We know that different stores are different Different
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-1.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-2.jpg)
![We know that different stores are “different” - Different level of sales - Different We know that different stores are “different” - Different level of sales - Different](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-3.jpg)
![common task common task](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-4.jpg)
![ID Age Educ Politics Treated 1 59 BA D 0 2 33 Ph. D ID Age Educ Politics Treated 1 59 BA D 0 2 33 Ph. D](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-5.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-6.jpg)
![CTR Loss in CTR from Link Demotion (US All Non-Navigational) 25, 4% 13, 5% CTR Loss in CTR from Link Demotion (US All Non-Navigational) 25, 4% 13, 5%](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-7.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-8.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-9.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-10.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-11.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-12.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-13.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-14.jpg)
![how does this matter for treatment? how does this matter for treatment?](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-15.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-16.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-17.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-18.jpg)
![library(rpart) library(rpart. plot) library(partykit) library(permute) library(maptree) library(rpart) library(rpart. plot) library(partykit) library(permute) library(maptree)](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-19.jpg)
![mydata <- read. csv("C: /Users/jlariv/Documents/Hetero. ATE/ln_Required. Data. csv") mydata<- mydata[, 2: ncol(mydata)] colnames(mydata) # mydata <- read. csv("C: /Users/jlariv/Documents/Hetero. ATE/ln_Required. Data. csv") mydata<- mydata[, 2: ncol(mydata)] colnames(mydata) #](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-20.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-21.jpg)
![So… Regression trees are cool at categorizing variables with a continuous LHS variable (e. So… Regression trees are cool at categorizing variables with a continuous LHS variable (e.](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-22.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-23.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-24.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-25.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-26.jpg)
![library(geosphere) x <- collapsed[ , c(2: 45, 65: 72)] ##pull variables to cluster on library(geosphere) x <- collapsed[ , c(2: 45, 65: 72)] ##pull variables to cluster on](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-27.jpg)
![Non-Price Competition Non-Price Competition](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-28.jpg)
![1/1/2022 Game Theory, Market Structures, and Firms Slide by Will Wang 29 1/1/2022 Game Theory, Market Structures, and Firms Slide by Will Wang 29](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-29.jpg)
![Hotelling (horizontal differentiation) • Assume that substitutes exist but they are imperfect and therefore Hotelling (horizontal differentiation) • Assume that substitutes exist but they are imperfect and therefore](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-30.jpg)
![Vertical differentiation 1/1/2022 • Game Theory, Market Structures, and Firms 31 Vertical differentiation 1/1/2022 • Game Theory, Market Structures, and Firms 31](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-31.jpg)
- Slides: 31
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-1.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-2.jpg)
![We know that different stores are different Different level of sales Different We know that different stores are “different” - Different level of sales - Different](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-3.jpg)
We know that different stores are “different” - Different level of sales - Different sales price - Different sociodemographic characteristics Is there a smart way to group them?
![common task common task](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-4.jpg)
common task
![ID Age Educ Politics Treated 1 59 BA D 0 2 33 Ph D ID Age Educ Politics Treated 1 59 BA D 0 2 33 Ph. D](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-5.jpg)
ID Age Educ Politics Treated 1 59 BA D 0 2 33 Ph. D n/a 1 3 41 HS I 1 … … …
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-6.jpg)
![CTR Loss in CTR from Link Demotion US All NonNavigational 25 4 13 5 CTR Loss in CTR from Link Demotion (US All Non-Navigational) 25, 4% 13, 5%](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-7.jpg)
CTR Loss in CTR from Link Demotion (US All Non-Navigational) 25, 4% 13, 5% 17, 9% 4, 9% 6, 9% 3, 5% 4, 0% Control (1, 3) (1, 5) (1 st Position) Original CTR of Position Gain from Increased Relevance 21, 6% 1, 7% 2, 1% (1, 10)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-8.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-9.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-10.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-11.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-12.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-13.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-14.jpg)
![how does this matter for treatment how does this matter for treatment?](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-15.jpg)
how does this matter for treatment?
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-16.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-17.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-18.jpg)
![libraryrpart libraryrpart plot librarypartykit librarypermute librarymaptree library(rpart) library(rpart. plot) library(partykit) library(permute) library(maptree)](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-19.jpg)
library(rpart) library(rpart. plot) library(partykit) library(permute) library(maptree)
![mydata read csvC UsersjlarivDocumentsHetero ATElnRequired Data csv mydata mydata 2 ncolmydata colnamesmydata mydata <- read. csv("C: /Users/jlariv/Documents/Hetero. ATE/ln_Required. Data. csv") mydata<- mydata[, 2: ncol(mydata)] colnames(mydata) #](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-20.jpg)
mydata <- read. csv("C: /Users/jlariv/Documents/Hetero. ATE/ln_Required. Data. csv") mydata<- mydata[, 2: ncol(mydata)] colnames(mydata) # This just gets the data in a format where everything has a name I can work with. data. To. Pass<mydata[, c("ln_resid_use", "heat_pump", "gas_heating", "vinyl", "wood", "brick", "beds", "top_floor", "basement", "tot_sq_ft", "yearbuilt", "unregistered", … …"undeclared", "rep", "dem", "treated", "k. Wh", "expense", "CO 2")] #This creates a dataframe with only the variables I want to use to explain variation in the variable I care to examine (e. g. , ln(residual use) #This next line actually fits the tree. bfit<-rpart(as. formula(ln_resid_use ~. ), data=data. To. Pass, method="anova", cp=0. 0003) #NOTE: cp=. 0003 is the "complexity parameter" and the lower it goes, the more complicated the tree gets (e. g. , more leaves). draw. tree(bfit) #This command draws the tree data. To. Pass$leaf = bfit$where #This final command identifies which observation is associated with what leaf and creates a new variable for it in the original dataframe #disco #Now you know where hashtags came from
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-21.jpg)
![So Regression trees are cool at categorizing variables with a continuous LHS variable e So… Regression trees are cool at categorizing variables with a continuous LHS variable (e.](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-22.jpg)
So… Regression trees are cool at categorizing variables with a continuous LHS variable (e. g. , minutes on application) What if there is no LHS variable? Example: rather than minutes on an application as a function of covariates, you care about how people use the application. -> e. g. , user types or “road types”
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-23.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-24.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-25.jpg)
![](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-26.jpg)
![librarygeosphere x collapsed c2 45 65 72 pull variables to cluster on library(geosphere) x <- collapsed[ , c(2: 45, 65: 72)] ##pull variables to cluster on](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-27.jpg)
library(geosphere) x <- collapsed[ , c(2: 45, 65: 72)] ##pull variables to cluster on from dataframe “c” colnames(x) <- colnames(collapsed)[c(2: 45, 65: 72)] ##Clustering for(i in 1: 1){ ##10 tries for each cluster size so we can test with multiple groupings if we choose set. seed(i) ##set the number of clusters we want to use for(j in c(8)){ ##clusters of size 8, 10, 15) k <- j ##K means clustering cl <- kmeans(x, k) ##k is the number of clusters, x is our set of clustering variables collapsed <- cbind(collapsed, cl$cluster) ##append data with cluster ids colnames(collapsed)[ncol(collapsed)] <- paste("cluster_", k, "_seed_", i, sep="") } } segment_clusters = data. frame(cbind(as. character(collapsed$tmc), collapsed$cluster_8_seed_1)) ##using k=10 segmentscsv$tmc <- as. character(segmentscsv$tmc) ##making sure segements aren't stored as factors segment_clusters$X 1 <- as. character(segment_clusters$X 1) ##X 1 is the segment variable segmentscsv <- merge(segmentscsv, segment_clusters, by. x = "tmc", by. y="X 1") colnames(segmentscsv) <- c(colnames(segmentscsv)[1: (ncol(segmentscsv)-1)], "cluster_id")
![NonPrice Competition Non-Price Competition](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-28.jpg)
Non-Price Competition
![112022 Game Theory Market Structures and Firms Slide by Will Wang 29 1/1/2022 Game Theory, Market Structures, and Firms Slide by Will Wang 29](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-29.jpg)
1/1/2022 Game Theory, Market Structures, and Firms Slide by Will Wang 29
![Hotelling horizontal differentiation Assume that substitutes exist but they are imperfect and therefore Hotelling (horizontal differentiation) • Assume that substitutes exist but they are imperfect and therefore](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-30.jpg)
Hotelling (horizontal differentiation) • Assume that substitutes exist but they are imperfect and therefore costly. - ex: Stevens Pass versus Crystal Mountain • Idea: How much should firms charge given where they are in horizontal “product space”? • Assume firms identical in every way but location (simplest possible model of differentiation). 1/1/2022 • Monopoly? • Anywhere • Duopoly? • Middle • Three players? • No pure strategy equilibrium • Median voter theorem? • How might it break down? “Walk” is costly and distribution not uniform Game Theory, Market Structures, and Firms 30
![Vertical differentiation 112022 Game Theory Market Structures and Firms 31 Vertical differentiation 1/1/2022 • Game Theory, Market Structures, and Firms 31](https://slidetodoc.com/presentation_image_h2/44e9da713981f67a7e16eb5577679a5c/image-31.jpg)
Vertical differentiation 1/1/2022 • Game Theory, Market Structures, and Firms 31
Insidan region jh
Concept of store management
Chiến lược kinh doanh quốc tế của walmart
Gây tê cơ vuông thắt lưng
Block xoang nhĩ độ 2 type 1
Tìm vết của đường thẳng
Sau thất bại ở hồ điển triệt
Thơ thất ngôn tứ tuyệt đường luật
Con hãy đưa tay khi thấy người vấp ngã
Thơ thất ngôn tứ tuyệt đường luật
Tôn thất thuyết là ai
Phân độ lown ngoại tâm thu
Dilan gorur
The one who reigns forever he is a friend of mine
Know history know self
What part of the brain stores long term memory
Stores minerals and anchors muscles
What stores information in a cell
Jordan v jewel food stores inc
Supermarket market map
Lidl pod point
Distributed data stores
Drainage basin inputs
Oviduct frog
Milland village shop
The distinct threadlike structures that contain the genetic
Consumable stores in accounting
Michaels stores edi
Family clothing stores
For service and retail stores a prime factor
Stores hereditary information
Stores specialists inc