Upload File

p r i c e e l a s ti c i ty · dat acamp f orecast i ng p roduct demand i n r e l ast i c vs. i nel...

  • Home
  • Documents
  • P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the
22
DataCamp Forecasting Product Demand in R Price elasticity FORECASTING PRODUCT DEMAND IN R Aric LaBarr, Ph.D. Senior Data Scientist, Elder Research

Upload: others

Post on 15-Mar-2020

1 views

Category:

Documents


0 download

Report

TRANSCRIPT

Page 1: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Price elasticity

FORECASTING PRODUCT DEMAND IN R

Aric LaBarr, Ph.D.Senior Data Scientist, Elder Research

Page 2: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Price vs. Demand

Price elasticity is the economic measure of how much demand "reacts" to

changes in price

As price changes, it is expected that demand changes as well, but how much?

Price Elasticity =%Change in Price%Change in Demand

Page 3: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Elastic vs. Inelastic

Elastic products are ones that have % changes in demand larger than the %

change in price (Price Elasticity > 1)

Inelastic products are ones that have % changes in demand smaller than the %

change in price (Price Elasticity < 1)

Unit elastic products are ones that have % changes in demand equal to the %

change in price (Price Elasticity = 1)

Page 4: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Linear Regression

Page 5: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Linear Regression

Page 6: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Price Elasticity ExampleM_hi <- as.vector(bev_xts_train[,"M.hi"])

M_hi_p <- as.vector(bev_xts_train[,"M.hi.p"])

M_hi_train <- data.frame(log(M_hi), log(M_hi_p))

colnames(M_hi_train) <- c("log_sales", "log_price")

model_M_hi <- lm(log_sales ~ log_price, data = M_hi_train)

model_M_hi

Call:

lm(formula = log_sales ~ log_price)

Coefficients:

(Intercept) log_price

8.9907 -0.7138

Page 7: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Let's practice!

FORECASTING PRODUCT DEMAND IN R

Page 8: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Seasonal / holiday /promotional effects

FORECASTING PRODUCT DEMAND IN R

Aric LaBarr, Ph.D.Senior Data Scientist, Elder Research

Page 9: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Influencers of Demand

Seasonal effects

Examples: Winter coats, bathing suits, school supplies, etc.

Holiday effects

Examples: Retail sales, holiday decorations, candy, etc.

Promotion effects

Examples: Digital marketing, shelf optimization, etc.

Page 10: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Seasonal / Holiday / Promotion?plot(M_hi) plot(M_hi_p)

Page 11: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Page 12: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Linear Regression! Again...

Linear regression helps us evaluate the relationship between many factors and

demand, not just price.

Add seasonal, holiday, and promotion effects to previous regression!

Any of these effects statistically significant?

Are the effects due to random chance or not?

Page 13: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Creating Effects Examplev.dates <- as.Date(c("2014-02-09", "2015-02-08", "2016-02-07"))

valentine <- as.xts(rep(1, 3), order.by = v.dates)

dates_train <- seq(as.Date("2014-01-19"), length = 154, by = "weeks")

valentine <- merge(valentine, dates_train, fill = 0)

head(valentine, n = 5)

valentine

2014-01-19 0

2014-01-26 0

2014-02-02 0

2014-02-09 1

2014-02-16 0

Page 14: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Adding Effects ExampleM_hi_train <- data.frame(M_hi_train, as.vector(valentine))

model_M_hi_full <- lm(log_sales ~ log_price + valentine, data = M_hi_train)

summary(model_M_hi_full)

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 8.93102 0.44693 19.983 < 2e-16 ***

log_price -0.70010 0.11103 -6.306 3e-09 ***

valentine 0.22942 0.07547 3.040 0.00279 **

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Page 15: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Let's practice!

FORECASTING PRODUCT DEMAND IN R

Page 16: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Forecasting with regression

FORECASTING PRODUCT DEMAND IN R

Aric LaBarr, Ph.D.Senior Data Scientist, Elder Research

Page 17: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Forecasting with Time Series

Page 18: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Forecasting with Regression

Page 19: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Future Input Variables

How to "predict" future input variables?

Holidays and Promotions: NO WORRIES - we know these ahead of time

Prices - Possible problem!

Prices set ahead of time (our assumption)

Forecast future prices with time series!

Page 20: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Future Input Variables Examplev.dates_v <- as.Date("2017-02-12")

valentine_v <- as.xts(1, order.by = v.dates_v)

dates_valid <- seq(as.Date("2017-01-01"), length = 22, by = "weeks")

valentine_v <- merge(valentine_v, dates_valid, fill = 0)

l_M_hi_p_valid <- log(bev_xts_valid[,"M.hi.p"])

model_M_valid <- data.frame(as.vector(l_M_hi_p_valid), as.vector(valentine_v))

colnames(model_M_valid) <- c("log_price", "valentine")

Page 21: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Future Regression Examplepred_M_hi <- predict(model_M_hi_full, model_M_valid)

head(pred_M_hi)

1 2 3 4 5 6

6.128652 6.129163 5.975786 6.030943 6.048169 6.099596

pred_M_hi <- exp(pred_M_hi)

head(pred_M_hi)

1 2 3 4 5 6

458.8170 459.0519 393.7775 416.1070 423.3371 445.6778

Page 22: P r i c e e l a s ti c i ty · Dat aCamp F orecast i ng P roduct Demand i n R E l ast i c vs. I nel ast i c Elastic products are ones that have % changes in demand larger than the

DataCamp Forecasting Product Demand in R

Let's practice!

FORECASTING PRODUCT DEMAND IN R


Pointer Analysis for C programs through AST traversalsedwards/papers/buss2005pointer2.pdf · Pointer Analysis for C programs through AST traversal Marcio Buss∗ Stephen A. Edwards†

AST Diesel OperationalAllegany MD 21501 AST 275 Motor Oil Operational Liberty TX 77535 AST 12000 Diesel Operational Liberty TX 76208 AST 200 Diesel Operational Liberty TX 76209 AST

Man. Itaca AST 14 C 255-00 - GB - Fondital

Gate Valves (Resilient Seated) • Butterfly Valves - Double … · 2015. 9. 16. · AST-30 8,220 AST-30 8,220 AST-40 12,240 AST-60 17,900 AST-60 17,900 AST-80 32,660 AST-80 32,660

C O AST AL HAMPTONS - PGH Bricks

C. Nicklin AST Aerobic. C. Nicklin AST Agility C. Nicklin AST Aerobic ‘with oxygen’. If exercise is not too fast and is steady, the heart can supply

Teacher: Kenji Tachibana Digital Photography I. C ast S hadow Case Study - 6 Slides

1 I NTERNATIONALIZATION P ROCESSES IN E AST A SIA

T-S4-01 · 2012. 3. 16. · f,9'6Ri 1.90'gga 9tÞ'zog'c 999'/,n.'z 006'699'C cco'zz¿'c (0'96'99z c 896'9C 811 686'ÊW 699'9û zp,g'g 6t6'ast, z I z'g9ç: l.cc'c€i 'c I '91Z'g 89d'c

AST I (All)

AST Protocols for PEM Water Electrolyis : Insight on ......| Fouda-Onana Frédéric | 10 Analytical Methods and main outcomes AST-1: 48h AST signal @ 90 C repetaed at least 4 times

LA MAR CALCA LA CONVENCION C AST ROVI EYN PAU CR TMB …portal.mtc.gob.pe/transportes/caminos/normas_carreteras/Mapas... · Cañahua C ay cti Ch Osper Chacchataipi C hapic i Chec

Scientific Computing (Phys 2109/ Ast 3100H) I. Scientfic

AST 2010 Descriptive Astronomy Study Guide – Exam I ...motor1.physics.wayne.edu/~giovanni/ast2010/studyguide.pdf · AST 2010! Descriptive Astronomy! Chapter 6Light and Telescopes"

General Plasma Physics I Notes AST 551 - Princeton

Aboveground Storage Tanks (AST) · Overview Regulations AST EVR Deadlines Standing Loss Control Overview Standing Loss Control Certification Phase I AST CertificationPhase I AST Certification

AST PROTOCOLS FOR PEM WATER ELECTROLYIS ...AST-1: 48h AST signal @ 90 C repetaed at least 4 times AST-2: 48 h AST@ 90 C repeated at leat 4 times AST-3: : 48 h AST@ 90 C repeated at

AST AST-L AST-PLUS - Anthony Liftgates, Inc. · AST, AST-L, and AST-PLUS Models 5 Anthony Lifgtates, Inc. 800-482-0003 5. Most problems occur when positioning the adapter frame tube

1/38 Alternative Substrates Y-C Jung,S-H Won, D.G. Ast

C ert i fi ed Scru mM ast er - Agil8 · + 44 (0) 207 796 9903 [email protected] C ert i fi ed Scru mM ast er ( C SM ) Liv e O n lin e T rain in g Workbook W ith Dav id H icks

DRAFT - eventphone.debef.eventphone.de/a/Ast. C. I._files/ast-ci-draft1.pdf · actually had been the only way in asterisk 1.0 and before, is using the configuration file extensions.conf

5xULN AST ALTQ.5xULN 750 mg 1 2 750 mg 1 ALTQ.5xULN Fi, 500 1

£ast African Maltings Ltd I EABL International Ltd ers

1 N EW M EXICO AFIS F AST ID U PGRADE C HANGE R EQUEST C ERTIFICATION N EW M EXICO AFIS F AST ID U PGRADE C HANGE R EQUEST C ERTIFICATION Executive Summary

AST AST-L AST-PLUS - Anthony Liftgates, Inc. · 4 INTRODUCTION i This Quick Reference Guide is an overview of the installation process and will assist in the installation of the Anthony

AST POLYGEYSER SKIDS - AST Bead FiltersPolyGeyser Benefits: • Automa c backwashing – these filters clean themselves • PolyGeyser has been tested and proven for over 20 years

T HE C AST

AST 204 design-and-analysis-of-experiments-5th-edition-douglas-c-montgomery.pdf

STORAGE UNIT AST-40, AST-50, AST-80, AST-120 · 2018-08-15 · storage unit . ast-40, ast-50, ast-80, ast-120 . the warranty on this storage unit is in effect only when the storage

Materi AST I

SPRIG C PPICE TA B T AVEUE ITT E AST B74 3DD ITT E AST B74 3DD€¦ · sprig c ppice ta b t aveue sprig c ppice ta b t aveue itt e ast b74 3dd itt e ast b74 3dd

ASTC Advanced Sensor Technologies · Company Profile and Products AST°C AST ENGINEERING INC. Advanced Sensor Technologies. AST Engineering Inc. started business 14 years ago as an

1. Uvod do programov an v C++ - cvut.cz...2/43 P rehled t emat Y C ast 1 { O p redm etu Y C ast 2 { Novinky v C++ oproti C Prvn program Vstup a v ystup Reference Implicitn parametry

The Milky Way I AST 112 Credit: Stephane Vetter

korean catalogue - Tempsens Instruments Pvt. Ltd · (b ast 450c ast 450 ast ast fo ast 250 fo/pl 750 firm ast tl 1500 ±05s 20 200 ico:t