β for the centred form of the simple linear regression model given by yi =α+β(xi −x ̄)+εi i=1,2,...,n. (b) Check that the estimates do give a minimum in the same way as we saw for the standard form of the simple linear regression model.

β for the centred form of the simple linear regression model given by yi =α+β(xi −x ̄)+εi i=1,2,...,n. (b) Check that the estimates do give a minimum in the same way as we saw for the standard form of the simple linear regression model.

Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter5: Inner Product Spaces

Section5.CR: Review Exercises

Problem 62CR

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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?
Compute for the necessary linear regressions based on the given data. (can use Excel or Minitab for this) An article in the Tappi Journal (March, 1986) presented data on green liquor Na2S concentration (in grams per liter) and paper machine production (in tons per day). The data (read from a graph) are shown as follows: (a) Fit a simple linear regression model with y green liquor Na2S concentration and x production. Draw a scatter diagram of the data and the resulting least squares fitted model.(b) Find the fitted value of y corresponding to x = 910 and the associated residual.
The relationship between yield of maize, date of planting, and planting density was investigated in an article. Let the variables be defined as follows. y = percent maize yield x = planting date (days after April 20) z = planting density (plants/ha) The following regression model with both quadratic terms where x₁ = x, X₂ = Z, X3 = x² and x4 = 2² provides a good description of the relationship between y and the independent variables. y =a +B₁x₁ + B₂X₂ + B3X3+B₁x₁ + e (a) If a = 21.07, B₁ = 0.653, B₂ = 0.0022, B3 = -0.0207, and B4 = 0.00002, what is the population regression function? y = 509 X (b) Use the regression function in Part (a) to determine the mean yield for a plot planted on May 7 with a density of 41,182 plants/ha. (Give the exact answer.) (c) Would the mean yield be higher for a planting date of May 7 or May 23 (for the same density)? The mean yield would be higher for [May 7 You may need to use the appropriate table in Appendix A to answer this question.
The table shows the average salaries y (in millions of dollars) of Major League Baseball players on opening day of baseball season from 2008 through 2013. (Source: Major League Baseball)(a) Find the least squares regression line for the data. Let x represent the year, with x = 8 corresponding to 2008.(b) Use the linear regression capabilities of a graphing utility to find a linear model for the data. How does this model compare with the model obtained in part (a)?(c) Use the linear model to create a table of estimated values for y. Compare the estimated values with the actual data.
Consider the following linear regression model that relates income per capita in thousand dollars of a country i (GDP P Ci), with its percentage of the population in the agricultural sector (P Ai): Model : GDP P Ci = β0 + β1P Ai + ui (a) Explain in words how to interpret parameters β0 and β1. What sign do you think these parameters might have? Explain. (b) Draw the (population) regression line associated with this model assuming that parameters β0 and β1 have the sign you have indicated in answering question (2a). Explain the meaning of this regression line.
An experiment was performed on a certain metal to determine if the strength is a function of heating time. Results based on 10 metal sheets are given below. ∑ X = 30 ∑ X 2 = 104 ∑ Y = 40 ∑ Y 2 = 178 ∑ XY = 134 Using the simple linear regression model, find the estimated y-intercept and slope and write the equation of the least squares regression line.
y y 90 54 50 53 80 91 35 41 60 48 35 61 60 71 40 56 60 71 55 68 40 47 65 36 55 53 35 11 50 68 60 70 65 57 90 79 50 79 35 59 A data set consist of dependent variable (y) and independent variable (x) as shown above. It is claims that the relationship between the x and y can be modelled through a regression model as follows: ŷ = a+bx where a and b are the estimated values for a and ß (refer to Appendix). (i) Determine the equation of the regression line to predict the y value from the x value. (ii) If the x value is 75, what is the value of y? (iii) Test the hypothesis that a=10 against the alternative a <10. Use a 0.05 level of significance. (iv) Construct a 95% prediction interval for the y with x=35.
Find the least linear regression of (1, 0), (3, 3), and (5, 6).
The relationship between yield of maize (a type of corn), date of planting, and planting density was investigated in an article. Let the variables be defined as follows. y = maize yield (percent) x1 = planting date (days after April 20) x2 = planting density (10,000 plants/ha) The following regression model with both quadratic terms where x3 = x12 and x4 = x22 provides a good description of the relationship between y and the independent variables. y = ? + ?1 x1 + ?2 x2 + ?3 x3 + ?4 x4 + e (a) If ? = 21.05, ?1 = 0.652, ?2 = 0.0025, ?3 = −0.0204, and ?4 = 0.5, what is the population regression function? y = (b) Use the regression function in part (a) to determine the mean yield (in percent) for a plot planted on May 8 with a density of 41,182 plants/ha. (Round your answer to two decimal places.) % (c) Would the mean yield be higher for a planting date of May 8 or May 22 (for the same density)? The mean yield would be higher for . (d) Is it…
The least squares regression line for a set of data is calculated to be y = 24.8 + 3.41x. (a) One of the points in the data set is (4, 37). Calculate the predicted value. (b) For the point in part (a), calculate the residual.
An engineer studied the relationship between the input and output of a production process. In(,X1) B, X2 1. He considered the non-Ilinear multiple regression model: Y = Bo + In order to estimate the parameters with a software package, the engineer needs to transform the above equation to a linear equation. Let U, V denote the transformed variables for X1 and Y What are U and V? (As functions of X1 and X2)
a) explain on he strenght and variation of the model (multiple regression) b) At a-value =0.01. test whether there is a significiant relationship between the dependent variable (y) and the independant variables x1, x2 and x3