Answered: The following table shows students'… | bartleby

MATLAB: An Introduction with Applications

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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The following table shows students' test scores on the first two tests in an introductory calculus class.
Calculus Test Scores
First
68 79
69
67 54 84 45
86 70 55
75 70
test, x
Second
66
70 55 45 67 50 74 65 45
65 58
test, y
Copy Data
Step 1 of 2: Find an equation of the least-squares regression line. Round your answer to three decimal places, if necessary.
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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.
Using least square method, find the regression line model. Interprete your result. Calculate the coefficient of correlation and explain. Calculate the coefficient of determination and explain your answer.
find the equation of the regression line. Round to 3 decimal places.
Use the least squares regression model to estimate the maximum sustained wind speed in a hurricane when the pressure reading is 950 mb. Interpret the slope of the least squares regression model in the context of wind speed and atmospheric pressure. Determine the coefficient of determination and interpret its meaning in the context of wind speed and atmospheric pressure.
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help please answer in text form with proper workings and explanation for each and every part and steps with concept and introduction no AI no copy paste remember answer must be in proper format with all working
Fill in the blanks. a. Multicollinearity is considered to be severe if the VIF for one or more predictor variables is ______. or greater. b. If the coefficient of multiple determination for the regression of the predictor variable x11 on all the other predictor variables in a regression equation is 0.6, then the VIF for x1 is ______. c. The effect of multicollinearity in a polynomial regression analysis can be reduced by ______. the predictor variable.
Data in the table comes from the NASA Global Climate Change website. It gives the average globaltemperature, in Celsius, for the given year. Use both rows for your calculations. For this problem, youwill see how well year correlates with the average temperature. Let ? be the number of years since1984 and ? be the average global temperature in degrees Celsius.Year 1984 1986 1988 1990 1992 1994 1996 1998 2000Temp 0.16 0.18 0.38 0.45 0.22 0.31 0.33 0.61 0.40Year 2002 2004 2006 2008 2010 2012 2014 2016 2018Temp 0.63 0.54 0.64 0.54 0.72 0.64 0.75 1.02 0.85a. Make a scatterplot of the data. Being sure to clearly label each axis. Keep in mind howeach variable is defined. Based on this scatterplot, is a linear model reasonable for this data set?Briefly explain.b. Determine the correlation coefficient for this data. Interpret this value in context.c. Determine the coefficient of determination for this data. Interpret this value incontext.d.State the regression line for the data.e. What does the…
determine whether the statement is true or false. If the statement is false, rewrite it as a true statement. The y-intercept b0 of a least-squares regression line has a useful interpretation only if the x-values are either all positive or all negative.
Interpreting technology: A biologist computed a least-squares regression line for predicting the brain weight in grams of a bird from its body weight in grams. The results are presented in the following Excel output. Coefficients Intercept 3.68952298 Body Weight 0.07800925 Part: 0 / 2 Part 1 of 2 Write the equation of the least-squares regression line. Use the full accuracy shown in the Excel output (do not round your answers). Regression line equation: y = X

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