代写129,971留学生作业、代做R实验作业、代写R程序设计作业、代做data课程作业代写Web开发|代写R语言编程

2019/7/16 NYU Classes : Advanced Test, Analysis, & Exp, Section 004 : Assignmentsfile:///Users/littlesunsu/Documents/NYU Classes _ Advanced Test, Analysis, & Exp, Section 004 _ Assignments.htm 1/2InstructionsYou are analyzing which factors are driving wine ratings. You are given a data set wine_data_clean_v2.csvThe data contains 129,971 observations with the following variables:country- country the wine is frompoints- points given to rate the wine (0%-100% scale), this is essentially the wine ratingprice- price of bottle of wine in USD ($)province-which province wine is fromvariety- type of wineFirst make sure you install the following packages to have these libraries:library(readr)library(data.table)library(broom)options(max.print = 100000) #is useful when regression output is really bigTry reading the file using the base function read.csv()Notice that some of the names of levels are not readable.Use the read_csv function from the readr package to read the file:wine_df2Design/Assignment 2/wine_data_clean_v2.csv)(you can also do this by setting file.path() first as we did in class to get your folder location)Now there is a problem that the variables that are supposed to be factors are characters.When running your regression model and adding character variables write as.factor(variable_name) so it treatsthe levels of the variable as factors (indicators/dummies)1) Write down the mathematical model for testing the impact of several drivers on the outcome (1 point) (Note thisis not the same as R code this should be a math formula)2) Write down the R model for testing the impact of several drivers on the outcome (1 point)3) Run the model in R, and assign it to a variable. Store the model estimates in a data frame by using the tidycommand from the broom package.sum_lmNotice how now all your regression estimates are stored as a data frame (data table in this case since we coercedit). This allows us to output the coefficients relevant to us in an easier way. Since we do not really care about theintercept we can generate a new data frame without the intercept:no_icepta) Which variables are statistically significant at the 0.05 significance level and have a coefficient estimate that ispositive (>0). What does it mean that these variables have a positive and significant coefficient? (5 points)[Hint: Your output should be a data set, you can use the data.table package or the plyr package]b) Which variable is statistically significant at the 0.05 significance level and has the highest impact on wine ratingpoints based on the coefficient estimate? Interpret your result. (5 points)[Hint: You can use data.table package or the dplyr package]c) Plot the residual vs. fitted graph. Which assumptions can you visually inspect from this graph. Do you think thelinear regression model assumptions of zero conditional mean and homoscedasticity?are satisfied? (2 points)d) How much variation in wine rating (points) is explained by your independent variables? (1 point)[Hint: You can use summary(your_reg_model_object)]2019/7/16 NYU Classes : Advanced Test, Analysis, & Exp, Section 004 : Assignmentsfile:///Users/littlesunsu/Documents/NYU Classes _ Advanced Test, Analysis, & Exp, Section 004 _ Assignments.htm 2/2e) After thinking some more about your experiment you realize that the wine tasters unique tastes and preferencesmay be biasing the results of your model. You decide to use a fixed-effects model in order to control forunobservable factors due to the wine tasters themselves. To use the fixed-effects approach you can add adummy/indicator variable for each taster, this is done automatically in R by including the taster_name variable as afactor in your regression:+as.factor(taster_name)i) Can you identify which tasters had a negative and significant impact on the average wine rating at the 0.05significance level? Identify the variable with the highest impact on average wine rating as you did in the previousmodel, is it the same or different as the one you had before? (3 points)ii) Which model has more predictive power in terms of ? Interpret your findings, are wine taster fixed-effectscontributing any predictive power to your model?(2 points)Additional resources for assignmentFile attachment wine_data_clean_v2.csv ( 6 MB; Jul 16, 2019 10:27 am )R22本团队核心人员组成主要包括BAT一线工程师，精通德英语！我们主要业务范围是代做编程大作业、课程设计等等。我们的方向领域：window编程 数值算法 AI人工智能 金融统计 计量分析 大数据 网络编程 WEB编程 通讯编程 游戏编程多媒体linux 外挂编程 程序API图像处理 嵌入式/单片机 数据库编程 控制台 进程与线程 网络安全 汇编语言 硬件编程 软件设计 工程标准规等。其中代写编程、代写程序、代写留学生程序作业语言或工具包括但不限于以下范围:C/C++/C#代写Java代写IT代写Python代写辅导编程作业Matlab代写Haskell代写Processing代写Linux环境搭建Rust代写Data Structure Assginment 数据结构代写MIPS代写Machine Learning 作业 代写Oracle/SQL/PostgreSQL/Pig 数据库代写/代做/辅导Web开发、网站开发、网站作业ASP.NET网站开发Finance Insurace Statistics统计、回归、迭代Prolog代写Computer Computational method代做因为专业，所以值得信赖。如有需要，请加QQ：99515681 或邮箱：[email protected] 微信：codehelp QQ：99515681 或邮箱：[email protected] 微信：codehelp