Question 1 1.Derive the least squares estimator of Bo for the regression modelY = Bo + Ei.2.Given the data (Xi) and (Yi), we will assume that a regressionmodel Y; = B1X; + E; is appropriate with normally distributed independent errorterms and variance o2 = 16.i124Xi7122530cantY;128213446540(a) State the likelihood function for the four Y observations.uss(b) Evaluate the likelihood function for B,17 and B, = 19. For which ofthese is the likelihood function the largest?(c) Find the maximum likelihood estimates for B, and Bo and using Sxx, Sxy,and the means for Xi and Yi, respectively, as demonstrated in theinstructional videos.

Given that, 1. Regression model:- Yi = βo + εi We need to derive the least squares estimator of…

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1. Derive the least squares estimator of Bo for the regression model Y; = Bo + Ei.

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?
Interpret the least squares regression line of this data set. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The correct least squares regression line for the data set is: y = 8.116x + 273.273 Use it to complete the following sentence: The least squares regression line predicts an additional annual rainfall if the average temperature of coastal waters increases by one degree millimetres of Celsius.
4. A runner was tested on a treadmill. During the test, his speed x (in km/h) and his heart rate y were measured. The results are shown in the table. y 122 132 145 161 178 190 x 8 10 12 14 16 18 (a) Test for the significance of regression using the analysis of variance with a = 0.05. Find the P-value for this test. Can you conclude that the model specifies a useful linear relationship between these two variables? (b) Estimate ². (c) Estimate the standard error of the slope and intercept in this model. (d) Test the hypothesis that the increase in the speed of 1 km/h results in the runner's heart rate average increase of 7 points at a = 0.05. Suppose that the alternative hypothesis is that the average increase of the runner's heart rate in this situation does not equal 7 points.
A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x). The results of the regression were: ŷ=a+bx a=-0.523 b=0.177 (a) Write the equation of the Least Squares Regression line of the form j= 0.177 +| -0.523 (b) Which is a possible value for the correlation coefficient, r? O 1.936 O -0.632 O-1.936 O 0.632 (C) If a country increases its life expectancy, the happiness index will O decrease O increase (d) If the life expectancy is increased by 1 years in a certain country, how much will the happiness index change? Round to two decimal places. (e) Use the regression line to predict the happiness index of a country with a life expectancy of 64 years. Round to two decimal places.
Suppose Y; are the fitted y-values for in a maximum-likelihood linear regression model and Y; are the observed values, i = 1,2,... Show that E(Y: - Ý;) = 0 i=1
The following sample contains the scores of 6 students selected at random in Mathematics and English. Use the scores in English as the dependent variable Y. Mathematics score (X) 70 92 80 74 65 83 English score (Y) 74 84 63 87 78 90 ∑x=464, ∑y=476,∑x^2=36354,∑y^2=38254, ∑xy=36926. Estimate the regression parameters and also write the prediction equation.
9. Regression of y= Bo + B1X11 + B2X12 + B3 X13 + BaX14 + û¡ yields the following results in tabular form. The tabulated t- statistics is 2.4 , 1.96 and 1.64 for one five and ten percent significance levels respectively. State the null and alternative hypotheses for each & Identify those variables affecting the dependent variable. Independent variables Coefficients Std. Err. Xi1 1731.784 255.4491 Xi2 667.8642 250.4062 Xi3 -38.44927 200.0217 Xi4 -48.14751 423.5669 Bo 3559.713 243.1407
Let kids denote the number of children ever born to a woman, and let educ denote years of education for the woman. A simple model relating fertility to years of education is: kids; = Bo + B1educ; + uż. 1. What are the parameters in the model? 2. What kinds of factors are contained in u? Are these likely to be cor- related with level of education? 3. Will a simple regression analysis uncover the ceteris paribus effect of education on fertility? Explain.
Two regression lines of a sample are X+6 Y=6 and 3X+2Y=0. Find the correlation coefficient.
A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x). The results of the regression were: ý=a+bx a=0.137 b=0.082 (a) Write the equation of the Least Squares Regression line of the form (b) Which is a possible value for the correlation coefficient, r? O-1.338 O-0.84 O 1.338 O 0.84 (c) If a country increases its life expectancy, the happiness index will O increase O decrease
Which of the non-parametric test for ordinal data is the best to use in the given scenario? An experiment was conducted to compare the strengths of two types of elastic bandages: one a standard bandage of a specified weight and the other the same standard but treated with a chemical substance. Ten pieces of each were randomly selected from production. Does the treated bandage tend to be stronger than the standard? Table 1. Strength measurements (and their ranks) for 2 types of bandages. STANDARD TREATED 1.21(2) 1.49(15) 1.43(12) 1.37(7.5) 1.35(6) 1.67(20) 1.51(17) 1.50(16) 1.39(9) 1.31(5) 1.17(1) 1.29(3.5) 1.48(14) 1.52(18) 1.42(11) 1.37(7.5) 1.29(3.5) 1.44(13) 1.4(10) 1.53(19) a. Mood median test b. sign test c. Wilcoxon rank-sum test d. Wilcoxon matched-pairs signed-ranks test e. Spearman and Kendall correlation coefficients f. Kruskal-Wallis test
The weight (in pounds) and height (in inches) for a child were measured every few months over a two- The equation ý = 17.4 + 0.5x is called the least- squares regression line because it year period. The results are displayed in the scatterplot. O is least able to make accurate predictions for the data. O makes the strongest association between weight and height. A Child's Weight and Height 40 O minimizes the sum of the squared distances from the actual y-value to the predicted y-value. 36 O maximizes the sum of the squared distances from the actual y-value to the predicted y-value. 32 28 24 20 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 4042 Weight (Pounds) Height (Inches)