Learning Plan 2: Regression and Programming in Business Making DecisionsCompetency

4. Assess a variety of programming problems in business making decisions.

This learning plan will address the following learning objectives to help you master the competency:

a. Use statistical methods for solving programming problems in business decision making.
b. Analyze decisions based on results and program output.

5. Construct regression models in decision making.

This learning plan will address the following learning objectives to help you master the competency:

a. Develop simple and multiple linear regression equations from sample data.
b. Identify components of a linear regression model.
c. Interpret F-test output.

Overview

In this learning plan you will practice using programming to make business decisions. Programming will include using linear and multiple regression. You will learn how to conduct simple and multiple regression analysis and interpret F-test output.

Learning Activities

1. SELECT one of the three articles to read and identify some of the key characteristics and uses of regression. (To find these articles go to theNAU Online Library and search within the ProQuest Central database.)
   Predicting supermarket sales: The use of regression trees
   Silva, A. L., & Margarida, G. M. S. C. (2005). Predicting supermarket sales: The use of regression trees. Journal of Targeting, Measurement and Analysis for Marketing, 13(3), 239-249. Retrieved from http://search.proquest.com/docview/236972669?accountid=36299
   Predicting housing value: A comparison of multiple regression analysis and artificial neural networks
   Nguyen, N., & Cripps, A. (2001). Predicting housing value: A comparison of multiple regression analysis and artificial neural networks. The Journal of Real Estate Research, 22(3), 313-336. Retrieved from http://search.proquest.com/docview/200301026?accountid=36299
   Functional regression: A New Model for Predicting Market Penetration of New Products
   Sood, A., James, G. M., & Tellis, G. J. (2009). Functional regression: A new model for predicting market penetration of new products.Marketing Science, 28(1), 36-51,194,196. Retrieved from http://search.proquest.com/docview/212307936?accountid=36299
2. READ/REVIEW Chapter 4 and Chapter 5.
3. REVIEW the PowerPoint Presentation for Chapter 4 and Chapter 5.
4. COMPLETE the following Quantitative Analysis for Management 12th ed. problems found in the textbook:

   4-10 “Musical Instrument” (pg. 142)
   4-11 “Statistically sig. relationship” (pg. 142) (Answer not provided in textbook - discuss in the live session)
   4-12 “Least squares” (pg. 142)
   (check your answers in back of book - Appendix G: Solutions to Selected Problems)

5. PARTICIPATE in Discussion 2: Visual Data Display (Scatter/Residual).
   See the "Participation" policy in the "Discussion Board" section of the "Expectations" document for a detailed explanation of the feedback requirement.
6. COMPLETE the LP2 Quiz.
7. VIEW the LP2 Seminar and complete the assessment.

Assignment

1. COMPLETE the LP2 Assignment: Akron Zoological Park.

SmartExpert

(118)

Status NEW Posted 24 May 2017 04:05 PM My Price 14.00

Reg-----------res-----------sio-----------n a-----------nd -----------Pro-----------gra-----------mmi-----------ng -----------in -----------Bus-----------ine-----------ss -----------Mak-----------ing----------- De-----------cis-----------ion-----------sCo-----------mpe-----------ten-----------cy-----------

Attachments

file 1495644384-Regression and Programming in Business Making DecisionsCompetency.docx preview (420 words )

----------- ----------- T-----------he -----------sim-----------ple----------- re-----------gre-----------ssi-----------on -----------equ-----------ati-----------on -----------to -----------pre-----------dic-----------t G-----------DP -----------usi-----------ng -----------rai-----------lro-----------ad -----------rev-----------enu-----------es -----------as -----------ind-----------epe-----------nde-----------nt -----------var-----------iab-----------le -----------is -----------giv-----------en -----------bel-----------ow ----------- Y -----------= b-----------0 +----------- b1----------- * -----------X1 -----------+ E----------- O-----------r -----------Gui-----------tar----------- Sa-----------les----------- = -----------b0 -----------+ b-----------1 *----------- Yo-----------utu-----------be -----------Vie-----------ws ----------- Th-----------e s-----------lop-----------e i-----------s c-----------alc-----------ula-----------ted----------- by----------- us-----------ing----------- th-----------e f-----------orm-----------ula----------- gi-----------ven----------- be-----------low-----------: ----------- = ----------- T-----------he -----------int-----------erc-----------ept----------- is----------- ca-----------lcu-----------lat-----------ed -----------by -----------usi-----------ng -----------the----------- fo-----------rmu-----------la -----------giv-----------en -----------bel-----------ow:----------- I-----------nte-----------rce-----------pt -----------= ----------- Th-----------e t-----------abl-----------e g-----------ive-----------n b-----------elo-----------w s-----------how-----------s t-----------he -----------wor-----------kin-----------gs -----------of -----------slo-----------pe -----------and----------- in-----------ter-----------cep-----------t o-----------f t-----------he -----------reg-----------res-----------sio-----------n e-----------qua-----------tio-----------n -----------Sl.----------- No----------- Yo-----------uTu-----------be -----------Vie-----------ws

file 1495644390-Regression and Programming in Business Making DecisionsCompetency.xlsx preview (22 words )

Sl-----------. N-----------oTo-----------tal-----------You-----------tub-----------e V-----------iew-----------s (-----------1,0-----------00s-----------) ------------ xG-----------uit-----------ar -----------Sal-----------es ------------ y-----------x^2-----------y^2-----------xyS-----------UMM-----------ARY----------- OU-----------TPU-----------TRe-----------gre-----------ssi-----------on -----------Sta-----------tis-----------tic-----------sMu-----------lti-----------ple----------- RR----------- Sq-----------uar-----------eAd-----------jus-----------ted----------- R -----------Squ-----------are-----------Sta-----------nda-----------rd -----------Err-----------orO-----------bse-----------rva-----------tio-----------nsA-----------NOV-----------ARe-----------gre-----------ssi-----------onR-----------esi-----------dua-----------lIn-----------ter-----------cep-----------tdf-----------SSM-----------SFS-----------ign-----------ifi-----------can-----------ce -----------FCo-----------eff-----------ici-----------ent-----------st -----------Sta-----------tP------------val-----------ueL-----------owe-----------r 9-----------5%U-----------ppe-----------r 9-----------5%L-----------owe-----------r 9-----------5.0-----------%Up-----------per----------- 95-----------.0%----------- -----------

Not Rated(0)

• Buy Answer