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**Basic Question 1 of 7**

True or False? If False, correct the statement.

Suppose you are performing a simple linear regression of Y on X and you test the hypothesis that the slope (β) is zero against a two-sided alternative. You have n = 25 observations and your computed test (t) statistic is 2.6. Then your P-value is given by .01 < P < .02, which gives borderline significance (i.e., you would reject H(0) at α = .02 but fail to reject H(0) at α = .01).

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**User Contributed Comments**
11

User |
Comment |
---|---|

woojacky |
GOOD |

Flora0406 |
I can't get this one. |

bmeisner |
You need a t table for this to check the p values. It will show you what the answer tells you that t(23, alpha=.02)=+-2.5 and that t(23, .01)=+-2.8. Does anyone remember if we have to memorize the t-values or is that given on the test? |

xiajessy |
yeah, we have to remember t VALUE |

xiajessy |
I mean certain critical t value like at 10% , 5%, 1% significance level, the rest will be provided via a table |

Nightsurfer |
Guys, we won't have to memorize t-values. If this were the normal distribution, perhaps. But t-value is a function of d.f. and confidence. WAY too hard to memorize! |

Cesarnew |
We do not need to memorize any t-values for this question. Just to understand and to interpret the P-value.All the relevant info is given. |

cfaajay |
Guys when P value is given,use that for determining if null hypothesis will be rejected or not..When p-value is less then the level of significance ,reject the null hypothesis for that level of significance as is the case here P value is less then level of significance (0.2) ,so reject null . |

quanttrader |
reject at 0.02 since t > t(critical) fail to reject at 0.01 since t< t(critical) |

quanttrader |
alpha at 0.01 implies a more conservative approach -- ie CL is 99%, and thus it will be harder to reject the null |

jpowers |
Remember this is a two-tailed test. |

You have a wonderful website and definitely should take some credit for your members' outstanding grades.

#### Colin Sampaleanu

**Learning Outcome Statements**

describe the use of analysis of variance (ANOVA) in regression analysis, interpret ANOVA results, and calculate and interpret the standard error of estimate in a simple linear regression

*CFA® 2025 Level I Curriculum, Volume 1, Module 10.*