ou are given the following information about x and y. X Independent Variable Y Dependent Variable 15 5 12 7 10 9 7 11 The least squares estimate of b1 equals [3 decimals] ___________ The least squares estimate of b0equals _______________ The sample correlation coefficient equals _________

ou are given the following information about x and y. X Independent Variable Y Dependent Variable 15 5 12 7 10 9 7 11 The least squares estimate of b1 equals [3 decimals] ___ The least squares estimate of b0equals _ The sample correlation coefficient equals ___

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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R3.4 Stats teachers' cars A random sample of AP Statistics teachers was asked to report the age (in years) and mile- age of their primary vehicles. A scatterplot of the data, a least-squares regression printout, and a residual plot are provided below. R squared - 82.0% R squared (adjusted) -81.1% -19280 with 21 -2-19 degrees of freedom Variable Constant Carage Coefficient 7288.54 11630.6 se. of Coeff bratio prob LII 931 0.2826 6591 1249 0.0001 200 25 se 75 e.0 25 se 75 se.e Crge
R9aPZz-ipr3eOdpzGp3RGOBqpycXAh1ecHvdOkNzr0QLN1Q/formResponse 2 points A study of the amount of rainfall and the quantity of air pollution removed produced the following data: Particulate removed, y Daily rainfall, x (0.01) (mcg/cum) 4.3 126 4.5 121 5.9 116 5.6 118 6.1 114 5.2 118 3.8 132 2.1 141 7.5 108 Find the equation of the regression line to predict the particulate removed from the amount of daily rainfall. y = 153.175 +6.324x y=-153.175 +6.324x WXWX y=-153.175-6.324x XWXW O Ə WWXX M y=-153.175 - 6,324x XXWW
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Fill in the blanks.a. When a sum of squares is divided by its degrees of freedom, the ratio is called a _______.b. The F-statistic for testing the utility of a multiple linear regression equation is defined to be _______.c. The degrees of freedom for the F-statistic for testing the utility of a multiple linear regression equation involving k predictor variables are ________ and ______.
Regression The following data are measurements of the relative humidity in a storage location and the moisture content of a sample of raw material taken over 15 days. 1. Fit a least squares line and use it to predict the value of moisture content when the relative humidity is 65. 2. Find ŷ; values corresponding to x;. Relative humidity % Moisture content % 46 12 53 15 29 7 61 17 36 10 39 11 47 11 49 12 52 14 38 55 16 32 8 57 18 54 14 44 12
Q3.1 Cholesterol Before and After Treatment A random sample of 16 people with high cholesterol were given a new drug designed to lower cholesterol levels. The table contains the cholesterol levels (in milligrams per deciliter) measured before and after treatment for each person. Person 1 2 3 4 Before 237 248 260 266 After 176 178 191 192 Person 9 10 11 14 15 16 Before 283 284 286 289 292 293 295 299 After 215 212 204 214 214 192 216 206 Save Answer Do these data support the claim that the average reduction in cholesterol for people on this medication would be greater than 70 mm/dL? Q3.2 Conditions Hypotheses: Write the appropriate hypotheses, including defining the parameters and identifying the subscripts appropriately. (Note: you are not required to use Greek letters. For example, writing sigma is sufficient for σ.) Save Answer 5 6 7 273 274 275 277 195 187 212 196 12 Q3.3 Mechanics Conditions: Use your own paper to explain how the Normal condition for this test has been met. Then…
A researcher want to know if there is a relationship between sleep and anxiety. The researcher randomly selected n = 5 participants and asked each to rate their sleep quality and anxiety level on a scale of 1 to 5. The data are below: Sleep X Anxiety Y 2 2 3 3 2 2 4 2 5 2 Calculate the Sum of Squares for X (SSX)
Given below are seven observations collected in a regression study on two variables, x (independent variable) and y (dependent variable). x y 2 12 3 9 6 8 7 7 8 6 7 5 9 2 The least-squares estimate of b0 equals [2 decimal points] ________ The least squares estimate of b1 equals [2 decimal points; also don't forget the sign] __________ Develop the least squares estimated regression equation _______ At 95% confidence, perform a t test and determine whether the slope is significantly different from zero. [3 decimals] t = _____________ Based on this information, we Reject H0 Do not reject H0 The coefficient of determination is [keep 3 decimal points] _________
The final question in the examples is (e) Find the value of the coefficient of determination r2. What percentage of the variation in y can be explained by the corresponding variation in x and the least-squares line? What percentage is unexplained? (Round your answer for r2 to three decimal places. Round your answers for the percentages to one decimal place.) r2 = explained % unexplained % (f) Predict the percentage of all fatal accidents due to speeding for 25-year-olds. (Round your answer to two decimal places.)
4.2.21 Question Help v An engineer wants to determine how the weight of a car, x, affects gas mileage, y. The following data represent the weights of various cars and their miles per gallon. C 2740 3045 3390 3870 4190 25.5 22.5 24.2 19.2 14.4 Car A B D E Weight (pounds), x Miles per Gallon, y (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. Write the equation for the least-squares regression line. (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) Enter your answer in the edit fields and then click Check Answer. 6 parts remaining Clean All Check Answer
longley is a built-in R data.frame which is sometimes used in economics to study regression. We are interested in some descriptive statistics related to the Employed column of the longley data.frame. We can access this data directly by using the assignment x <- longley$Employed (In R, use ? Longley for info on this data) The values of x if assigned as above are: [1] 60.323, 61.122, 60.171, 61.187, 63.221, 63.639, 64.989, 63.761 [9] 66.019, 67.857, 68.169, 66.513, 68.655, 69.564, 69.331, 70.551 You should get these values from the R assignment above rather than manually entering the data. b. Calculate the 3rd quartile of x using the R summary function c. Calculate the sample range & median of x h. What proportion of the x values are less than 1 sample standard deviation from the sample mean? k. How many of the elements of x are smaller than the sample mean of x?
Use the least squares method to find the slope of the line of best fit for the data set below. Round to three decimal places. x 67 28 26 47 86 32 15 70 y 46 34 33 38 46 37 35 44 −4.430 4.430 −0.202 0.202