Q2. Consider an experiment to compare six oil refineries. Three refineries are in Texas and three are inOklahoma The observed variable is the amount of gasoline produced in a day. For each refinery,there are obscrvations on two days. We write a model for these data asYjk =H+a; + Bij +ejk; i=1, 2;j=1,2, 3; k= 1, 2.Here a refers to the state, and B refers to the refinery within state. This model can be written as alincar model Y = XBß+e, where, B = (µ, a,, az, B11, Bı2, B13, B21, B22, B23)'.(a) Write out Y, X, and e for this model.(b) Compute XX.(c) Show that X is not of full column rank. Specifically, show that there are three columns of X that canbe written as linear combinations of the other six columns.(d) Show that the remaining six columns of X are linearly independent.%3D

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Answered: Q2. Consider an experiment to compare…

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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2. The line of best fit for a data set is y = 1. 1x + 3. 4. Find the residual for each of the coordinate pairs, (x, y). a. (5,8.8) b. (2.5,5.95) c. (0,3.72) d. (1.5,5.05) e. (-3,0) f. (-5,- 4.86) 284 ort pe ADOS SU 14
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.5 8.4 6.9 3.9 2.7 2.8 2.2 0.6 yy 13.7 11 10 7 6.3 6.4 6.2 4.8 xx = thousands of automatic weaponsyy = murders per 100,000 residentsThis data can be modeled by the equation y=0.82x+4.16.y=0.82x+4.16. 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 3.9 thousand automatic weapons?Answer = Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 1.7 thousand automatic weapons?Answer = Round to 3 decimal places.
Below is the data for speed vs fuel consumption. The first set is speed(x); the second is fuel (y). if x = 10, y = 21 if x = 20 y =13, if x=30 y=10 if x = 40 y =8 if x = 50 y=7 if x =60 y = 5.9 if x = 70 y = 6.3 if x = 80 y = 6.95 if x = 90 y = 7.57 if x = 100 y = 8.27 if x = 110 y = 9.03 if x = 120 y = 9.87 if x = 130 y = 10.79 if x = 140 y = 11.77 and if x = 150 y =12.83. The equation of the LSR line is y^ = 11.058 - .015x The average of (x, y) are (80, 9.9) respectively. Find another point using the LSR equation, and graph the line
The table shows the lengths and weights of seven muskies captured by the Department of Natural Resources in Catfish Lake in Eagle River, Wisconsin. Musky 22 y = 20 18 16 14 (a) Construct a scattergram for the data. (Let x stand for length and y stand for weight.) 12 No Solution 10 8 6 1 4 2 3 4 5 6 7 Help 2 24 Length (in.) 26 26 27 29 33 35 36 38 28 Weight (lb) 30 5 8 9 12 14 14 19 32 34 36 40 Clear All Delete Fill 38 WebAssign. Graphing Tool Graph Layers << After you add an object to the graph you can use Graph Layers to view and edit its properties. (b) Assume a linear relationship and write a regression equation. (Use the points (26, 5) and (38, 19) for the regression line.) (c) Estimate the weight of a musky that is 31 inches long. (Round your answer to the nearest integer.) lb
Answer the following: 2. Describe the form, direction, and strength of the relationship between these variables. Does there appear to be any outliers? 3. The equation of least-squares regression line is y = 6.871 + 0.061x. This line goes through the points (0, 6.9) and (700, 49.5). Use these points to add the regression line to your scatterplot above. 4. One animal is farthest from the regression line and is an outlier for these two variables. Find the name of this animal and write a sentence describing how its position differs from the overall relationship among the other animals. 5. The elephant has the longest gestation period of these animals. a. Considering only the gestation variable, would you say that the elephant is an outlier? Write a sentence or two explaining why or why not? b. Considering both variables and the relationship between average life span and average gestation period, would you say the elephant is an outlier now? Write a sentence or two explaining why or why…
#9 only. Just use #8 for reference
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.5 8.2 6.9 3.3 2.8 2.2 2.6 0.6 yy 13.7 10.9 10.2 7 6.1 5.8 6.3 4.2 xx = thousands of automatic weaponsyy = murders per 100,000 residentsThis data can be modeled by the equation y=0.87x+3.9.y=0.87x+3.9. Use this equation to answer the following;A) How many murders per 100,000 residents can be expected in a state with 2.5 thousand automatic weapons?Answer = Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 5.2 thousand automatic weapons?Answer = Round to 3 decimal places. Submit QuestionQuestion 17
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.6 8.5 7.2 3.6 2.6 2.3 2.1 0.4 yy 13.8 11.4 9.9 7.1 5.9 5.8 5.8 4.7 xx = thousands of automatic weaponsyy = murders per 100,000 residentsThis data can be modeled by the equation y=0.84x+4.02.y=0.84x+4.02. 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 4.9 thousand automatic weapons?Answer = Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 10.3 thousand automatic weapons?Answer = Round to 3 decimal places.
Kenneth is studying the relationship between the time spent exercising per day and the time spent outside per day. Data were collected ranging from 20 minutes to 90 minutes per day exercising. The line of best fit for the data is given below. Assume the line of best fit is significant and there is a strong linear relationship between the variables. y= 0.13x + 40.5 Answer the following (each is equally weighted): 1. Identify the explanatory and response variables (make sure your answer is related to the application and not just in terms of x and y). 2. According to the line of best fit, what would be the predicted number of minutes spent outside for someone who spent 70 minutes exercising? 3. Would it be appropriate to use this model to predict the number of minutes spent outside for someone who spent 0 minutes exercising? Why or why not (remember to include appropriate terminology).
2. Find the least linear fit to the following data: * * 2 3 2 -3 -3 -1 55
Two refreshment stands kept track of the number of cases of soda they sold weekly during the summer time, as shown on the dot plots below. Stand A Stand B 10 11 12 13 14 15 16 17 18 19 10 11 12 13 14 15 16 17 18 19 Number of Cases Sold Number of Cases Sold What is the difference between the modes of the number of cases of soda sold? OA. 11 ов. 2 ос. 5 OD. 16
8

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