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The Linear Regression Equation, Y = a + BX, Was

question 42

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The linear regression equation, Y = a + bX, was estimated. The following computer printout was
obtained:
 DEPENDENT VARIABLE:  Y  R-SQUARE  F-RATIO  P-VALUE ON F  OBSERVATIONS: 180.30667.0760.0171\begin{array}{rllll}\text { DEPENDENT VARIABLE: } & \text { Y } & \text { R-SQUARE } & \text { F-RATIO } & \text { P-VALUE ON F } \\\text { OBSERVATIONS: } &18 & 0.3066 & 7.076 & 0.0171\end{array}

 VARIABLE  PARAMETER  ESTIMATE  STANDARD  ERROR  T-RATIO  P-VALUE  INTERCEPT 15.485.093.040.0008X21.368.032.660.0171\begin{array}{lllll}\text { VARIABLE } & \begin{array}{llll}\text { PARAMETER } \\\text { ESTIMATE }\end{array} & \begin{array}{l}\text { STANDARD } \\\text { ERROR }\end{array} & \text { T-RATIO } & \text { P-VALUE } \\\text { INTERCEPT } & 15.48 & 5.09 & 3.04 & 0.0008\\X&-21.36&8.03&-2.66&0.0171\end{array}
-What is the critical value of t at the 1% level of significance?

Design recursive algorithms by identifying base cases and recursive cases.
Understand the concept of tail recursion.
Recognize the consequences of infinite recursion and how to avoid it.
Calculate the results of simple recursive methods.

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