In simple linear regression, most often we perform a two-tail test of the population slope $$\beta _ { 1 }$$ to determine whether there is sufficient evidence to infer that a linear relationship exists. Which of the following best describes the null and alternative hypotheses needed for a test of significance? A) Ho: β₀ = 0 H_A: β₀ ≠ 0 B) Ho: β₁ ≠ 0 H_A: β₁ = 0 C) Ho: β₀ = 0. H_A: β₀ ≠ 0 D) Ho: β₁ = 0 H_A: β₁ ≠ 0.

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In simple linear regression, the null hypothesis (Ho) states that the population slope (β1) is equal to zero, indicating no linear relationship. The alternative hypothesis (H_A) states that the population slope (β1) is not equal to zero, indicating a linear relationship exists.

In Simple Linear Regression, Most Often We Perform a Two-Tail β1\beta _ { 1 }β1​

In Simple Linear Regression, Most Often We Perform a Two-Tail $\beta _ { 1 }$