We wish to estimate the effect of weight on serum cholesterol (S. C.) level in healthy females aged between 20 and 25. To estimate the regression equation of S.C. level on weight, a random sample of 10 females in this age class was taken, with the following results: a. Compute (beta) B^ 1 the estimate of the slope coefficient b. Compute (beta) B^ 0 the estimate of the intercept

We wish to estimate the effect of weight on serum cholesterol (S. C.) level in healthy females aged between 20 and 25. To estimate the regression equation of S.C. level on weight, a random sample of 10 females in this age class was taken, with the following results: a. Compute (beta) B^ 1 the estimate of the slope coefficient b. Compute (beta) B^ 0 the estimate of the intercept

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semi-fragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P14_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semi-fragile cargo.
A study was done to determine the relationship between the alcohol content of beer, and the amount of calories in a 12 oz serving size. The sample data is provided in the data below. a) From this sample data, can you conclude (at the alpha=0.05 level) that there is in fact a linear correlation between the two variables? Use the statistics (correlation coefficient, value) to describe the correlation (if any). B) Using the sample data, find a regression equation to estimate the number of calories in a beer. C) Dogfish Head Brewery in Milton, Delaware has an I.P.A. beer called "120 minute IPA" which has an alcohol content of 18%. Using your regression equation, estimate the number of calories in a 12 oz serving of 120 Minute. Brand/Beer Alcohol Content (%) Calories (per 12 oz) Big Sky Scape Goat Pale Ale 4.70 163 Sierra Nevada Harvest Ale 6.70 215 Steel Reserve 8.10 222 Coors Light 4.15 104 Genesee Cream Ale 5.10 162 Sierra Nevada Summeefest Beer 5.00 158 Michelob Beer…
The Life Insurance Company is attempting to model the weight, Y (in pounds), of a random sample of n=92 randomly selected adults using height, X1 (in inches), and gender, I2 (0 = Male 1=Female). In addition, as part of the research objective, we also wish to determine if the influence of height (X1) on weight (Y) depends on gender (I2) and vice versa. Write out the general regression equation for this model, based on the research objectives and information provided. Using the general equation from part A, write out the specific regression equation for a female. Using the general equation from part A, write out the specific regression equation for a male. If it was found that the influence of height on weight did NOT depend on gender, how would this change the equation given in part A of this problem? Rewrite the general equation from part A here.
Calculate the equation of the regression line and calculate the correlation coefficient
For 40 eruptions of the Old Faithful geyser in Yellowstone National Park, duration times (sec) were recorded along with the heights (ft) of the eruptions. The linear correlation coefficient is r = 0.0915 and the regression equation is = 119 + 0.0331x, where x represents duration time. The mean of the 40 duration times is 245.0 sec and the mean of the 40 heights is 127.2 ft. What is the best predicted height of an eruption with a duration time of 120 min?
The following table gives the ages of female students in school and the corresponding Body Mass index (BMI) of 8 randomly selected students.(α=0.05) Age24223032211925 BMI27283029282726 Determine whether ages and BMI are significantly related.Determine the coefficient of linear determination and Intepret.Set up a linear regression equation to predict body mass index from age of students.Predict the body mass index of student who has the age of 26 years.
If there is a positive correlation between X and Y, then the regression equation Y = bX + a will have _____.
The fish in my pond have mean lenth 14 inches with a standard deviation of 2 inches and mean weight 4 pounds with a standard deviation of .8 pounds. The correlation coefficient of length and weight is .4. If the length of a particular randomly selected fish is reported to be 15 inches, then what should we predict for the weight of that fish using simple linear regression?
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 91 76 76 5 Left Arm 175 170 146 147 146 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = +x (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
For the population of Jellystone bears, the mean weight is 400 pounds and the mean blood pressure is 150, and the SLR regression slope for predicting blood pressure using weight has been found to be 0.05. What is the SLR regression intercept for predicting blood pressure using weight?
The owner of Original Italian Pizza restaurant chain wants to understand which variable most strongly influences the sales of his specialty deep-dish pizza. He has gathered data on the monthly sales of deep-dish pizzas at his restaurants and observations on other potentially relevant variables for each of several outlets in central Indiana. These data are provided in the file P10_04.xlsx. Estimate a simple linear regression equation between the quantity sold (Y) and each of the following candidates for the best explanatory variable: average price of deep-dish pizzas (X1), monthly advertising expenditures (X2), and disposable income per household in the areas surrounding the outlets (X3). Round your answers for intercept coefficients to the nearest whole number and slope coefficients to two decimal places, if necessary. If your answer is negative number, enter "minus" sign.