8:35KA pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, y. Sherandomly selects 11 children from her practice, measures their heights and head circumferences, and obtains theaccompanying data. Complete parts (a) through (g) below.Click the icon to view the children's data.(a) Find the least-squares regression line treating height as the explanatory variable and head circumference as theresponse variable.Data Tabley = 0.194 x + (12.5)(Round the slope to three decimal places and round the constant to one decimal place as needed.)(b) Interpret the slope and y-intercept, if appropriate.First interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete yourchoice.Height (inches), x Head Circumference (inches), y D27.7517.824.7517.325.7517.426.2517.72517.128.2517.926.7517.52717.717.517.717.8262627.5(Round toPrinttwo decimal places as needed.)|||LTE 2.1 4G=DoneOXaverage.(c) Use the regression equation to predict the head circumference of a child who is 25 inches tall.y = 0.194 in.., on average.41%fill in the answer box toaverage.average.

(a) Obtain the regression equation. The regression equation is obtained below as follows: From the…

Answered: 8:35 K A pediatrician wants to…

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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A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, v. She randomly selects 11 children from her practice, measures their heights and head circumferences, and obtains the accompanying data. Complete parts (a) through (g) below. A Click the icon to view the children's data. (a) Find the least-squares regression line treating height as the explanatory variable and head circumference as the response variable. y=x+ (D (Round the slope to three decimal places and round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. First interpret the slope. Select the correct choice below and, if necessary. in the answer box to complete your choice. O A. For every inch increase in height, the head circumference increases by (Round to three decimal places as needed.) in., on average. O B. For a height of 0 inches, the head circumference is predicted to be (Round to three decimal places as…
KidsFeet Regression Line y-Intercept The KidsFeet dataframe contains data collected on 39 fourth grade students in Ann Arbor, MI, in October 1997. Two of the measurements taken on the children were the length in centimeters, (length), and width in centimeters, (width), of their longest foot. This data could be used to answer the following Research Question: How is the width of a fourth-grade student's foot related to the length? Which of the following is the y-intercept of the regression line? ## ## Simple Linear Regression## ## Correlation coefficient r = 0.6411 ## ## Equation of Regression Line:## ## length = 9.817 + 1.658 * width ## ## Residual Standard Error: s = 1.025 ## R^2 (unadjusted): R^2 = 0.411 ( ) 1.0248 ( ) 1.6576 ( ) 22.8153 ( ) 0.411 ( ) 9.8172 ( )0.6411
Is It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
A least squares regression model performed to predict the selling price of houses found the following equation: Pricê = 169.328+35.3Area + 0.718Lotsize - 6543Age where Price is in dollars, Area is in square feet, Lotsize is in square feet, and Age is in years. The R2 is 0.92. One of the following interpretations is correct. Which is it? Explain why. a.) Each year a house Ages, it is worth $6543 less. b.) Every extra square foot of Area is associated with an additional $35.30 in average price, for houses with a given Lotsize and Age. c.) Every dollar in price means Lotsize increases 0.718 square feet. d.) This model fits 92% of the data points exactly.
6:24 59% K A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, y. She randomly selects 11 children from her practice, measures their heights and head circumferences, and obtains the accompanying data. Complete parts (a) through (g) below. (a) Find the least-squares regression line treating height as the explanatory variable and head circumference as the response variable. y = 0.194 x + (12.5) (Round the slope to three decimal places and round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. First interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice. OA. For every inch increase in head circumference, the height increases by in., on average. (Round to three decimal places as needed.) B. For a height of 0 inches, the head circumference is predicted to be (Round to three decimal places as needed.) OC. For a head…
In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.
Statistical software is generally used to test the significance of a regression model. The table below shows software output for a least squares regression line. Fill in the blanks with the correct answers, using the table above. Assume a significance level of 0.05. The variable "unemp" is the_______variable. The value of the slope of the regression line is_______. The slope of the regression line is not significantly different from______because the p-value is______ which is larger than 0.05.
A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, y. She randomly selects 11 children from her practice, measures their heights and head circumferences, and obtains the accompanying data. Complete parts (a) through (g) below. Click the icon to view the children's data. Data table (a) Find the least-squares regression line treating height y =x+ (O (Round the slope to three decimal places and round the d Height (inches), x Head Circumference (inches), y O. 28 17.6 24.5 17.1 25.75 17.1 25.75 17.5 24.25 17.0 27.75 17.7 26.5 17.3 27.25 17.6 26.5 17.3 26.5 17.5 27.75 17.6 Print Done Help me solve this View an example Get more help - Media - Clear all Check answer
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
Q1: [Regression and Correlation] The index of biotic integrity (IBI) is a measure of water quality in streams. As a data analyst you must monitor, track, and predict changes in water quality. You want to create a simple linear regression model that will allow you to predict changes in IBI in forested area. The following table conveys sample data from a coastal forest region and gives the data for IBI and forested area in square kilometers. Let forest area be the predictor variable (x) and IBI be the response variable (y). IBI X 47 72 21 72234 19 58 49 Forest Area Y 38 59 27 24 63 49 45 Required a. Q2: [Naïve Bayes] a. Write the pseudo-code of the following Naïve Bayes algorithm b. Consider the given dataset that classifies animals into two distinct classes. The classes are labeled as 'cat' and 'dog'. Use Naive Bayes classifier to figure out the class (cat/dog) of an instance if it has the following values of attributed Plot Scatter graph b. Determine the regression equation c. Compute…
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…

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