The following table contains the parameter estimates of the linear probability regression model and the logistic regression model.When considering a binary response variable y and two predictor variables, x1 and x2, What is the estimated linear probability impliedby the logistic probability regression model for x1 = 3 with x2 = 9?VariableInterceptX₁x2O 1.87O -0.87O 0.50O 0.35LinearProbability-0.520.40-0.02Logistic-1.140.85-0.14 39-40 .The following table contains the parameter estimates of the linear probability regression model and the logistic regressionmodel. When considering a binary response variable y and two predictor variables, x1 and x2, What is the estimated linearprobability implied by the logistic probability regression model for x1 = 3 with x2 = 9?VariableInterceptX₁x2O 0.35O 1.87-0.35-0.87LinearProbability-0.760.43-0.02Logistic-1.120.85-0.18

Given- Regression model linear probability = -0.52Logistic probability regression model -x1= 3 x2 =…

Answered: The following table contains the…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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The multiple linear regression analysis is one of the most reliable and accurate tools for cost estimating. The validity of the analysis depends on the relevance of the independent variables selected. Please respond to the following: Discuss what independent variables should be selected to estimate the maintenance costs for a manufacturing company using multiple linear regression analysis. Explain why these independent variables were selected. What are other variables should be considered, but decided not to include? Explain why.
Problem 3 Picante Corporation expects demand for mp3 players that they produce to increase. Demand for their mp3 players for last year was as follows: MONTH JANUARY FEBRUARY MARCH APRIL 2,150 2,000 2,200 AUGUST SEPTEMBER OCTOBER 2,450 2,700 2,850 DEMAND 2,100 MONTH JULY DEMAND 2,650 MAY JUNE 2,500 2,350 NOVEMBER DECEMBER 3,150 3,000 a. Using least squares regression analysis, what would you estimate demand to be for each month next year? b. To be reasonably confident of meeting demand, Picante Corporation decides to use three standard errors of estimate for safety. How many additional units should be held to meet this level of confidence?
Descriptive statistics for the closing stock prices of two companies for several trading periods are shown below. Baba, Inc Maman, Inc. Mean 4.00 Mean 15.91 Standard Error 0.11 Standard Error 0.29 Median 3.92 Median 16.59 Mode 4.19 Mode 15.58 Standard Deviation 1.12 Standard Deviation 3.21 Sample Variance 1.25 Sample Variance 10.31 Kurtosis -1.15 Kurtosis 3.52 Skewness 0.08 Skewness -2.09 Range 3.95 Range 13.45 Minimum 2.03 Minimum 5.55 Maximum 5.98 Maximum 18.99 Sum 436.32 Sum 1972.61 Count 109 Count 124 Which company's stock price has a more dispersed distribution? Maman, Inc. Baba, Inc.
Forecasting method A has errors of 10, -15, 5 and 20. Forecasting method B has errors of -10, 4, 16 and -8. The MAD of method A is equal to The MAD for method B is equal to is more accurate than method Based on MAD, method
Cross-Sectional Regression Analysis WasteTec is a large construction company that specializesin the construction of large wastewater treatment plants and recycling plants. A major cost driver ineither type of facility is the capacity of the plant. For example, the capacity of a recycling plant ismeasured by the number of tons of water per day (TPD) that the plant can process. These plants canvary in size from a few hundred TPD to as many as several thousand TPD. Regression analysis is auseful method to estimate the cost of a new plant by using a regression equation developed from priorplant construction projects. The dependent variable of the regression is the actual construction costsof each project, while the independent variable is the TPD for the plant. Below is a sample of somerecent projects and the related construction costs (in thousands):TPD CostsCommerce, CA 360 $ 59,369Hudson Falls, NY 400 77,013Layton, UT 420 50,405Oxford Township, NJ 450 75,779Savannah, GA 500…
4. The number of auto accidents in Athens, Ohio, is related to the regional number of registered automobiles in thou- sands (X1), alcoholic beverage sales in $10,000s (X2), and rainfall in inches (X3). Furthermore, the regression formula has been calculated as: Y=a+b1X1 +b2X2 +b3X3 Where Y = number of automobile accidents a = 7.5 bl = 3.5 b2 = 4.5 b3 = 2.5 Calculate the expected number of automobile accidents under conditions a, b, and c: X, X2 X, (a) 2 3 (b) 5 1 (c) 7 2 N m4
What is differ from SMA (Simple moving average), WMA (Weighted moving average), SLR (Single linear regression), and ESM ( Exponential smoothing model).
In a research study, a linear regression analysis was performed to investigate the relationship between the size of a household (number of family members) and their monthly grocery expenses (in dollars). The linear model obtained is Y=200+150X Where: Y represents the predicted monthly grocery expenses and X represents the size of the household (number of family members). Interpret the slope in context, including the numeric value of the slope and the units of both X and Y. For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac). BIUS Paragraph Arial P 10pt > A VV Ix SE X QS |||| 0 WORDS POWERED BY TINY
answer a,b,c
The table below shows the sales figures for a brand of shoe over the last 12 months.Months SalesJanuary 69February 75March 86April 92May 95June 100July 108August 115September 125October 131November 140December 150 Using the following, forecast the sales for the months up to January the following year:- 1. Use exponential Smoothing when α= .6 and the forecast for March is 350.2. Determine which of the three forecasting technique is the most accurate using Mean Absolute Deviation MAD
bex Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 3.136512 0.964065 0.985521 0.971252 0971252 Given the above regression statistics, what is the value of the coefficient of determination? 0.964065 3.136512
How efficient is an Regression analysis technique?

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