Given are five observations for two variables, and y.The estimated regression equation for these data is = 75.74-2.92x.a. Compute SSE, SST, and SSR.YiThe least squares line provided an - Select your answerdecimal).fit;4SSE(to 2 decimals)SST(to 2 decimals)SSR(to 2 decimals)b. Compute the coefficient of determination ². Comment on the goodness of fit.(to 3 decimals)0015 18 2057 57 46 17 12Check My Work (2 remaining)% of the variability in y has been explained by the estimated regression equation (to 1c. Compute the sample correlation coefficient. Enter negative value as negative number.(to 3 decimals)

Regression are used to predict the future value over the another. It explain the relationship…

Answered: Given are five observations for two…

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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Suppose that for a typical FedEx package delivery, the cost of the shipment is a function of the weight of the package measured in ounces. You want to try to predict the cost of a typical shipment given package dimensions. If 10 packages in a city are sampled and the regression output is given below, report the regression equation. 1) (cost of delivery) = 1.468*(weight) - 23.015 2) (weight) = -23.015*(cost of delivery) + 1.468 3) (cost of delivery) = -23.015*(weight) + 1.468 4) (cost of delivery) = 1.468*(weight) 5) (weight) = 1.468*(cost of delivery) - 23.015
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 1004 952 1237 1116 1177 1249 Temperature (°F) 83.1 76 95.1 87.5 92.1 88.4 What is the regression equation? (^ over y)=_____+_____x (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per minute? The best-predicted temperature when a bug is chirping at 3000 chirps per minute is ____°F. (Round to one decimal place as needed.) What is wrong with this predicted value? Choose the correct answer below. A. It is…
The money raised and spent (both in millions of dollars) by all congressional campaigns for 8 recent 2-year periods are shown in the table. The equation of the regression line is y = 0.940x + 34. 128. Find the coefficient of determination and interpret the result. 785.8 784.5 1032.7 963.8 1211.5 Money raised, x Money spent, y 456.6 654.6 733.8 449.4 691.4 734.6 764.4 739.3 1018.6 930.3 1172.9 Find the coefficient of determination and interpret the result. 2 = 0.989 (Round to hree decimal places as needed.)
Use the given data to find the scatter plot, equation of the regression line, and prediction. 4) Managers rate employees according to job performance and attitude. The results for several randomly selected employees are given below. Performance 59 63 65 69 58 77 76 | 69 70 64 Attitude 72 67 78 82 75 87 92 83 87 78 What function do you use to see the scatterplot on the calculator. Scatterplot from the calculator:
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable, Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? 1220 1075 986 88 5 843 821 849 697 758 1170 867 939 D Chirps in 1 min Temperature ("F) What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000 chirps per minute is F (Round to one decimal place as needed.) What is wrong with this predicted value? Choose the correct answer below O A. The first variable should have been the dependent variable O B. It is unrealistically high. The value 3000 is…
The table shows the average weekly wages (in dollars) for state government employees and federal government employees for 8 years. The equation of the regression line is y = 1.493x - 83.403. Complete parts (a) and (b) below. A Average Weekly Wages (state), x Average Weekly Wages (federal), y 764 1003 766 1048 791 1119 (a) Find the coefficient of determination and interpret the result. r² = 0 (Round to three decimal places as needed.) 800 1152 843 1201 887 1250 924 1277 939 1306
Used cars 2010 Vehix.com offered several used ToyotaCorollas for sale. The following table displays the ages ofthe cars and the advertised prices. a) Make a scatterplot for these data.b) Do you think a linear model is appropriate? Explain.c) Find the equation of the regression line. d) Check the residuals to see if the conditions for infer-ence are met. Age (yr) Price ($) Age (yr) Price ($)1 15988 6 99951 13988 6 119882 14488 7 89903 10995 8 94883 13998 8 89954 13622 9 59904 12810 10 41005 9988 12 2995
The table lists the average tuition and fees at private colleges and universities for selected years. Year 1985 1990 1995 2000 2008 5311 25,115 Tuition and Fees (in dollars) 9375 SO 12,418 (a) Find the equation of the least-squares regression line that models the data. y 840.450 (Type the slope as a decimal rounded to three decimal places. Round the y-intercept to the nearest integer.) (b) Graph the data and the regression line in the same viewing window using the parameters given below the graph choices. Choose the correct graph below. OA. O B. o HE RO OC. 16,230 A o ƠN
The accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.972. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y= - 0.0070x + 44.4405. Complete parts (a) and (b) below. Click the icon to view the data table. ..... (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in is by the linear model. Data Table (Round to one decimal p Full data set gas mileage Miles per Weight (pounds), x Weight (pounds), x Miles per Gallon, y Car Car Gallon, y…
Which of the following data sets or plots could have a regression line with a negative slope? Select all that apply. Select all that apply: the difference in the number of ships launched by competing ship builders as a function of the number of months since the start of last year the number of hawks sighted per day as a function of the number of days since the two-week study started the total number of ships launched by a ship builder as a function of the number of months since the start of last year the average number of hawks sighted per day in a series of studies as a function of the number of days since the ten-week study started
11. For temperature (x) and number of ice cream cones sold per hour (y). (65, 8), (70, 10), (75, 11), (80,13), (85, 12), (90, 16). Interpret the coefficient of determination. Optional Answers: 1. 88.2% of the variability in the number of cones sold is explained by the least-squares regression model. 2. 93.9% of the variability in the number of cones sold is explained by the least-squares regression model. 3. 88.2% of the variability in the temperature is explained by the least-squares regression model. 4. 93.9% of the variability in the temperature is explained by the least-squares regression model.

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