Question 8The OLS regression of Garment Embroidery total costs on the number of machine hours and thenumber of operator hours. The estimated coefficient on the machine hours is 10 with a p-value lessthan 0.01. The estimated coefficient on the operator hour is 30 with a p-value equal 0.40. What isthe estimated cost for an order that requires 100 machine hours and 50 operator hours?$1,000$1,500$2,500None of above

Given Information: There are two independent variables: X1:Number of machine hours X2: Number of…

Answered: The OLS regression of Garment…

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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Suppose an oceanographer monitors the daily salinity of a particular ocean in relation to the temperature of the water. The oceanographer plots the data with temperature, in degrees Celsius (°C), along the horizontal axis and salinity, in parts per thousand (ppt), along the vertical axis. Select the true statement about the data point identified by the arrow. An ocean temperature of 21 °C causes the salinity to be 29 ppt. The predicted salinity is 29 ppt when the ocean temperature for the day is 21 °C. An ocean temperature of 29 °C corresponds to a salinity level of 21 ppt. The observed salinity is 29 ppt when the ocean temperature for the day is 21 °C. There is no relationship between the temperature of the ocean on a given day and salinity.
Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with the gender dummy variable. Regression Statistics Multiple R 0.963 R Square Adjusted R Square Standard Error Observations 20 ANOVA df SS MS F Significance F Regression 24686354.49 6171589 47.8 2.3289E-08 Residual 129243 Total 26625000…
Which equation is the best fit for the data in the table? X Y 3 12 5 27 8 48 11 O y=-.0026x^2+.33x+1.16 O y=1.94x+2.140 O y=1.732vx-1 O y=1.732x-1
You are a big soccer fan and you decide to gather information on the goal differential (goals scored - goals allowed) for your favorite team in the Premier League during the last season. You find that the average goal differential for home games (games that take place on the team's own stadium) was 1.37 and the average goal differential for away games (games that take place on the opponent team's stadium) was 0.32. With the data you have, if you regress goal differential (call it variable G) on a dummy variable (call it variable D) that takes the value 1 if the game is a home game and takes the value zero if the game is an away game, and obtain Ĝi = Bo + B₁ D₁, then, Bo will be equal to equal to and ₁ will be
I’m taking a statistics and probability class. Please get this correct because I want to learn. I have gotten wrong answers on here before
Shelia’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 136 milligrams per deciliter (mg/dl) one hour after ingesting a sugary drink. Shelia’s measured glucose level one hour after the sugary drink varies according to the Normal distribution with μ = 125 mg/dl and σ = 11 mg/dl. a)What are the mean and standard deviation of the distribution of sample proportion if measurements are made instead on 40 separate days? b)If measurements are made on 40 separate days and the mean result is compared with the criterion 140 mg/dl, what is the probability that Shelia is diagnosed as having gestational diabetes? c)Let’s consider what could happen if we took 40 separate measurements from Shelia. What is the blood glucose level L such that the probability…
3. Describe the problem that outliers present for a regression analysis and outline what you could do to resolve this problem.
Give me an example How Efficient are Regression analysis for Estimating Costs?
Q1) A real estate consultant is considering developing a series of price models for residential houses in different areas of the province of Ontario, in Canada. The dataset, provided by the Windsor and Essex County Board, covers residential home sales in Windsor. To develop the model, the consultant performs a linear regression to estimate the price (in Canadian dollars) as a linear function of the size of the apartment (in square feet) and obtains the following Excel result. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.53580413 0.28708607 0.28577556 41101.9362 546 ANOVA df MS Significance F 1 3.70082E+11 3.7008E+11 219.065461 6.74643E-42 Regression Residual Total 544 9.19017E+11 1689369158 545 1.2891E+12 Coefficients Standard Error 62171.5874 4537.036582 13.7031268 6.2969E-37 53259.33066 71083.8441 t Stat P-value Lower 95% Upper 95% Intercept LOTSIZE 12.0187669 0.81203165 14.8008601 6.7464E-42 10.42366523 13.6138685 a) Write…
Please answer letters b-e.
Q1. A psychologist conducted a study on the relationship between introversion and shyness. The values are results for scales of introversion and shyness. High positive scores on each scale indicate high introversion or shyness; and high negative scores indicate low introversion or shyness. Data collected from 10 people as follows: Introversion 4 -7 -1 0 6 7 -4 -9 -5 8 Shyness 11 -7 -1 -3 0 7 -1 -8 -1 5 a) Compute the correlation coefficient relating introversion and shyness scores. b) Find the regression equation which predicts introversion from shyness. c) What introversion score would you predict from a shyness score of 7?

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