1. The relationship between energy consumption and householdincome was studied, yielding the following data on householdincome X (in units of $1000/year) and energy consumption Y(inunits of 10^8 Btu/year).EnergyHousehold income (x)consumption (y)20.01.83.030.54.840.055.160.374.988.495.2a. Estimate the linear regression equation.5.06.57.09.09.1b. If x=50 (household income of $50,000), estimate the averageenergy consumed for households of this income. What wouldyour estimate be for a single household?

The independent variable is Household income. The dependent variable is Energy consumption. We have…

Answered: 1. The relationship between energy…

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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HW5 The number of pounds of steam used per month by a chemical plant is thought to be related to the average ambient temperature (in F) for that month. The past year’s usage and temperatures are in the following table: Month Temp. Usage/1000 Month Temp. Usage/1000 Jan. 21 185.79 July. 68 621.55 Feb. 24 214.47 Aug. 74 675.06 Mar. 32 288.03 Sept. 62 562.03 Apr. 47 424.84 Oct. 50 452.93 May 50 454.58 Nov. 41 369.95 June 59 539.03 Dec. 30 273.93 Assuming that a simple linear regression model is appropriate, fit the regression model relating stem usage (y) to the average temperature (x). What is the estimate of ? What is the estimate of expected stem usage when the average temperature is 55 F? What change in mean stem usage is expected when the monthly average temperature changes by 1 F? Suppose that the monthly average temperature is 47 F. Calculate the fitted value of y and the corresponding residual. Test for significance of regression using α=0.01 (Use ANOVA).…
Below is data collected over 6 specific years. The data collected is the Consumer Price Index (CPI) and the cost of a slice of tuna. We would like to build a model using the CPI to predict the cost of a slice of pizza in a given year. Year 1960 1973 1986 1995 2002 2003 CPI (x) 30.2 48.3 112.3 162.2 191.9 197.8 Cost of a slice of tuna(y) 0.15 0.35 1.00 1.25 1.75 2.00 Plot the data. Are we justified in creating a Linear Regression model to predict the price of a pizza slice using the CPI? Why or why not? Calculate the descriptive statistics. Calculate the sums of squares Calculate the slope, intercept, and correlation. What is this correlation value telling us? Assemble the prediction equation, and interpret the slope within the context of the problem. State the hypotheses for the hypothesis test for regression.
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…
A firm experienced the demand shown in the following table. Fill in the table by preparing forecasts based on a five-year moving average, a three-year moving average, and exponential smoothing (w=0.9w=0.9 and w=0.3w=0.3). (Note: The exponential smoothing forecasts may be begun by assuming Yˆt + 1=YtY^t + 1=Yt.) Year Actual Demand Moving Average Exponential Smoothing (5-year) (3-year) (W = 0.9) (W = 0.3) 2000 800 2001 790 2002 785 2003 785 792 2004 790 787 2005 805 790 787 2006 825 791 794 2007 850 798 807 2008 825 811 827 2009 860 819 834 2010 * 833 846 The following table shows the square errors, (Yt−Yˆt - 1)2Yt−Y^t - 12, for forecasts from 2005 through 2009. Fill the table by calculating the root mean square error (RMSE) for each of the methods. Year Square…
World Production of Coal A study proposed in 1980 by researchers from the major producers and consumers of the world's coal concluded that coal could and must play an important role in fueling global economic growth over the next 20 years. Suppose that the world production of coal in 1980 was 5.5 billion metric tons. If output increased at the rate of 5.5e0.05t billion metric tons/year in year t (t = 0 corresponding to 1980), determine how much more coal was produced worldwide between 1980 and the end of the twentieth century. (Round your answer to one decimal place.)
Q1. Assume that the rate at which radioactive nuclei decay is proportional to the number of such nuclei that are present in a given sample. In certain sample 10% of the original number of radioactive nuclei have undergone disintegration in a period of 100 years. a) What percentage of the original radioactive nuclei will remain after 1000 years? b) In how many years will only one-fourth (1/4) of the original number remain?
36. From the following data, estimate the number of persons earning between Rs. 360 and Rs. 370 per day. Wages (in Rs.) (e-4)No. of persons (in '000) 250 Below 340 120 340-360 100 360-380 70 380-400 50 400-420

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