The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. For a random sample of 33 Cadets, each bought "new" two years ago and each sold "used" within the past month, the least-squares regression equation relating the two variables mileage (denoted by xr) and used selling price (denoted by y, in dollars) was information, test for a significant linear relationship between the two variables by doing a hypothesis test regarding the population slope B, (Assume that the 39.52-0.56x. The standard error of the slope of this least-squares regression line was approximately 0.43. Based on this variable y follows a normal distribution for each value of x and that the other regression assumptions are satisfied.) Use the 0.10 level of significance, and perform a two-tailed test. Then fill in the table below. 图 (If necessary, consult a list of formulas.) The null hypothesis: H : 0 The alternative hypothesis: H :0 OSO The type of test statistic: (Choose one) - The value of the test statistic: (Round to at least three decimal places.) The p-yalue: (Round to at least three decimal places.) 口口 ? 日 B.

The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. For a random sample of 33 Cadets, each bought "new" two years ago and each sold "used" within the past month, the least-squares regression equation relating the two variables mileage (denoted by xr) and used selling price (denoted by y, in dollars) was information, test for a significant linear relationship between the two variables by doing a hypothesis test regarding the population slope B, (Assume that the 39.52-0.56x. The standard error of the slope of this least-squares regression line was approximately 0.43. Based on this variable y follows a normal distribution for each value of x and that the other regression assumptions are satisfied.) Use the 0.10 level of significance, and perform a two-tailed test. Then fill in the table below. 图 (If necessary, consult a list of formulas.) The null hypothesis: H : 0 The alternative hypothesis: H :0 OSO The type of test statistic: (Choose one) - The value of the test statistic: (Round to at least three decimal places.) The p-yalue: (Round to at least three decimal places.) 口口 ? 日 B.

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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9(20 pts). Correlation analysis and Simple Linear Regression. A study is con- ducted with a group of dieters to see if the number of grams of fat each consumes per day, x, is related to cholesterol level y. The data are shown in Spreadsheet "Question 8": Fat Grams x: 6.8 5.5 8.2 10 8.6 9.1 8.6 10.4 5.9 6.3 7.6 8 8.5 7.9 5.9 6.7 9.1 10.1 9.5 8.9 Cholesterol Level y: 212 192 193 263 222 250 190 249 190 185 192 201 215 189 203 194 241 256 255 245 pole sta ber of of perm lity o n = 20, Στ = 161.6, y = 216.85, Σ Υ = 4337, = 8.08, Σν = 955159. Στ? = 1347.52, Σ y = 35671.5, (a) Compute the value of the correlation coefficient, r, between x and y. (b) Test if the population correlation coefficient p > 0.5 at a = 0.05. (c) Determine the regression line equation y' = a + bx. (d) Predict the cholesterol level of a dieter who consumes x = 8.5 grams of fat per day. (e) Find the 90% prediction interval of the cholesterol level of a person who consumes x=8.5 grams of fat per day. at mo 9 mally tribu…
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, y = bo + b,x, 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 2 3 4.5 Overall Grades 99 98 96 92 89 88 80 Table Copy Data Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.
To determine the effectiveness of group study sessions, a college instructor gathers data on hours of attendance and exam scores for students in the class. Which variable, hours of attendance or exam scores, would be the response variable for a least-squares regression equation? is it hours of attendance or exam scores?
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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 Studying 0.5 1 1.5 2 3 3.5 4.5 Midterm Grades 63 66 68 72 74 93 94 Table Step 5 of 6 : Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the value of the independent variable is increased by one unit, then find the change in the dependent variable yˆ.
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + 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 Answer How to enter your answer (opens in new window) Bone Density 40 61 62 68 69 357 350 343 340 315 Step 6 of 6: Find the value of the coefficient of determination. Round your answer to three decimal places. Tables Copy Data Keypad Keyboard Shortcuts Table Previous step answers Submit Answer Dec 3 4:51 VI
Researchers measured the percent body fat and the preferred amount of salt (percent weight/volume) for several children. Here are data for seven children salt: Preferred amount of salt x 0.2 0.3 0.4 0.5 0.6 0.8 1.1 Percent body fat y 20 30 22 30 38 23 30 Using your calculator or software, what is the equation of the least-squares regression line for predicting percent body fat from preferred amount of salt? (a) yˆ = 24.2 + 6.0 x (b) yˆ = 0.1 5 + 0.0 1x (c) yˆ = 6.0 + 24.2 x
The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) through (c) below. Click the icon to view the data table. ..... (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for females. Find the least-squares regression line for males. y =x+O %D/ (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) Find the least-squares regression line for females. y = ý =x+O %3D (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) (b) Interpret the slope of the least-squares regression line for each gender, if appropriate. How might an insurance company use this information? What is the correct interpretation of the…
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, ŷ = bo + bịx, 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 2.5 3 4 4.5 Overall Grades 89 86 81 79 72 67 62 Table Copy Data Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.
Seven North American Green Frogs (Rana clamitans) had their jumping distance recorded (in mm) multiple times in a laboratory. The mean jumping distance for these frogs along with their length (measured from snout to vent in miMillimeters) are presented in the table below. Length of Frog 52 68 37 65 77 81 59 Mean Jumping Distance 546 673 415 659 793 814 563 (a) Determine the linear regression model that will best predit the mean jumping distance of a North American Green Frog based on the frog's length. (b) How well does the linear regression model fit this sample data? (c) Use the linear regression model to predict the mean jumping distance of a North American Green Frog that is 48 mm in length. No excel, please.
A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: Which of the independent variables in the model are significant at the 5% level? Formulate the hypothesis and explain the answer. How far can you rely upon this model? Or, what is the percentage variation in House explained by the model? What is the predicted house size (in hundreds of square feet) for an individual earning an annual income of IDR 400 million and having a family size of 4?
The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + bjx, 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 49 50 51 58 Bone Density 360 353 336 333 310 Table Copy Data Step 1 of 6: Find the estimated slope. Round your answer to three decimal places.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. 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 Studying 0.5 1 1.5 2 3 3.5 4.5 Midterm Grades 63 66 68 72 74 93 94 Table Step 4 of 6 : Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.