Suppose that you are modelling cooling costs (y) as a function of the daily high temperature(x) for a building, using a simple linear regression model. You have data consisting of pairs ofobservations (x, y) over many years. Suppose that from this data you find a Pearsoncorrelation between x and y of 0.5.If tomorrow the temperature is 3 standard deviations above the mean, how many standarddeviations above the mean would you expect the cooling costs to be?If it is true that, tomorrow, the temperature is as given in the previous problem, then theexpectation that you gave is the exact cooling cost that will occur.TrueO False

Correlation is the measure of the strength of association between two variables. The value of…

Answered: Suppose that you are modelling cooling…

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 data below is the amount of tar and nicotine in a sample of different brands of 100 mm, non-menthol, filtered cigarettes. a) Using the calculator, find a linear regression model that gives the amount of nicotine, N, as a function of the amount of tar, t. Write the function below. N(t)= b) use this model of nicotine to estimate the amount of nicotine in a cigarette if the amount of tar is listed as 21 mg. c) What is the correlation coefficient, r, for this model? r= Tar 5 16 17 13 13 14 9 15 2 15 13 14 15 16 7 17 Nicotine 0.4 1 1.2 0.8 0.8 1 0.8 1 0.2 1.1 0.8 1 0.9 1.1 0.6 1.3
What is regression models?
(b) What is the change in mean chlorine content when the degree of delignification increases from 7 to 8?What is the change in mean chlorine content when the degree of delignification increases from 9 to 10?
(4) Does how wide a possum's belly is (in cm) tell you how long a possum's tail is (in cm)? That's what re- searchers in Australia wanted to know. They built a simple linear regression model treating X as the possum's belly girth and Y as the possum's tail length. Below and at the top of the next page are all the results you will need to answer the ensuing questions, including: ● The straight line of best fit: ŷ = 30.2 + .21x • The correlation between the observed y and predicted ŷ: ry,ŷ .294 • At the top of the next page are three residual plots: (a) the plot of belly girth vs. the residuals, (b) the plot of the residuals in order of collection, (c) the histogram of the residuals = These are real data (source) consisting of measurements on each of 104 mountain brushtail possums, trapped at seven sites from Southern Victoria to central Queensland, Australia.
Advertising aimed at teenagers tends to contain dance-beat music, adventure, and a fast pace rather than words. To test to see how teenagers respond to different types of commercials, 150 teenagers then watched a set of commercials with dance-beat music, adventure, and a fast pace. After the commercials, the teenagers were asked whether they would want to buy the product and 102 responded that they would buy the product. Another sample of 125 teenagers where shown a set of commercialscomprised primarily of words. After the commercials, the teenagers were asked whether they would buy the product and 60 responded that they would like to buy the product. Based on both samples, at the 5% level of significance, test the hypothesis that there is at least a 10% difference in the proportion of teenagers who would buy the product after watching the different types of commercials?
A negative correlation between variables X and Y will always result in a positive slope in the linear regression model. Cannot tell from the given information. False True
The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) mported by a country, for the last seven years. Construct and interpret a 95% prediction interval for the amount of crude oil imported by the this country when the amount of crude oil produced by the country is 5,508 thousand barrels per day. The equation of the regression line is y = - 1.120x + 15,839.271. Oil produced, x 5,684 5,654 5,452 5,157 5,061 5,030 5,826 9,680 10,041 10,154 10,121 10,060 Oil imported, y 9,304 9,105 Construct and interpret a 95% prediction interval for the amount of crude oil imported when the amount of crude oil produced by the country is 5,508 thousand barrels per day. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) and O A. We can be 95% confident that when the amount of oil produced is 5,508 thousand barrels, the…
The linear regression equation for predicting systolic blood pressure from age is as follows: y = 42 + 2.5x . Find the residual for a person who is 21 years of age with a systolic blood pressure of 91.9.
This graph shows data on income and happiness from a sample. The fitted linear is based on a linear regression of happiness on income. According to this graph, which of the following is true? (Select all that apply.) happiness оооо 6- + 2- 0- income The slope of the regression line is negative. There is a positive relationship between income and happiness. More income causes more happiness. As income increases, people tend to be happier.
The age and height (in cm) of 400 adult women from Bolivia were measured. A researcher wants to know if age has any effect on height. A linear regression is carried out in Minitab and the following output obtained. Coefficients Term Constant Age (a) Write down the regression model. (b) Interpret the regression coefficient for the fitted model. (c) Use the output from Minitab to explain if the age of a participant affects their height. Percent (d) The normal probability plot of the residuals from this regression model is given below. Do the assumptions of the regression model seem reasonable? Justify your answer. 99.9 8 28 22299229 88 Coef SE Coef 152.94 7.69 0.022 0.231 01 -100 T-Value P-Value VIF 19.90 0.000 0.10 0.924 1.00 -50 Normal Probability Plot (response is Height) 0 Residual 50 ***** 100 150
The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last 7 years. Construct and interpret a 98% prediction interval for the amount of oil imported by the country and the amount of oil produced by the country is 5,483 thousand barrels per day. The equation of the regression line is y = -1.194x + 16,234.906
The data shown below are the initial weights and gains in weight (in grams) of female rats on a high protein diet from 24 to 84 days of age. The point of interest in these data is whether the gain in weight is related to (dependent on) the initial weight. If so, then feeding experiments on female rats can be made more precise by adjusting for differences in initial weights of the rats. Statistically test this with a regression analysis. Rat Number Initial Weight (gms) Weight Gain (gms) 1 50 128 2 64 159 3 76 158 4 64 119 5 74 133 6 60 112 7 69 96 8 68 126 9 56 132 10 48 118 11 57 107 12 59 106 13 46 82 14 45 103 15 65 104 I used excel's regression took pak and got the following: SUMMARY OUTPUT Regression Statistics Multiple R 0.489416452 R Square 0.239528464 Adjusted R Square 0.181030653 Standard Error 8.869929833 Observations 15…

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