1) Researchers are curious whether the age a couple gets married affects the number of childrenthey have. They collect the following set of data:● Father's age at marriage (x₁)● Mother's age at marriage (x₂)●The number of children they have had after 10 years of marriage (y)They look at the following models, model 1 number of children as a function of Father's age andMother's age, model 2 number of children as a function of Father's age, and model 3 number ofchildren as a function of Mother's age.Model 1SourceDf SSMS45.73Model3 137.18Residual1261.095.09Total15198.27Model 2SourceDf SSMSModel2123.2561.625Residual1375.025.77Total15198.27Model 3SourceDf SSMSModel2112.7856.39Residual1385.496.576Total15198.27a.What write the three models we consider with this data?b. Given the ANOVA tables for each model, are they valid modelsC. Given the ANOVA tables and using the extra sum of square

From the given information, x1 be the father's age at marriage , x2 be the mother's age at marriage…

Answered: 1) Researchers are curious whether the…

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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Dr. Lillian Fok, a New Orleans psychologist, specializes in treating patients who are agoraphobic (i.e., afraid to leave their homes). The following table indicates how many patients Dr. Fok has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year. Year Number of Patients Robbery Rate per 1,000 Population The simple linear regression equation that shows the best relationship between the number of patients and the robbery rate is (round your responses to three decimal places) where y Number of Patients and x = Robbery Rate. = 1 2 3 4 6 7 36 33 40 41 55 60 58.3 61.1 73.4 75.7 81.1 89.0 101.1 5 40 8 54 94.8 9 58 103.3 10 61 116.2
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.8 8.2 6.9 3.9 2.3 2.4 2.6 0.6 y 14.2 11.2 10.2 7.5 5.9 6.4 6 4.3 x = thousands of automatic weaponsy = murders per 100,000 residents This data can be modeled by the equation y=0.88x+3.95. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 7.2 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 1.6 thousand automatic weapons? Answer = Round to 3 decimal places.
A chemistry experiment is performed measuring the solubility of potassium chloride (KCl) in water at different temperatures. The goal was to determine if there is a linear relationship between the temperature of the water and how much KCl can dissolve, measured as grams per 100 milliliter (g/100mL). After the experiments were performed, the following data was collected with temperature being the independent x-variable and solubility being the dependent y-variable: Temperature (°C) x Solubility (g/100mL) y 10 31 20 33 30 37 40 41 50 42 Based on the data given for temperature and solubility of KCl and without doing any math yet, which of the following do you predict would best describe the relationship between these variables? A positive linear relationship (r close to 1) A positive linear relationship (r close to -1) A negative linear relationship (r close to 1) A negative linear relationship (r close to -1)…
Breakfast cereals We saw in Chapter 7 that the calorie con-tent of a breakfast cereal is linearly associated with its sugar content. Is that the whole story? Here’s the output of aregression model that regresses Calories for each serving onits Protein(g), Fat(g), Fiber(g), Carbohydrate(g), andSugars(g) content.Dependent variable is CaloriesR-squared = 84.5, R-squared (adjusted) = 83.4,s = 7.947 with 77 - 6 = 71 degrees of freedomSource Sum ofSquares dfMeanSquare F-RatioRegression 24367.5 5 4873.50 77.2Residual 4484.45 71 63.1613Variable Coefficient SE(Coeff) t-Ratio P-ValueIntercept 20.2454 5.984 3.38 0.0012Protein 5.69540 1.072 5.32 60.0001Fat 8.35958 1.033 8.09 60.0001Fiber -1.02018 0.4835 -2.11 0.0384Carbo 2.93570 0.2601 11.3 60.0001Sugars 3.31849 0.2501 13.3 60.0001Assuming that the conditions for multiple regressionare met,a) What is the regression equation?b) Do you think this model would do a reasonably goodjob at predicting calories? Explain.c) To check the conditions, what…
The number of pounds of steam used per month by a chemical plant is thought to be related to the average ambient temperature (in F) for that month. The past year's usage and temperatures are in the following table: Usage/ Month Temp. 1000 Month 21 185.79 July. 24 214.47 Aug. 32 288.03 Sept. 47 424.84 Oct. 50 454.58 Nov. 59 539.03 Dec. Jan. Feb. Mar. Apr. May June Temp. 68 74 62 50 41 30 Identify which are the independent and dependent variables. Usage/ 1000 621.55 675.06 562.03 452.93 369.95 273.98
Question 4 A company that focuses on research and development (R&D) is interested in the relationship between profit and some variables that can explain profit variations. They want to predict the profits of some new and exciting projects because some are risky. The predictor variables considered are: RISK which is a company derived metric, R&D which is the research and development costs, REGION (WC, EC and KZN) and average per capita income (INCOME in R1000.00). The analyst decides to estimate the regression function with the following response function. (profit) = bo + b₁XRISK + b₂XR&D + b₂XWc + b₁XEC + b₂XINC Answer the following questions. 4.1. The analyst considered the fact that the effects of multicollinearity may influence some analytical and visual conclusions. Use the following results to show the process of establishing by using extra sums of squares whether there is that multicollinearity present or not. Explain and interpret in detail. SSR (X) = 2573.72, SSR(x₁) = 2600.7,…
Data was gathered on the amount of rainfall X (in inches) for the last 5 years to analyze if the amount of rainfall X (in inches) could be used to predict the acorn output Y (in tons) for a group of oak trees. Researchers expect the the acorn output to increase linearly with the increase in rainfall amount. That is, they are specifically interested in testing if the correlation between the rainfall and the acorn output is positive (i.e.: ) . When the necessary conditions are satisfied, under the null hypothesis, test statistic for above test Group of answer choices follows a t distribution with degrees of freedom (n-1) follows a chi-square distribution with degrees of freedom (n-1) follows a t distribution with degrees of freedom (n-2) follows a standard normal distribution. follows a chi-square distribution with degrees of freedom (n-2)
The marketing research department of a large company knows that the company's monthly sales are influenced by the way in which it spends money on marketing. For example, monthly expenditures such as the ones listed below are known to have an effect on y, the company's total monthly sales (in millions of dollars). x = money spent on television advertising (in 1000's of dollars) X2 = money spent on promotion (i.e., free samples) x3 = money spent on newspaper advertising (in 1000's of dollars) X = average discounts offered to retail outlets (in %) Using data from the previous 26 months, the company decides to collect data on 3 of the independent variables to use in a multiple regression model for estimating monthly sales. If the R´ for this model is 0.5, fill in the missing entries in the ANOVA table associated with this model. Do all calculations to at least three decimal places. Source of variation Degrees of freedom Sums of Mean F statistic squares squares Regression Error Total 4+
The following table gives the amount spent on cellular service. Date Cellular service revenue(in billions) 2011 1.01 2012 1.05 2013 1.09 2014 1.11 Plot the data points. (Let tbe the number of years since 2011 and C the amount of cellular service revenue, in billions of dollars.) CORRECT (b) Find the equation of the regression line. (Let t be the number of years since 2011 and C the amount of cellular service revenue, in billions of dollars. Round the regression line parameters to three decimal places.) C(t) = C(t) = 0.034t+1.014 CORRECT Add its graph to the plotted data. CORRECT (c) In 2015, $1.14 trillion was spent on cellular service. If you had been a financial strategist in 2014 with only the data in the table above available, what would have been your prediction for the amount spent on cellular service in 2015? (Round your answer to two decimal places.) billion dollars CORRECT…

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