4. We would like to determine how a man's weight can be used to predict his systolic bloodpressure. We measure the weights (in pounds) and the systolic blood pressures of asample of 17 men. The data are plotted on a scatterplot and it can be seen that a linearrelationship is a reasonable assumption. The least squares regression line is calculatedto be ŷ = 125.561 + 0.145x. The ANOVA table (with some values missing) is shownbelow:|Source of Variation | df | Sum of Squares Mean SquareFRegressionErrorTotal26.46617.82(a) We would like to test whether a linear relationship exists between a man's weightand his systolic blood pressure. We will conduct an analysis of variance to test Ho:B1 = 0 vs. Ha: B1 + 0 at the 5% level of significance. Enter all missing values inthe ANOVA table. Calculate the value of the test statistic, find the P-value of thetest and provide a properly worded conclusion.(b) What is the value of the correlation between weight and systolic blood pressure forthis sample of 17 men?(c) What is the estimate of the parameter o in the simple linear regression model?

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Answered: 4. We would like to determine how a…

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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The following table shows the starting salary and profile of a sample of 10 2 p employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance.What is the computed R square of the resulting multiple linear regression and its interpretation? * Civil Years of Starting salary GPA service experience ratings 79.5 15000 80.1 15000 81.2 78.0 15500 81.3 79.0 16000 82.4 80.0 16200 83.4 85.0 17500 87.9 89.9 89.1 18000 90.3 16,300 84.2 17000 87.0 17900 88.1 84.1 89.0 89.2 R squared = 0.8053; This means that 80.53% of the total variation in the starting salary can be explained by its linear relationship with GPA, years of experience and civil service ratings. R squared = 0.9651; This means that 96.51% of the total variation in the starting salary can be explained by its linear relationship with GPA, years of…
You plan to fit a regression model that will be used to predict first-year college GPA (FYGPA) from high-school GPA (HSGPA), ACT score (ACT), first-generation status (Yes or No), socioeconomic class (lower class, lower to middle class, middle to upper class, and upper class), and school type (public or private). What is the total number of estimated regression coefficients? If the sample size is n = 250 students, what are the degrees of freedom for the following sources of variation: Regression Error Total
The local utility company surveys 12 randomly selected customers. For each survey participant, the company collects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regression analysis appears below: Bill 13.45 + 4.39*Size Coefficients Estimate Std. Error (Intercept) 13.45 Size 4.39 0.54 0.2 We are 90% confident that the mean annual electric bill increases by between dollars and dollars for every additional square foot in home size. Round your answers to three decimal places and enter in increasing order.
Consider the following data,Study Hours (Y): 2, 4 ,6 ,8 ,10 ,13, 7Sleeping Hours (X): 10, 9, 8, 7,6 ,7, 5 i) Calculate and analyze the fitted regression line between the number of study hours and the number of sleeping hours of different intakes of CSE students.ii) Find the coefficient of determination and interpret your data.iii) Predict study hour when he/she sleeps 11 hours.
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.
There is a dataset of size n = 51 and is for the 50 states and the District of Columbia in the United States. The dependent variable is year 2002 birth rate per 1000 females 18 to 19 years old and independent variable is the violent crime rate (per 1000 population). A simple linear regression model is run with the results given below. What is the Pearson correlation coefficient between x and y variables? The R squared of the model? What kind of relationship there is?
Consider a certain data set on shoulder girth and height of a group of individuals. The mean shoulder girth is 107.20 cm with a standard deviation of 10.37 cm. The mean height is 171.14 cm with a standard deviation of 9.41 cm. The correlation between height and shoulder girth is 0.67. Calculate R2 of the regression line for predicting height from shoulder girth, and interpret it in the context of the application.
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