Retail price data for n= 60 hard disk drives were recently reported in a computer magazine. Three variables were recorded for each hard disk drive: y = Retail PRICE (measured in dollars) X1 = Microprocessor SPEED (measured in megahertz) (Values in sample range from 10 to 40) x2 = CHIP size (measured in computer processing units) (Values in sample range from 286 to 486) A first-order regression model. was fit to the data. Part of the printout follows: Parameter Estimates T FOR 0 ERROR PARAMETER =0 PROB>ITI PARAMETER STANDARD VARIABLE DF ESTIMATE INTERCEPT 1 -373.526392 1258.1243396 -0.297 0.7676 1 104838940 1 3.571850 Identify and interpret the estimate of B2- SPEED 22.36298195 4688 0.0001 CHP 3.89422935 0.917 0.3629

Retail price data for n= 60 hard disk drives were recently reported in a computer magazine. Three variables were recorded for each hard disk drive: y = Retail PRICE (measured in dollars) X1 = Microprocessor SPEED (measured in megahertz) (Values in sample range from 10 to 40) x2 = CHIP size (measured in computer processing units) (Values in sample range from 286 to 486) A first-order regression model. was fit to the data. Part of the printout follows: Parameter Estimates T FOR 0 ERROR PARAMETER =0 PROB>ITI PARAMETER STANDARD VARIABLE DF ESTIMATE INTERCEPT 1 -373.526392 1258.1243396 -0.297 0.7676 1 104838940 1 3.571850 Identify and interpret the estimate of B2- SPEED 22.36298195 4688 0.0001 CHP 3.89422935 0.917 0.3629

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Suppose researchers are interested in exploring the factors which affect depression for Australian adults. The researchers recruited a sample of 99 Australian adults and collected data on several variables which may influence depression. Note that here depression is represented by a score, with higher values representing higher levels of depression. The variables for this study are listed below: Age Gender (0 = female, 1 = male) Stress level Anxiety level Depression Prior to running their analysis (multiple regression), the researchers would like to check the regression assumptions for the data. Which of the assumptions below is not an assumption for multiple regression? A) Sphericity B) Linearity and Additivity C) Equal variances D) Normality E) Metric scales
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 47 48 56 60 67 Bone Density 359 350 334 314 313 Find the estimated slope. Round your answer to three decimal places.
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0 0.5 2 3 4.5 5 6 Overall Grades 99 98 96 92 89 88 80 Table Step 6 of 6 : Find the value of the coefficient of determination. Round your answer to three decimal places.
What is the differed annual expenditures of two families if their annual net incomes are differed by 2000? The computed regression line has a value of a=4.32 and b=2.12.
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density48 35151 32056 31860 31169 310 Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places.
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0.5 1 2.5 3 4 5 5.5 Overall Grades 98 95 90 79 75 69 66 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6: Determine if the statement "Not all points predicted by the linear model fall on the…
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 35 43 53 54 55 Bone Density 350 340 339 321 310 Table Step 6 of 6 : Find the value of the coefficient of determination. Round your answer
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
A local retail store compared their monthly sales of umbrellas with the amount of rainfall that occured during that month. They computed the following statistics: Rainfall (in) # of umbrellas mean = 4.64 mean = 34.2 SD = 1.17 SD = 13.2 r = 0.8 1. Find the equation for the regression line that predicts the monthly sales of umbrellas from monthly rainfall.
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0.5 1.5 2 2.5 3 4 4.5 Overall Grades 97 93 74 73 65 61 60 Table Step 2 of 6 : Find the estimated y-intercept. Round your answer to three decimal
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density34 35745 34148 33160 32965 325 Step 3 of 6: Determine the value of the dependent variable yˆ at x=0.
Let price denote a price index for the goods sold by a restaurant, advert the amount spent on advertising, sales the sales for the restaurant, and consider the following two regressions First regression: sales = B1 + B2price + B3price? + B4advert + ßsadvert? + e, Second regression: sales = B1 + B2price + B3price? + e We estimate both regressions using a sample of 105 observations. The sum of square residuals (E ê) from the first regression equals 50, while the sum of square Li=1 residuals from the second regression equals 70. Suppose we are interested in testing the null hypothesis that expected sales do not depend on advertising. What is the F- statistic for this null hypothesis? Recall the F-statistic is given by ((SSER - SSEU)/J)/(SSEy/(n – K)). O a. -15 O b. 42 Oc. 21 O d. 20 O e. All other options are incorrect.