Practical Management Science
6th Edition
ISBN: 9781337406659
Author: WINSTON, Wayne L.
Publisher: Cengage,
expand_more
expand_more
format_list_bulleted
Question
Chapter 14.2, Problem 2P
Summary Introduction
To classify: The consumers using logistic regression based on explanatory variables.
Introduction: Simulation model is the digital prototype of the physical model that helps to
Expert Solution & Answer
Trending nowThis is a popular solution!
Students have asked these similar questions
Which best describes the null hypothesis associated with an Analysis of Variance (ANOVA)?
Group of answer choices
a. Ho: Variance 1 = Variance 2 = Variance 3
b. Ho: Standard Deviation 1 = Standard Deviation 2 = Standard Deviation 3
c. Ho: Proportion 1 = Proportion 2 = Proportion 3
d. Ho: Median 1 = Median 2 = Median 3
e. Ho: Mean 1 = Mean 2 = Mean 3
Which modeling techniques are suitable to build a predictive model ? (Multiple answer may be correct)
Group of answer choices
Multiple linear regression
Classification tree
Logistic regression
Clustering
Mabbly Marketing conducted a study includes 200 sports enthusiast and obtained
the following data:
Sports
College
No College
Preference Degree
Degree
NBA
40
55
NFL
10
95
What is the probability that a randomly selected survey participant prefers the NFL?
0.5880
0.5250
0.2000
0.6050
Knowledge Booster
Similar questions
- Data6011168289244865150815943464930649748351114673181655377588940649889455124564365623958361869657477754720539912843235106717172412496938978170625613733011912030776887677869411491998415319814876892100981001650What is the 60th percentile of the following dataset? 77 35 48 65Please input this data using excel. please demonistrate on how to solve this excel problemarrow_forwardWhat is producivity analysis? What is prescriptive data analytic model? How are the two related or how are they different? Give example of each.arrow_forwardA sample of twenty automobiles was taken, and the miles per gallon (MPG), horsepower, and total weight were recorded. Develop a linear regression model to predict MPG using horsepower as the only indepen- dent variable. Develop another model with weight as the independent variable. Which of these two models is better? Explain. MPG 44 44 40 37 37 34 35 32 30 28 26 26 25 22 20 21 18 18 16 16 4 HORSEPOWER 67 50 62 69 66 63 90 99 63 91 94 88 124 97 114 102 114 142 153 139 WEIGHT 1,844 1,998 1,752 1,980 1,797 2,199 2,404 2,611 3,236 2,606 2,580 2,507 2,922 2,434 3,248 2,812 3,382 3,197 4,380 4,036arrow_forward
- please explain the following random sampling methods along with giving examples for each in a business context situation. a) Systematic random sample b) Stratified Sample c) Cluster Samplearrow_forwardM3,arrow_forwardAccording to Stuart (2010), which of the following statements about observational study designs for causal inference are typically true? Choose all that apply. Including propensity score estimates as a predictor in a regression model on outcomes can help resolve imbalance between treatment and control groups for covariates used in the propensity score model Standard diagnostic tools for binary prediction or classification models (e.g. logistic regression or classification trees) are similarly used in evaluating propensity score estimates When conditional ignorability holds given the observed covariates, then the treatment assignment will also be ignorable conditioned on the propensity scores Overfitting in propensity score estimation can achieve more efficient estimates of treatment effects than using propensity score estimates that are closer to the true propensities Unlike full matching, subclassification, and weighting methods, nearest neighbor matching does not necessarily use all…arrow_forward
- . What are the advantages and disadvantages of Regression Model, Econometric Model, Driving Indicator Models?arrow_forwardi need this answer i will 3 uovotesarrow_forwardThe file P14_01.xlsx contains data on 100 consumers who drink beer. Some of them prefer light beer, and others prefer regular beer. A major beer producer believes that the following variables might be useful in discriminating between these two groups: gender, marital status, annual income level, and age. a. Use logistic regression to classify the consumers on the basis of these explanatory variables. How successful is it? Which variables appear to be most important in the classification? b. Consider a new customer: Male, Married, Income 42,000, Age 47. Use the logistic regression equation to estimate the probability that this customer prefers Regular. How would you classify this person?arrow_forward
- The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?arrow_forwardDo the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.arrow_forwardManagement of a home appliance store wants to understand the growth pattern of the monthly sales of a new technology device over the past two years. The managers have recorded the relevant data in the file P13_05.xlsx. Have the sales of this device been growing linearly over the past 24 months? By examining the results of a linear trend line, explain why or why not.arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Practical Management ScienceOperations ManagementISBN:9781337406659Author:WINSTON, Wayne L.Publisher:Cengage,
Practical Management Science
Operations Management
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:Cengage,