Themarketing manager wants to estimate the effectof the MBA program on Salary controlling for the otherfactors. Which regression model is the MOSTappropriate?Oa. Salary = B_0+B_1 MBA + εOb. Salary = 3_0+ B_1 MBA + B_2 Work + ec.Salary = B_0+B_1 MBA+B_2 Work + B_3 Age+8Od. Salary = B_0+ B_1 MBA + B_2 Work + B_3 Age+B_4 Gender + ε

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Answered: The marketing manager wants to estimate…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.5: Correlation And Causation

Problem 2AGP

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Does Table 1 represent a linear function? If so, finda linear equation that models the data.
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
The marketing manager wants to test if the effect of the MBA program on salary depends on the work experience while controlling for the other factors. Which regression model is the MOST appropriate? O a. Salary = B_0+B_1 MBA+B_2 Work +B_3 Age +3_4 Gender + 8 Ob. Salary = B_0+ B_1 MBAx Work + B_2 Age + B 3 Gender & Oc. Salary = B_0+ B_1 MBA + B_2 MBA x Work + B_3 Age + B_4 Gender + e Od. Salary = B_0+ B_1 MBA+B_2 Work + B_3 MBA x Work + 3_4 Age + B_5 Gender + &
When should a regression model not be used to make a prediction?
Which is an assumption of linear regression analysis? The mean of the residuals should be
To monitor and improve its productivity, a company made an investigation and found out that the factor that affects the productivity the most is the absenteeism. The company data analytics department have collected data about the two variables (Productivity and Absenteeism) for the 12 past years as shown in the table below. Now the purpose of the company is to determine, through regression analysis, whether the productivity is statistically affected by the absenteeism level or not. Year Absenteeism Productivity (in number of absent worker) (in Million AED) 1 204 342 2 352 336 3 154 406 4 206 410 5 422 278 6 530 214 7 750 138 8 482 268 9 374 262 10 120 356 11 188 396 12 634 152 Questions: Construct a scatter diagram for the data about productivity and absenteeism then interpret the possible relationship that can be found. Construct a simple regression model to predict the…
Tire pressure (psi) and mileage (mpg) were recorded for a random sample of seven cars of thesame make and model. The extended data table (left) and fit model report (right) are based on aquadratic model What is the predicted average mileage at tire pressure x = 31?
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The following result perspective in RapidMiner shows a multiple linear regression model. Based on the diagram, the model for our dependent variable Y is Predicted Y= (Insulation *0.420)+(Temperature *0.071)+(Avg_Age*0.065)+(Home_Size *0.311)+7.589 Attribute Insulation Temperature Avg Age Home Size (Intercept) O True O False Coefficient 3.323 -0.869 1.968 3.173 134.511 Std. Error 0.420 0.071 0.065 0.311 7.589 Std. Coefficient 0.164 -0.262 0.527 0.131 ? Tolerance 0.431 0.405 0.491 0.914 ? t-Stat 7.906 -12.222 30.217 10.210 17.725
researcher collects some socioeconomic variables of Turkey and wants to model the natural gas consumption arough years by multiple linear regression. Below is the table summarizing the coefficients of variables and their statistical parameters, and the figure showing the change of natural gas consumption in this time period • Actual constant term X₁: population (million people) X2: GDP per capita (thousand $) X3: Inflation percentage X4: Mean winter temperature coefficients -36.5 0.578 2.133 -0.0733 -0.0375 standard error 6.73 0.133 0.221 0.0126 0,201 t value -5.43 4.34 p-value 1.42* 10.5 2.20*10- 9.67 9.47*10-10 -0.19 -5.81 5.46* 10.6 0.853 Natural gas consumption (billon m²) 50 40 30 20 10 R¹ = 0.992 standard error 1.51 1986 1990 1994 1998 -Estimated 2002 Star 2006 2010 a) Write the multiple regression equation in its standard form: y =B₁ + B₁x₁ + ß₂x₂ + ... b) Which variables significantly affect the natural gas consumption? Why? c) Which variables positively, which variables…
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