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
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
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
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
Regression analysis is a powerful and commonly used tool in business research. One important step in regression is to determine the dependent and independent variable(s). In a bivariate regression, which variable is the dependent variable and which one is the independent variable? What does the intercept of a regression tell? What does the slope of a regression tell? What are some of the main uses of a regression? Provide an example of a situation wherein a bivariate regression would be a good choice for analyzing data. Justify your answers using examples and reasoning.
A company hires you to develop a linear regression forecasting model. Based on the company's historical sales information, you determine the intercept value of the model to be 1,500. You also find the slope value is -25. If after developing the model you are given a value of X = 8, which of the following is the resulting forecast error using this model if the actual sales is 1250?
The number of auto accidents in Athens, Ohio is related to the regional number of regisgtered automobiles in thousands (X1) alcholic beverage sales in $10,000 (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= 8.0 b1= 3.5 b2=5.0 b3= 2.4 For the given values of X1,X2, and X3, the expected number of accidents will be ( round your responses to one decimal place) X1 X2 X3 # of accidents 6.0 7.0 0.0 [__]
Based on the following regression ANOVA table, how many predictors are studied? Source df SS MS F Regression 4 1793.2356 448.3089 7.4854 Residual 45 2695.0996 59.8911 Total 49 4488.3352 1 3 4 5

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