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ALY6010 Discrete probability and normal distributions
创建日期：2024-04-24 18:02:34
标签： ALY6010
Discrete probability and normal distributions

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ALY6010 Module 1 Project

Discrete probability and normal distributions
Overview and Rationale
This assignment is designed to provide you with hands-on experience in performing descriptive statistical
methods on a data set. The data set is provided in an Excel workbook and contains a wide range to data types
that you will need to work with.
Assignment Summary
Using the data provided in the attached Excel workbook, apply the methods of graphical and numerical
descriptive statistics.
Follow the instructions in the project document to analyze the data presented in the Excel workbook. Then
complete a report summarizing your data analyses.
Important note on the report: for this project, your report will be a HTML file produced using R Markdown.
Important note 2: I understand that some students are still learning R and R Markdown. If you are in this
group, this week deadline is flexible, and you can present your report up to five days after the deadline.
Files to submit: Important to remember, for this project you must submit two files:
• Your R Markdown File.
• Your HTML report.
Tasks to complete before starting your project.
1. Install the latest versions of R and R Studio on your computer.
(Read file: 01a R Install, create folder and project.ppt)
2. Create a folder on your computer named “ALY6010 R Project” and a subfolder named “DataSets”.
3. From R Studio, create an R Project for this class using the “ALY6010 R Project” folder you created
above. (Read file: 01a R Install, create folder and project.ppt)
4. Learn how to import data sets into R using the strategy requested by your instructor.
(Read file: 03 R Import data sets.ppt)
5. Learn how to use R Markdown. We will use only basic codes to produce the HTML outcome reports.
(Read file: R Markdown Introduction.ppt)
6. Save the file “M1data_carsales.xlsx” inside your DataSets folder.
7. Create an R Markdown file inside your ALY6010 R Project, name this file: Project1_myname.Rmd.
(Read file: R Markdown Introduction.ppt)
8. Import the data set into R Studio using the strategy you leant above and present the code using an
initial R chunk.
9. Do not present install.package() codes on your report. If you need to install any new package, do it
directly in the R Studio console.

Create an initial R chunk to activate your libraries and import your data sets. Use the following header on this R
chunk: {r message=FALSE, warning=FALSE}
Some libraries to include on your libraries R Chunk. If you do not have them, install the packages in the
console.
library(readxl)
library(tidyverse)
library(dplyr)
library(DT)
library(RColorBrewer)
library(rio)
library(dbplyr)
library(psych)
library(FSA)

Report starts here
Title.
Create a Title to your report with the report’s name (Project 1 Report), name and CRN of the class, your name,
your instructor’s name, and date you submit the report. Here, there is an example:

Introduction.
Create a title for your Introduction section. Here, there is a code example:

(A) Write some sentences to present general information about car sales market, global and in India.
Here there are some websites you can read, these are examples, find others if you prefer:

(B) Write a paragraph describing and explaining the importance of discrete and continuous probability
distributions.
(C) Write a sentence describing the data set you are about to use.
Analysis section.
Task 1
If you don’t know the dplyr::select() and psych::describe() codes, this will be a good opportunity to learn.
• Create an R Chunk.

•Start with the name of the data set, then using the pipes %>% , apply code dplyr::select() to select only the
variables Efficiency, Power_bhp, Seats, Km, and Price.
• Using a second pipe, apply code psych::describe(), nothing inside the parenthesis.
• Run the code. Two things that should call your attention: descriptive statistics are in the columns, not in the
rows, and there are too many decimals. Correct these issues.
• Using another pipe, enter code t() to transpose values. Run code and observe.
• Using another pipe, enter code round(2) to reduce decimals to only 2.
• Using another pipe, enter code knitr::kable() to improve table presentation.
Present the table on your Report.
Write some observations about the code strategy you just learnt.
Task 2.
• Prepare and present a bar plot to show the frequencies of variable location.
• Prepare and present a bar plot to show the frequencies of variable fuel type.
• Prepare and present a bar plot to show the frequencies of variable transmission.
• Prepare and present a bar plot to show the frequencies of variable owner.
Important: Use code par(mfrow=c(2,2)) to organize your bar plots presentation in a 2x2 matrix.
• Improve your graphs presentation with clear y- and x- axes labels, colors.
Task 3
Create a table with the variable location on the rows, and present their corresponding frequencies, cumulative
frequencies, percentages, and cumulative percentages.
If you have decimals, always reduce them to 2 or 3 only.
Follow these steps:
• Create a table to present locations and its frequencies.
• Convert table using as.data.frame()
• Rename columns: Var1 to Location and Freq to Frequency.
• Use code mutate() to create three new columns (these are new calculated fields):
• The cumulative frequencies, name column: CumFrequency.
• The percentages, name column: Percentage
• The cumulative percentage, name column: CumPercentage
Present it using a table library of your choice: library(DT) or library(knitr).
Optional: to apply kable, practice these codes to present your table, you will need to install package
kableExtra.

knitr::kable(digits = 2, caption = "Task 3 Table") %>%
kable_classic(full_width = FALSE, font_size = 12)

Task 4.
Repeat the codes used for task 3, this time present frequencies, cumulative frequencies, percentages, and
cumulative percentages for variable owner.
Task 5
• Prepare horizontal box plots and one histogram to display the data distribution of numerical kilometers.
• Use the code par(mfrow=c(2,1), mai=(1,1,1,1)) at the beginning of the R chunk.
• mfrow will present the two figures one of top of the other as a group, in this case, c(2,1) indicates 2 rows and
1 column. Mai will change the margins of your figures, bottom, left. Top. Right. Play with the mai numbers to
observe changes.
• Remove the title of your graphs by using main = NA.


Remember to always make observations after each task.
Task 6.
Similar to task 5, this time present the box plot and histogram for variable price.
Task 7
Prepare and present a box plot to display the price distribution per location.
Your figure must contain several boxes inside.
Provide your figure with a good presentation format.
Remember to always make observations after each task.
Task 8
Similar to task 7, prepare and present a box plot to display kilometers distribution per owner.
Task 9
Apply and present the outcomes of code boxplot.stats() for variable kilometers.
Explain the information obtained with the application of this code.
Task 10
With the information obtained in task 9, prepare and present a dotchart() to display the quartiles values
($stats) for variable kilometers.
案例分类
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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