In a company introducing a new product on the market it was decided to build a model explainingthe dependence of the sales volume Y (in thousands of units) on the tested price of the product X_1(in PLN) and expenditure on promotion and advertising of the product X_2 (in thousands of PLN).Based on the data given in the table belowXt220X+1Yt1516201930223123352530correlation coefficients between variables were calculated and obtained[-0, 980,86.Ro= - vector of correlation coefficients of the endogenous variable with potential explanatory variables,1-0,9- matrix of correlation coefficients between pairs of potential explanatory variables.R=-0,9Find:(e) Interpret the results obtainedUse Excel or MatLab or other.987 655

Given information: It is given that a company is introducing a new product on the market and it was…

Answered: In a company introducing a new product…

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 maintenance manager at a trucking company wants to build a regression model to forecast the time (in years) until the first engine overhaul based on four predictor variables: (1) annual miles driven (in 1,000s of miles), (2) average load weight (in tons), (3) average driving speed (in mph), and (4) oil change interval (in 1,000s of miles). Based on driver logs and onboard computers, data have been obtained for a sample of 25 trucks. A portion of the data is shown in the accompanying table. Time Miles Load Speed Oil 7.7 42.9 22.0 44.0 16.0 0.8 98.3 20.0 47.0 34.0 6.3 61.1 22.0 62.0 15.0 E Click here for the Excel Data File b. Estimate the regression model. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Time = Miles Load Speed oil + + d. What is the predicted time before the first engine overhaul for a particular truck driven 60,000 miles per year with an average load of 25 tons, an average driving speed of 53 mph, and 21,000 miles…
The demand and forecast information for the XYZ Company over a twelve-month period has been collected in the Microsoft Excel Online file below. Use the Microsoft Excel Online file below to develop forecast accuracy and answer the following questions. Forecast Accuracy Measures Period Actual Demand Forecast Error Absolute Error Error^2 Abs. % Error 1 1,300 1,378 2 2,000 1,676 3 1,800 1,974 4 1,700 2,272 5 2,300 2,570 6 3,800 2,868 7 3,200 3,166 8 3,100 3,464 9 3,900 3,761 10 4,600 4,059 11 4,200 4,357 12 4,300 4,655 Total Average RSFE MAD MSE MAPE Tracking Signal 1. What can be concluded about the quality of the forecasts? Assume that the control limit for the tracking signal is ±3. The results indicate (bias or no bias) in the…
Beachcomer Ltd is a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model. SALES = a+BAGE + yMIL + SENG Where SALES = amount that a vehicle is sold for (000's), AGE = age of vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained: The regression equation is ***** Coef StDev t-ratio p-value Predictor Constant 1.7586 0.2525 6.9648 0.0000 AGE 0.2124 0.3175 * 0.5042 MIL -0.7527 0.3586 -2.0991 ** ENG 4.8124 0.6196 7.7664 0.0000…
A method of estimating earth temperature based on local precipitation is to build models based on 100s of years of earth at several different constant green house gas level (So constant global temperature for 100s of years) , and compare the correlation of statistical relationships between predicted precipitation patterns in a region, and observed precipitation patterns for a region in a few year time period.
Please answer as many as your allowed too. Thank you :) A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x).The results of the regression were: ˆyy^=a+bxa=-1.68b=0.168 (a) Write the equation of the Least Squares Regression line of the formˆyy^= + x(b) Which is a possible value for the correlation coefficient, rr? -1.417 1.417 0.702 -0.702 (c) If a country increases its life expectancy, the happiness index will increase decrease (d) If the life expectancy is increased by 0.5 years in a certain country, how much will the happiness index change? Round to two decimal places.(e) Use the regression line to predict the happiness index of a country with a life expectancy of 69 years. Round to two decimal places.
Employee’s human capital is measured in terms of his/her age, education, tenure, and rank. Number of years an employee worked in the same organization measures the amount of firm-specific human capital that employee possesses. Data of 413 employees’ tenure was analyzed to predict interim leadership role importance in a study reported in Journal of Advanced Management Research (Vol. 9, 2012). The following table shows the data of these employees tenure in the organization: Tenure (in years) 0-1 1-6 6-11 11-20 20-21 No. of employees 48 109 58 75 123 Calculate mean tenure of these 413 employees.
Beachcomer Ltd is a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model. SALES = a+BAGE + yMIL + SENG Where SALES = amount that a vehicle is sold for (000's), AGE = age of vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained: The regression equation is ***** Coef StDev t-ratio p-value Predictor Constant 1.7586 0.2525 6.9648 0.0000 AGE 0.2124 0.3175 * 0.5042 MIL -0.7527 0.3586 -2.0991 ** ENG 4.8124 0.6196 7.7664 0.0000…
Beachcomer Ltd is a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model. SALES = a+BAGE + yMIL + SENG Where SALES = amount that a vehicle is sold for (000's), AGE = age of vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained: The regression equation is ***** Coef StDev t-ratio p-value Predictor Constant 1.7586 0.2525 6.9648 0.0000 AGE 0.2124 0.3175 * 0.5042 MIL -0.7527 0.3586 -2.0991 ** ENG 4.8124 0.6196 7.7664 0.0000…
How do I make a hypothesis(es) to predict the effect of a manipulation of an independent variable on a quantitative dependent variable when flying an airplane?
Discuss on nominal data
I need help with part b please to find the two models which use the best subset of variables. thanks

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