Q.2 Since outbreak of COVID-19 pandemic insurance companies started spending on Advertisement of Corona Kavach policy to enhance the revenue due to rising number of cases in India. The data below explains the advertising expenditure and revenue of seven companies. a) Is there any association between advertising expenditure and revenue earned ? b) Use the data to develop a regression line to predict the amount of advertising by revenues? c) What is your interpretation from the calculations in part a and b respectively? |Name of company Birla Sunlife Bharti Axa Religare Health care Маx Bupa Reliance health care HDFC ergo United Insurance Advertising (in Rs.) 1073 Revenues (in Rs.) 408.2 4898 3345 3296 2822 79.7. 123.0 104.6 107.8 2577 829 118.3 114.6

Q.2 Since outbreak of COVID-19 pandemic insurance companies started spending on Advertisement of Corona Kavach policy to enhance the revenue due to rising number of cases in India. The data below explains the advertising expenditure and revenue of seven companies. a) Is there any association between advertising expenditure and revenue earned ? b) Use the data to develop a regression line to predict the amount of advertising by revenues? c) What is your interpretation from the calculations in part a and b respectively? |Name of company Birla Sunlife Bharti Axa Religare Health care Маx Bupa Reliance health care HDFC ergo United Insurance Advertising (in Rs.) 1073 Revenues (in Rs.) 408.2 4898 3345 3296 2822 79.7. 123.0 104.6 107.8 2577 829 118.3 114.6

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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12 young batsmen practiced batting at the nets for varying periods of time, and their dot ball percentage was calculated at the end of the month: a) Find the relationship between dot ball percentage and practice time per month using a scatter diagram and interpret. b) Find correlation coefficient and comment. c) Fit a least square regression equation (line) of dot ball percentage on practice time per month and comment. d) What will be the dot ball percentage when practice time per month is 32hr? e) Comment on the regression equation and explore how well it fits.
a) Compute the value of the sample correlation coefficient, r. (Round your answer to four decimal places.) r = b) Calculate the test statistic and determine the P-value. (Round your test statistic to one decimal place and your P-value to three decimal places.) t = P- value = c) If a regression analysis were to be carried out to predict endurance from lactate level, what proportion of observed variation in endurance could be attributed to the approximate linear relationship? Answer the question without doing any regression calculations. (Round your answer to four decimal places.) ____________ d) If a regression analysis were to be carried out to predict lactate level from endurance, what proportion of observed variation in endurance could be attributed to the approximate linear relationship? Answer the question without doing any regression calculations. (Round your answer to four decimal places.) _____________
a) Find a correlation coefficient r, round it to 4 decimal places. Do not round partial results. b) Interpret previous result clearly in terms of direction and strength of association. c) Find the equation of regression line. Equation of regression line: …………………………………………
Researchers at UC San Francisco discovered that highplasma levels of vitamins B, C, D, and E are associated with better cognitive performance. “Each standard devia-tion higher plasma level for these vitamins predicted a global cognitive score 0.28 standard deviations better,”the researchers reported. Which value are the researchersinterpreting in this statement?A) the correlation coefficient between plasma level andcognitive scoreB) the y-intercept of the regression model predictingcognitive score from plasma levelC) the slope of the regression model predicting cognitivescore from plasma levelD) the standard deviation of the regression model’sresidualsE) R2for the regression model
Amount of brake fluid can affect the breaking time of a vehicle. Engineer Nsiah conducts an experiment to determine if this statement is true. The breaking time data for the trials for different levels of brake fluid as presented in table below. i) Obtain the linear regression model for the given data ii) Compute the correlation coefficient, r iii) Find the Breaking time given the amount of oil is 45cm2 Amount of 20 10 30 40 60 50 oil(cm3) Braking time(s) 9. (iv) The coefficient of determination, r for the given data 3.
A small independent organic food store offers a variety of specialty coffees. To determine whether price has an impact on sales, the managers kept track of how many kilograms of each variety of coffee were sold last month. The data and summary statistics (including the averages, standard deviations and the correlation coefficient) are shown in the table. If the "price per kilogram" is the predictor variable and "kilograms sold" is the response variable, then use a regression line to predict the the amount of kilograms sold for a "price per kilogram" equal to $15. Round your numbers to 3 decimal places.
From the article “Association of cognitive functioning with retinal nerve fiber layer thickness” by van Koolwijk et al., in Investigative Ophthalmology & Visual Science, October 2009, Vol. 50, No. 10, below is table 2 showing the results of fitting several multiple linear regression models for different response variables. Write down the fitted model corresponding to the last row of the table. You can leave the intercept as hatB0. Interpret the coefficient values corresponding to the RNFL Thickness and Male variables.
The following data represent the speed at which a ball was hit (in miles per hour) and the distance it traveled (in feet) for a random sample of home runs in a Major League baseball game in 2018. Complete parts (a) through (f). Click here to view the data. Click here to view the critical values of the correlation coefficient. (a) Find the least-squares regression line treating speed at which the ball was hit as the explanatory variable and distance the ball traveled as the response variable. y = 5x+ (5 (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Begin by interpreting the slope. A. The slope of this least-squares regression line says that the distance the ball travels increases by the slope with every 1 mile per hour increase in the speed that the ball was hit. O B. The slope of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit.…
4. A study is conducted in patients with HIV. The primary outcome is CD4 cell count, which is a measure of the stage of the disease. Lower CD4 counts are associated with more advanced disease. The investigators are interested in the association between vitamin and mineral supplements and CD4 count. A multiple regression analysis is performed relating CD4 count to use of supplements (coded as 1 = yes, 0 = no) and to duration of HIV, in years (i.e., the number of years between the diagnosis of HIV and the study date). For the analysis, = CD4 count. Ÿ = 501.41 + 12.67 Supplements – 30.23 Duration of HIV What is the expected CD4 count for a patient not taking supplements who has had HIV for 5 years? A) 350.26 B) 501.41 C) 30.23 D) 5|
3. Wine Participant magazine has collected average price per bottle for the prestigious Chateau Le Thundebird bordeaux for different vintages (years). The data appears in the table below. year of bottling price a) draw the scatter diagram showing how wine price varies by vintage year b) use the most appropriate regression equation to determine the relationship between year of bottling (age) and price. c) what is the explanatory power (RSQ) of that equation d) determine the predicted price of a bottle of this wine for the 2017 vintage. 2009 36 2010 40 2011 51 2012 60 2013 68 2014 72 2015 70 2016 65 2018 51 2019 44 2020 39