A company that focuses on research and development (R&D) is interested in the relationship betweenprofit and some variables that can explain profit variations. They want to predict the profits of somenew and exciting projects because some are risky. The predictor variables considered are: RISK whichis a company derived metric, R&D which is the research and development costs, REGION (WC, ECand KZN) and average per capita income (INCOME in R1000.00).The analyst decides to estimate the regression function with the following response function.Ỹ (profit) = bo + b₁XRisk + b₂X R&D +b3Xwc + b₁XEC + b₂XINCAnswer the following questions.4.1. The analyst considered the fact that the effects of multicollinearity may influence some analyticaland visual conclusions. Use the following results to show the process of establishing by using5extra sums of squares whether there is that multicollinearity present or not. Explain and interpretin detail.SSR(X) = 2573.72, SSR(X₁) = 2600.7, SSR(X2)X₁) = 1573.72, SSR(X₁| X5) = 400.2.4.2. One of the ways to address the problem of multicollinearity is to do ridge regression. Give a shortbut detailed explanation of the motivation and application of using ridge regression.

In the given research problem, there is a dependent variable which determines the profit and is…

Answered: A company that focuses on research and…

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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Sheila's doctor is concerned that she may suffer from gestational diabetes (high blood glucose levels during pregnancy). There is variation both in the actual glucose level and in the blood test that measures the level. A patient is classified as having gestational diabetes if her glucose level is above 155 milligrams per deciliter (mg/dl) one hour after a sugary drink. Sheila's measured glucose level one hour after the sugary drink varies according to the Normal distribution with μ = 140 mg/dl and σ = 11 mg/dl.Let X = Sheila's measured glucose level one hour after a sugary drink(a) P(X > 155) = (Use 3 decimal places)Suppose measurements are made on 3 separate days and the mean result is compared with the criterion 155 mg/dl.(b) P(X > 155) = (Use 3 decimal places)(c) What sample mean blood glucose level is higher than 95% of all other sample mean blood glucose levels? Hint: this requires a backward Normal calculation. (Use 2 decimal places)
Suppose you a manager for a local car dealership, and you want to use a linear regression model to predict the price of a used car. You decide to use four predictor variables - "Age' (how long the car has been in use since it was produced), "Dents" (the number of visible dents on the outside of the car), "Accidents" (the number of accidents the car has been in), and "mpg" (the fuel efficiency of the car, measured in miles per gallon). Your dataset contains this information for the past 120 cars sold at your dealership. Using this model, your analysis finds an R² of 37%. What is the F statistic of your analysis? Note: 1- Only round your final answer. Round your final answer to two decimal places.
Explain how to choose the dependent and independent variables in regression analysis used for costestimation
Many statistical textbooks define regression analysis as a statistical tool that is used mainly for description or prediction. Based on this statement, provide two examples of an application or applications using regression analysis for decision-making—one for description and one for prediction.
It is believed that the electrical energy that a chemical plant consumes each month is related to the average environmental temperature, the number of days in the month, the average purity of the product, and the tons of product manufactured. Historical data is available for the previous year and is presented in the following table (seen in the image=. Perform simple linear regression analysis
What are the functional forms of the regression model? Explain.
Isla Esme, Inc., sells tea products to various customers. In recent years, profits have been declining. The CFO of the business investigated the reasons for the profit decline and performed regression analysis for sales and costs. He determined that sales depend on product price, delivery speed, customer services, and marketing expenses. He also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales.Question: Define the coefficient of determination and explain what it means in this scenario.
A real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently sold homes in the space, the following variables are used in a multiple regression model. y = sales price (in thousands of dollars) x₁ = total floor area (in square feet) x₂ = number of bedrooms x3 distance to nearest high school (in miles) = The estimated model is as follows. =76+0.098x₁ +16x₂ - 8x3 Answer the questions below for the interpretation of the coefficient of X₂ in this model. (a) Holding the other variables fixed, what is the average change in sales price for each additional bedroom in a house? dollars (b) Is this change an increase or a decrease? O increase O decrease X
To determine if additional shelf space to the store brand canned vegetables or increasing advertising expenditures would be more effective strategy to expand sales would I use a simple linear model, multiple linear regression or an interaction model? What equation would I use
If data set has a relationtionship that is best described by a linear model, then the residual plot will
What needs to change in an overall problem if one wants to use linear regression?
APPLIED STATISTICS UPVOTE WILL BE GIVEN. YOU MAY USE EXCEL OR IBM SPSS. PLEASE WRITE/TYPE THE COMPLETE SOLUTIONS. TAKE A SCREENSHOT IF EXCEL OR SPSS IS BEING USED. An article in Technometrics by S. C. Narula and J. F. Wellington (“Prediction, Linear Regression, and a Minimum Sum of Relative Errors,” Vol. 19, 1977) presents data on the selling price (y) and annual taxes (x) for 24 houses. The taxes include local, school and county taxes. The data are shown in the table below. Calculate the least square estimates of slope and intercept. Answer letter D.

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