When building the complete 2nd order model, you need to create interactions between all of the following EXCEPT:qualitative predictors and other qualitative predictorslinear quantitative predictors and qualitative predictorsqualitative predictors and quadratic quantitative predictorslinear quantitative predictors and quadratic quantitative predictors

Answered: When building the complete 2nd order…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The Head of production at Amiba private limited needed to examine the key determinants of company profitability($,000). So, he collected relevant(monthly) data and following is the summary of his regression output. SUMMARY OUTPUT Regression Statistics Multiple R 0.5776 R Square 0.3336 Adjusted R Square 0.2670 Standard Error 2.2743 Observations 34 ANOVA df SS MS F Significance F Regression 3 77.6774 25.8925 5.0060 0.0062 Residual 30 155.1693 5.1723 Total 33 232.8467 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 10.49 2.9211 3.5927 0.0012 4.5291 16.4605 4.5291 16.4605 Variable X 0.99 1.2461 0.7937 0.4336 -1.5559 3.5340 -1.5559 3.5340 Variable Y 2.56 1.1291 2.2663 0.0308 0.2529 4.8648 0.2529 4.8648 Variable Z 2.06…
Answer d, e, f and g
Create scatterplots using the data in the spreadsheet linked above and display the equation for the regression line. What is the equation for the regression line that predicts mortgage amount using household income as the explanatory variable? Ý : Ex: 1.234 x + (Ex: 1.234) What is the interpretation of the slope? Pick What is the interpretation of the intercept? Pick Pick Household income when mortgage amount is 0 Mortgage amount when household income is 0 No practical interpretation 2
The table shows the numbers B (in thousands) of bachelor's degrees earned by women in the United States from 2002 through 2013 (Source: National Center for Education Statistics). Bachelor's degrees, B DATA Year 2002 2003 742 776 2004 804 2005 2006 2007 826 855 875 2008 895 2009 916 943 2010 2011 982 1026 2012 2013 1053 A linear model for the data is B(t) = 26.9t + 689, 2 < t < 13, where t represents the year, witht = 2 corresponding to 2002. (1) What re the slope and y-intercept of the model? Interpret their meaning in the context of the problem. (2) Use the model to approximte the number of bachelor's degrees earned by women for year 2011. Compare the estimates to the actual data. Is the model a good fit for the data? Spreadsheet at LanonPrecalculus.com
we are going to create a linear regression model from a set of data. Suppose we get a job working for a company that provides their employees with company stock every year they work with the company. The amount of stock earned may be based on a few other variables, but all we are looking at right now is time served with the company. Here is the data we collected: Years With Company Company Stock (Thousands of $) 11 51 14 64 9 41 8 40 14 61 12 54 3 21 7 31 8 39 10 51 7 27 Use the following link to graph your data and create a regression model. The equation of your model will be at the bottom of the page, where it says “Equation.” https://www.graphpad.com/quickcalcs/linear1/Links to an external site.
Find the required linear model using least-squares regression. The following table shows the number of operating federal credit unions in a certain country for several years. 2011 2012 2013 2014 4497 4323 4156 3976 Year Number of federal credit unions (a) The linear model for these data is y = (Round to the nearest tenth as needed.) (a) Find a linear model for these data with x = 11 corresponding to the year 2011. (b) Assuming the trend continues, estimate the number of federal credit unions in the year 2018. X + 2015 (b) The estimated number of credit unions for the year 2018 is (Round to the nearest integer as needed.) 3807
2.1 Sales personnel want to further develop the model to include other variables. Merchandise Sales ($10,000s) Tickets Sold (1000s) Win or Loss (W or L) Event includes fan giveaway (Y or N) 15 54 W N 17 67 L Y 10 53 L Y 9 49 W Y 16 59 W N 14 58 L Y 12 56 W N 19 63 W Y 18 65 L Y 17 61 L Y Should you keep either or both of the variables in the model and why? What is the regression equation (only include variables that should be in the model)? What is the merchandise sales forecast for 58,000 tickets sold at a winning event that does not include a fan giveaway?
9.2
A highway department is studying the relationship between traffic flow and speed. The following model has been hypothesized: y = B, + B,x + E where y = traffic flow in vehicles per hour x = vehicle speed in miles per hour. The following data were collected during rush hour for six highways leading out of the city. Traffic Flow (y) Vehicle Speed (x) 1,257 35 1,329 40 1,227 30 1,335 45 1,351 50 1,123 25 In working further with this problem, statisticians suggested the use of the following curvilinear estimated regression equation. ý = bo + b,x + b,x? (a) Develop an estimated regression equation for the data of the form ý = b, + b,x + b,x2. (Round b, to the nearest integer and b, to two decimal places and b, to three decimal places.)
give handwritten answer of the question-Which one of the following is the most used model or calibration curve for a one-component system in quantitative analysis? Y = bo + b1X1 + b2X2 + error Y = bo + b1X + b1X2 + error Y = bo + b1X + error Y = bo + b1X1 + b2X2 + b3X1X2 + error
Statistics students in Oxnard College sampled 9 textbooks in the Condor bookstore and the recorded number of pages in each textbook and its cost. The data is shown below: Number of pages (x) Cost (y) 850 77.5 288 34.92 762 80.58 698 61.82 955 82.95 571 51.39 547 57.23 434 50.06 430 35.7 A student calculates a linear model using technology y = ______ x + _______.…
Training Dept. of Nimrod Inc wants to develop a regression-based compensation model (compensation in $ per year, Comp) for its mid-level managers to encourage performance, loyalty, and continuing education based on three variables. ▪ Business unit-profitability (Profit per year in $). ▪ Working experiences in Nimrod Inc (Years). ▪ Whether or not a manager has a graduate degree (Grads). If a manager has a graduate degree equals 1, 0 otherwise. Table Attached Question: Which explanatory variables and interaction terms are significant and not significant at alpha = 5%? Explain your answer briefly.

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