perature. Moreover, one recogn that an inference about the number of f Hogg 86.
ID: 3338458 • Letter: P
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perature. Moreover, one recogn that an inference about the number of f Hogg 86. It g the o be lus- hip perature is needed. The actual tempera temperature recorded at a previous la to extrapolate inferences to a region f officials had looked at this plot, certain example shows why it is important to h important decisions. These comments raise two intere scatter plot of one variable againsta data. Yes, it is true that some data w Challenger. But not all the relevant takes knowledge of statistics as we ability to question the relevance of on of re Exercises 6.5-I model sum to zero. al 6.5 the least squares fit of the simple linear regression on in ,n), l. Show that the residuals, Yi-Yi (i = 1,2, 2. In some situations where the regression model is 0 is ual to 0, i.e., Yi-xi + where for i-1,2, . . . ,n ex gi useful, it is known that the mean of Y when X are independent and N(0,02). o) Opheain the mnrloaos, and @ of and 2 under this model. You may use, with- out proof, the fact that and 2 are independent, together with Theorem 9.3-1.) 6.5-3. The midterm and final exam scores of 10 students in a statistics course are tabulated as shown. (a) Calculate the least squares regression line for these (b) Plot the points and the least squares regression line on (c) Find the value of 2. data. the same graph.Explanation / Answer
6.5-1
Let suppose, we have the following data of sales being the dependent variable and advertising being the independent variable. We want to predict if advertising has an impact on the sales.
Below is the output of simple linear regression alongwith the residuals. In this case, we can see that the sum of residuals is 0: -
Advertising Sales 23 145 27 136 21 143 39 147 37 146 33 143 23 139 44 144 45 139 16 129 30 140 42 146 54 146 27 143 34 145 15 144 19 140 38 145 44 143 47 146 43 149 27 141 51 146 61 149 39 143Related Questions
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