNoCaucasian1311 21.15337364111MaleNoCaucasian298 17.97624337016FemaleNoCaucasian431 68.71375825616MaleNoCaucasian1587 146.18395406615MaleNoCaucasian1050 15.84647685312FemaleNoCaucasian745 12.03131825818FemaleYesCaucasian210 16.81913377415MaleYesAsian0 39.1131897212MaleNoAsian0 107.98660336414MaleYesCaucasian227 13.56132613719MaleYesAsian297 34.53732715717FemaleYesAsian47 28.57529596011FemaleNoAA0 46.00766374214MaleYesCaucasian1046 69.25163863012FemaleYesAsian768 16.48233264115MaleNoCaucasian271 40.4424828818FemaleNoAA510 35.17721176216FemaleNoCaucasian0 91.36291134717MaleYesAsian1341 27.03921614017FemaleNoCaucasian0 23.01214108116MaleNoCaucasian0 27.24114026715FemaleYesAsian0 148.0881578313MaleYesCaucasian454 62.60270568411FemaleNoCaucasian904 11.80813007714FemaleNoAA0 29.56425293012FemaleYesCaucasian0 27.57825313415FemaleYesCaucasian0 26.42755335015FemaleYesAsian1404 57.20234117211FemaleNoCaucasian0 123.29983768917MaleNoAA1259 18.14534615615MaleYesAA255 23.79338215612FemaleYesAA868 10.72615684619MaleYesAsian0 23.28354434913MaleYesAA912 21.45558298012FemaleYesAA1018 34.66458357715FemaleYesAA835 44.47335008116FemaleNoAA8 54.6634116708FemaleNoAA75 36.3553613359MaleYesAsian187 21.37420737411FemaleYesCaucasian0 107.84110384877MaleNoAA1597 39.83160453212FemaleYesAA1425 91.87667543310MaleYesCaucasian605 103.89374168417MaleNoAsian669 19.63648966410FemaleNoAA710 17.39227483214MaleYesCaucasian68 19.52946735114MaleNoAsian642 17.05551105515FemaleYesCaucasian805 23.85715015616MaleYesCaucasian0 15.18424206911FemaleYesCaucasian0 13.4448864410MaleYesAsian0 63.93157282814FemaleYesAA581 35.86448316613FemaleYesCaucasian534 41.41921202411FemaleNoCaucasian156 92.11246123217MaleNoCaucasian0 55.05631553116MaleYesAA0 19.5371362349FemaleYesAsian0 31.81142847513FemaleYesCaucasian429 56.25655217216FemaleYesCaucasian1020 42.35755508312FemaleYesAsian653 53.31930005313MaleNoAsian0 12.23848656711FemaleNoCaucasian836 31.35317058114MaleYesCaucasian0 63.80975305612MaleYesCaucasian1086 13.67623308016FemaleNoAA0 76.78259774412MaleYesAsian548 25.38345274611MaleYesCaucasian570 35.69128803515MaleNoAA0 29.40323273714FemaleYesCaucasian0 27.4728203211FemaleYesAsian0 27.3361793612FemaleYesCaucasian1099 34.7722021579MaleNoAsian0 36.9344270639FemaleYesCaucasian283 76.34846976018MaleNoAsian108 14.88747455812MaleYesAA724 121.834106735416MaleNoAA1573 30.13221685217MaleNoCaucasian0 24.0526073218MaleYesCaucasian0 22.37939653414FemaleYesAsian384 28.31643912910FemaleNoCaucasian453 58.02674996711FemaleNoCaucasian1237 10.63535846916MaleYesAsian423 46.10251808112MaleYesAA516 58.9296420669FemaleYesAA789 80.86140902915FemaleYesAsian0 158.889115896217FemaleYesCaucasian1448 30.4244423014FemaleNoAA450 36.47238065213MaleNoAA188 23.36521797515MaleNoAsian0 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