Interpret the hypotheses you specified above. O Ho: There is a linear relationship between x and y. Ha: There is no linear relationship between x and y O Ho: All of the explanatory variables are important in explaining/predicting x. Ha: None of the explanatory variables are important in explaining/predicting x. O Ho: There is no linear relationship between x and y. Ha: There is a linear relationship between x and y O Ho: None of the explanatory variables are important in explaining/predicting x. Ha: At least one explanatory variable is important in explaining/predicting x. State the decision rule. O Reject Ho if p > 0.025. Do not reject Ho if p $ 0.025. O Reject Ho if p 0.05. Do not reject Ho if p s 0.05. State the appropriate test statistic name, degrees of freedom, test statistic value, and the associated p-value (Enter your degrees of freedom as a whole number, the test statistic value to three decimal places, and the p-value to four decimal places). --Select-- ] ( ) = 1 .P -Select- State your decision. O Do not reject the null hypothesis: There is not a linear relationship between y and x. O Do not reject the null hypothesis: There is a linear relationship between y and x. O Reject the null hypothesis: There is not a linear relationship between y and x. O Reject the null hypothesis: There is a linear relationship between y and x. (c) What would be a typical size error of prediction when you use this regression model? (Round your answer to three decimal places.)Choose the correct interpretation of the typical size error of prediction you identified above by mentally inserting the value into the blanks below. O When using this model to estimate parameters, we expect to be off by units, on average. When using this model to make predictions, we expect to be units closer to the true value, on average. When using this model to make predictions, we expect to be off by units, on average. O When using this model to estimate parameters, we expect to be units closer to the true value, on average. Regardless of your conclusions above concerning the quality of the model, use the model to answer the following questions. (d) Use the model to make a prediction when x = 1.0. (Round your answer to three decimal places.) Imagine that the actual value is 4.730 when x = 1.0. Calculate the residual. (Round your answer to three decimal places.) Interpret the residual you calculated immediately above by mentally inserting the ABSOLUTE VALUE of the residual into the blanks below. O When using this model to make predictions, we expect to be off by _ units, on average. Our prediction was units higher than the actual target value when x = 1.0. Our prediction was an overestimate. O Our prediction was units lower than the actual target value when x = 1.0. Our prediction was an underestimate. O Our prediction was units lower than the actual target value when x = 1.0. Our prediction was an overestimate. O Our prediction was units higher than the actual target value when x = 1.0. Our prediction was an underestimate. O When using this model to make predictions, we expect to be units closer to the true value, on average. (e) Use the model to make a prediction when x = 17.0. (Round your answer to three decimal places.) Imagine that the actual value is 52.565 when x = 17.0. Calculate the residual. (Round your answer to three decimal places.) Interpret the residual you calculated immediately above by mentally inserting the ABSOLUTE VALUE of the residual into the blanks below. Our prediction was units lower than the actual target value when x = 17.0. Our prediction was an overestimate. Our prediction was units lower than the actual target value when x = 17.0. Our prediction was an underestimate. O When using this model to make predictions, we expect to be units closer to the true value, on average. O Our prediction was units higher than the actual target value when x = 17.0. Our prediction was an underestimate. O Our prediction was units higher than the actual target value when x = 17.0. Our prediction was an overestimate. O When using this model to make predictions, we expect to be off by _ units, on average.Simple Linear Regression: Suppose a simple linear regression analysis provides the following results: bo = 2.000, bj = 2.875, $= 0.750, Sp. = 0.500, S = 1.364 and n = 24. Use this information to answer the following questions. (a) State the model equation. Op = Po + Pix Ox = Po + Ply Op = Do + Pist, Op = Do + fix+ Past, Op - Pot ex Op - Po + Pixi + #2x2 (b) Test for a linear relationship between x and y. Use a 5% level of significance. State the hypotheses to be tested. O Ho: Po = 0 Hai Poo O Ho: P2 = 0 Ha: #2 # 0 O Ho: 84 = 0 Ha: 4 + 0 O Ho: 1 = 0 Hai #1 $ 0 O Ho: B3 = 0 Hai B3 + 0 Interpret the hypotheses you specified above. O Ho: There is a linear relationship between x and y. Ha: There is no linear relationship between x and y O Ho: All of the explanatory variables are important in explaining/predicting x. Ha: None of the explanatory variables are important in explaining/predicting x. O Ho: There is no linear relationship between x and y. Ha: There is a linear relationship between x and y O Ho: None of the explanatory variables are important in explaining/predicting x. Hat At least one explanatory variable is important in explaining/predicting x