At a fish market, different measurements of a certain type of fish were recorded for a sample of 35 fish. An analysist used the data todevelop a linear model of the relationship between the weight (in grams) of the fish, and its length, height and width (all in cm). Theresults of the multiple linear regression to predict the weight of the fish are as follows:Regression StatisticsMultiple RR Square0.9710.943Adjusted R Square0.937Standard Error52.428Observations35dfSignificance FANOVAMSFRegression31402868467622.7170.12240.0000Residual31852112748.742Total341488079CoefficientsStandard Errort StatP-value-13.2InterceptLength-1009.076.50.000012.87.51.70.0971Height63.115.93.90.0004Width51.634.01.50.13911. What percentage of variation in weight is not explained by the regression model? Give youranswer correct to 1 decimal place.2. Use the ANOVA p-value to test the appropriate hypothesis at the 5% level of significance. For each one of the following questions, select thecorrect answer from the list below.v 15.1 State the decision about HoA. Reject the null hypothesis15.2 State the conclusion about H1B. Fail to reject the null hypothesisc. There is sufficient evidence for the alternative hypothesisD. There is insufficient evidence for the alternative hypothesis

Answered: 1. What percentage of variation in…

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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If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
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?
A business statistics professor would like to develop a regression model to predict the final exam scores for students based on their current GPAs, the number of hours they studied for the exam, the number of times they were absent during the semester, and their genders. The data for these variables are given in the accompanying table at the bottom of this page. a) Using Excel, construct a regression model using all of the independent variables. Create the dummy variable Gen, which equals 1 for a male and 0 for a female student ( this assignment is arbitrary) complete the regression equation for the model below, where y= Score, x1= GPA, x2= Hours, x3= Absenses, and x4= Gen. y= (__) + (__)x1 + (__)x2 + (__)x3 + (__)x4 b) Test the significance of the overall regression model using a= 0.10. c) interpret the meaning of the regression coefficient for the dummy variable. d) using the p-values, identify which independent variables are significant with a= 0.10. e) construct a regression…
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?
Find the equation of the regression line for the data based on time spend forstudying and current CGPA
create graph of the two-variable data with a regression line, r, r2, and separate residual plot
The data point height=175 cm, height =80 cm was misrecorded as height=145 cm, height =80 cm. If we use the simple linear regression and do not correct the mistake, what is the impact of this point on the model?
Given a generic data set (x,y) with a linear regression. How do you determine if the y(dependent) will be less/greater than a certain value at a decided value of x?
A researcher was investigating variables that might be associated with the academic performance of high school students. The data included the average Math SAS score of all high school seniors in the city that took the exam (labeled as the variable SAT-M), the average number of dollars per pupil spent on education by the city (labeled as the variable $Per Pupil), and the percentage of high school seniors in the city that took the exam (labeled as the variable %Taking). The researcher ran the following multiple linear regression model as SAT-M=Beta0 + Beta1($Per Pupil) + Beta2(%Taking). This model was fit to the data using the method of least-squares, results shown inside of table within photo. If we want to test using ANOVA F-test with hypotheses Ho: Beta1=Beta2=0 versus H1: at least one of the Beta is not 0, what would the value of our F-statistic mean?
In the method of regression, data sets are summarized in a useful form. The “independent” pieces of data are called inputs or regressors, whereas a quantity that is a function of the inputs, will be called a response. In an experiment, the effect of increasing storage temperature is related to the number of spoiled loaves of bread after 7 days. In this case, which variable will be the regressor?
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.
Given a generic data set (x,y) with a linear regression. How do you determine if the y(dependent) will be less than a certain value?

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