The amount of water used by the production facilities of a plant varies. Observations on water usage and other, possibly related, variables were collected for 17 months. The following model is fitted: USAGE = Bo + B1 TEMP + B2 PROD+ B3 DAYS+ B4 PAYR + Bs HOUR + E %3D where the variable is USAGE = monthly water usage (gallons/100) and the explanatory variables are response %3D TEMP = average monthly temperature (°F) amount of production number of operating days in the month number of people on the monthly plant payroll number of hours shut down for maintenance %3D PROD %3D DAYS PAYR HOUR Below is the selected SAS output with edit. Dependent Variable:NJSAGE monthly water usage (gallons/100) Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr> F Model Error 113.54388 Corrected Total 319.50011

The amount of water used by the production facilities of a plant varies. Observations on water usage and other, possibly related, variables were collected for 17 months. The following model is fitted: USAGE = Bo + B1 TEMP + B2 PROD+ B3 DAYS+ B4 PAYR + Bs HOUR + E %3D where the variable is USAGE = monthly water usage (gallons/100) and the explanatory variables are response %3D TEMP = average monthly temperature (°F) amount of production number of operating days in the month number of people on the monthly plant payroll number of hours shut down for maintenance %3D PROD %3D DAYS PAYR HOUR Below is the selected SAS output with edit. Dependent Variable:NJSAGE monthly water usage (gallons/100) Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr> F Model Error 113.54388 Corrected Total 319.50011

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 table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.8 8.6 6.8 3.7 2.8 2.6 2.6 0.9 14.1 11.5 9.8 6.9 6.6 6.5 6.2 4.6 thousands of automatic weapons y = murders per 100,000 residents %3D This data can be modeled by the equation y = 0.86x + 3.99. Use this equation to answer the following; A) How many murders per 100,000 residents can be expected in a state with 7.7 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 1.9 thousand automatic weapons? Answer = Round to 3 decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.3 8 6.8 3.9 2.6 2.4 2.1 0.3 yy 13.8 10.9 9.7 7.6 6.2 5.8 6.1 4.4 xx = thousands of automatic weaponsyy = murders per 100,000 residents This data can be modeled by the equation y=0.85x+4.07.y=0.85x+4.07. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 7.7 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 1 thousand automatic weapons? Answer = Round to 3 decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.4 8.5 6.6 3.6 2.3 2.5 2.1 0.6 y 13.4 11.4 9.3 7.2 5.9 5.9 5.6 4.3 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.85x + 3.88. Use this equation to answer the following: Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 10 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 9 thousand automatic weapons? Answer = Answer= Round to 3 decimal places.
You did not really answer my question. You only included dummy variables for MSS. You need to also include dummy variables for BOROUGH and the make the model variables for both MSS and BOROUGH. You were asked to help with analysis of birth weights (BW) of 10,000 infants born in NYC during a certain period of time. The aim of the analysis is to see whether the birth weights of the infants are associated with mothers AGE at birth (continuous variable in years) and mothers smoking status (the maternal smoking status MSS contains 4 categories “Non-smoker”, “Past-smoker”, “Passive-smoker”, “Smoker”) and NYC boroughs (the BOROUGH variable contains 5 categories “Manhattan”, “Bronx”, “Brooklyn”, “Queens” and “Staten Island”). Questions: 1.Write down the population model that estimates BW based on variables AGE and BOROUGH(with the dummy variables). Make sure that it is clear what each predictor means. 2.How many parallel lines are computed by model from population model you created above?…
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.9 8.6 7.1 3.8 2.3 2.3 2.2 0.3 y 14.3 11 9.9 7.3 5.7 5.7 5.6 4.1 x = thousands of automatic weaponsy = murders per 100,000 residentsThis data can be modeled by the equation y=0.87x+3.75. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator.A) How many murders per 100,000 residents can be expected in a state with 5 thousand automatic weapons?Answer = Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 10.5 thousand automatic weapons?Answer = Round to 3 decimal places.
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.
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The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.8 8.2 6.8 3.5 2.8 2.8 2.3 0.5 y 13.9 10.8 10.1 7.2 6.5 6.5 6.3 4.8 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.81x + 4.35. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 7 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 7.8 thousand automatic weapons? Answer = Answer = Round to 3 decimal places.
When coastal power stations take in large quantities of cooling water, it is inevitable that a number of fish are drawn in with the water. Various methods have been designed to screen out the fish. A particular article examined intake fish catch at an English power plant and several other variables thought to affect fish intake. y = fish intake (number of fish) = water temperature (°C) X1 x2 x3 X4 = number of pumps running = sea state (values 0, 1, 2, or 3) = speed (knots) Part of the data given in the article were used to obtain the estimated regression equation (based on n = 26). SSRegr = 1486.4 and SSResid = 2230.3 were also calculated. y = 92 2.18x₁-19.20x29.38x3 + 2.32x4 (a) What proportion of observed variation in fish intake can be explained by the model relationship? (Round your answer to four decimal places, if needed.) R² = (b) Estimate the value of o. (Round your answer to three decimal places, if needed.) Se (c) Calculate adjusted R². (Round your answer to four decimal…
Based on data from 34 adults who exercise regularly, the line of best fit for the relationship between bicep girth (the length in centimeters around the upper arm) and the person's weight is: predicted weight = 2.6 bicep girth – 10.5 %3D where predicted weight is measured in kilograms and bicep girth is measured in centimeters.
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 the glucose level is above 140 miligrams per deciliter (mg/dl) one hour after having a sugary drink. Sheila's measured glucose level one hour after the sugary drink varies according to the Normal distribution with μμ = 120 mg/dl and σσ = 10 mg/dl. (a) If a single glucose measurement is made, what is the probability that Sheila is diagnosed as having gestational diabetes?(b) If measurements are made on 5 separate days and the mean result is compared with the criterion 140 mg/dl, what is the probability that Sheila is diagnosed as having gestational diabetes?
Please answer parts d, e and f.