Consider the following linear regression Yi = x;ß+ u;, where i = 1, ., 6; x; = 3i; y = (1,2, 3, 4, 5, 6)'; = 1. Find the OLS estimator of ß. Moreover, find the unbiased estimator of o?.
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- A researcher believes that there is a linear association between the level of potassiumcontent (y) in milligrams and the amount of fiber (x) in grams in cereal. The regression line forthe data is computed to be: ŷ = 36+27x rate. It was also computed that r = .62 b. If a cereal has 4 grams of fiber and a potassium content of 160 milligrams, what is thevalue of the residual?4. Consider the following multiple regression results, where the dependent variable is the number of movie tickets sold per week, X, is the ticket price, and X2 is the cost of DVD rental. -51.0918X¡1 + 41.4607X¡2 (37.0184) (13.130) (0.584) Sp t = (-6.800) SSR = 17,023 SSE = 6,262 = 23,285 SST = п 3D 20 Complete the missing entries in the output. 1 | Are the slope coefficients, b, and b2, individually statistically significant (a = 0.10 2. 3. Calculate the standard error of the regression (s.) and the R2.The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density35 35043 34053 33954 32155 310 Step 3 of 6 : Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.
- 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.If our data were a perfect fit to our regression model, such that y; = ŷ;, we would expect | to be in the CI on p.Suppose that I want to estimate the effect of x₁ on y. Consider the univariate regression line: how to calculate a and b₁ using OLS? y = a + b₁x₁
- The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1^x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age Bone Density35 35043 34053 33954 32155 310 Step 4 of 6 : Determine the value of the dependent variable yˆ at x=0.There is a relationship between the following variables as y = a + b * (1 / x).Determine the regression coefficients (a and b) of the regression equation.Calculate the correlation coefficient and interpret the degree of correlation by comparing the calculated correlation coefficient with the critical correlation coefficient for 1% significance level, taking into account the number of data. Estimate the y-value for x = 0.8 and the x-value for y = 3.5 x 0,58 0,5 0,32 0,18 0,15 0,1 y 1,2 1,5 2 3 4 5I just need help on on parts H, i and J. The regression line for part G is on the first page. Thank you.
- I need correct only handwritten otherwise skip plsBelow are bivariate data O each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squares regression line. The equation for this line is ing birthrate and life expectancy information for y = 81.87 – 0.46x. Birthrate, x (number of births per 1000 pop.) Female life expectancy, y (in years) 85- 35.7 67.7 80- 41.5 63.9 75 31.9 63.3 19.9 73.0 70 50.5 60.4 65. 24.4 72.7 60- 50.1 63.2 55 13.8 72.5 50 50.3 54.6 45.6 57.9 15.9 76.2 Figure 1 26.6 71.9 Send data to Excel
