From the table below, solve the following.b. Find mean of x [Select]Xa. Find the Pearson correlation coefficient [Select]c. Find the mean of y [Select]3781065d. Find the regression equation [Select][Select]y = 1.05 +0.92xy = 3.74 +0.68xy = 4.08 +0.81xy = 5.13 +0.73xy41112753

Pearson Correlation Coefficient is denoted as 'r' and it is computed as below…

Answered: From the table below, solve the…

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter1: Functions

Section1.2: The Least Square Line

Problem 5E

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For the following table of data. x 1 2 3 4 5 6 7 8 9 10 y 0 0.5 1 2 2.5 3 3 4 4.5 5 a. draw a scatterplot. b. calculate the correlation coefficient. c. calculate the least squares line and graph it on the scatterplot. d. predict the y value when x is 11.
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.
A weather forecaster examines the weather patterns in a random sample of cities in order to better understand how the number of days of rain a city gets per year is related to the number of hours of sunshine that city gets per year.The regression equation to predict hours of sunshine based on days of rain is as follows: Predicted hours of sunshine = 2847 – 6.88(days of rain). From this regression equation, we know that r, or the correlation between days of rain and hours of sunshine, must be weak strong positive negative non-linear.
10) A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.767 b=31.009 r2=0.609961 r=-0.781 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)
AL0. A survey was conducted to study the retationship between expenditure on accommodation (X) and expenditure on food and entertainment (Y) and the following results were obtained : Mean S.D. Rs. 1730 630.5 Expenditure on Accommodation Expenditure on food and entertainment Coefficient of Correlation Rs. 488 236.2 .00t to tuo 2569 0.67 Write down the regression equation and estimate the expenditure on food and entertainment, if the expenditure on accommodation is Rs. 1200.
The following table shows the approximate number of wireless telephone subscriptions in recent years. 2005 2006 2007 2008 2009 2010 Subscriptions(Millions) 194 220 243 263 277 293 Year 1. Find the linear correlation coefficient of the data. 2. Fit the data into a linear equation and compute for the slope and the simple regression equation.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.711 b=30.651 r²-0.670761 r=-0.819 Use this to predict the number of situps a person who watches 10.5 hours of TV can do (to one decimal place)
A regression was run to determine if there is a relationship between hours of TV watched per day (xx) and number of situps a person can do (yy).The results of the regression were:y=ax+b a=-0.629 b=39.045 r2=0.806404 r=-0.898 Use this to predict the number of situps a person who watches 6.5 hour(s) of TV can do, and please round your answer to a whole number.

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From the table below, solve the following. b. Find mean of x [Select] X c. Find the mean of y [Select] 3 7 8 a. Find the Pearson correlation coefficient [Select] 10 6 5 d. Find the regression equation [Select] [Select] y = 1.05 +0.92x y = 3.74 +0.68x y = 4.08 +0.81x y = 5.13 +0.73x y 4 11 12 7 5 3