1Consider an estimated linear regression model with a response Y and four predictors X1, X2, X3,and X4, based on a random sample of 25 sales agents of medical supplies in 3 regions of theprovince. Y is annual sales in 1000$, X1 in 100$, X2 in 100$, X3 in %, and X4 in 1000$. Theregression results are given below.Coefficients Standard ErrorIntercept -593.53745 259.195852.513140.314281.905950.742392.651014.63566-0.120730.37181X1X2X3X4Type your numerical answer here.Numbers onlyFind the lower limit of the change in the mean response indollars, for a unit change in X1 with a 95% confidencelevel, assuming that the other predictors remain unchangedover the period.

Slope is the measure of regression. Slope is the changing variable of the data set. The standard…

Answered: 1 Consider an estimated linear…

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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In a sample of cars reviewed by Motor Trend magazine, the mean horsepower (hp) was 150 hp with a standard deviation of 36 hp. The mean weight (lbs) was 2500 lbs with a standard deviation of 720 lbs. Assume the relationship between weight and horsepower is linear and has a correlation of r = +0.55. What is the slope of the linear regression model predicting weight (y-variable) from horsepower (x-variable)? 9 13 15 11
The flow rate in a device used for air quality measurement depends on the pressure drop x (inches of water) across the device's filter. Suppose that for x values between 5 and 20, these two variables are related according to the simple linear regression model with true regression line y = -0.11 + 0.097x. (a.1) What is the true average flow rate for a pressure drop of 10 in.?(a.2) A drop of 15 in.?(b) What is the true average change in flow rate associated with a 1 inch increase in pressure drop?(c) What is the average change in flow rate when pressure drop decreases by 5 in.?
The least-square regression line for the given data is y = 0.449x - 30.27. Determine the residual of a data point for which x = 90 and y=10, rounding to three decimal places. Temperature, x Number of absences, y OA. -0.14 OB. 20.14 C. 115.78 OD. 10.14 72 3 85 7 91 10 90 10 88 8 98 15 75 100 4 15 80- 5
The least-squares regression equation is y=784.6x+12,431 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7962. In a particular region, 26.5 percent of adults 25 years and older have at least a bachelor's degree. The median income in this region is $29,889. Is this income higher or lower than what you would expect? Why?
5. Write the equation of the least-squares regression line defining any variables used. Round coefficients to 4 decimal places .
The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Predict the median income of a region in which 30% of adults 25 years and older have at least a bachelor's degree.
A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x). The results of the regression were: ý=a+bx a=-1.692 b=0.117 (a) Write the equation of the Least Squares Regression line of the form (b) Which is a possible value for the correlation coefficient, r? O -0.649 O 1.32 O 0.649 O-1.32 (c) If a country increases its life expectancy, the happiness index will O increase O decrease (d) If the life expectancy is increased by 2.5 years in a certain country, how much will the happiness index change? Round to two decimal places. (e) Use the regression line to predict the happiness index of a country with a life expectancy of 75 years. Round to two decimal places.
A group of Maternal and Child Health public health practitioners are interested in the relationship between depression and a number of health outcomes. Suppose the research team gathers information on a group of participants, and constructs a multiple linear regression model looking at the relationship between depression and household income dichotomized as above and below the federal poverty line controlling for a number of potential confounders. The following is a computerized output displaying the results of their analysis. Parameter Estimate Standard Error t Value Pr > |t| Intercept 0.2617346843 0.09209917 2.84 0.0046 Income (1/0) -.1962038300 0.04574793 -4.29 <.0001 Race (W or AA) -.0320329506 0.03900447 -0.82 0.4118 bmicontinuous 0.0051185980 0.00216986 2.36 0.0186 Alcohol (Y/N) -.0088735044 0.03090631 -0.29 0.7741 A) What are the independent and dependent variables? B) Which potential…
A linear regression analysis reveals a strong, negative linear relationship between x and y. Which of the following could possibly be the results from this analysis? (A) ŷ 13.1 27.4x, r = 0.85 (B) == 27.4+ 13.1x, r = -0.95 (D) == (C) ŷ 13.1+ 27.4x, r = 0.95 542 385x, r = -0.15 (E) 0.85 0.25x, r = -0.85

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