Let’s say we have a linear model, either one variable or many. How do we conduct a test of significance for the coefficient of a single explanatory variable in the model? Similarly, how can we determine if an explanatory variable has a significant impact on the response variable?
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- How to do a hypothesis test for a mean difference (matched pairs)
- How to do a hypothesis test for a population proportion
- How to do a hypothesis test for population variance
- How to do a hypothesis test for the difference between means when both population variances are known
- How to do a hypothesis test for the difference between two proportions
- How to do a hypothesis test for the mean with known standard deviation
- How to do a hypothesis test for the ratio of two population variances
- How to do a one-sided hypothesis test for two sample means
- How to do a two-sided hypothesis test for a sample mean
- How to do a two-sided hypothesis test for two sample means
We will use the fake data shown below with a single variable model. You can use a model created from your own actual data instead.
1 2 3 x <- c( 34, 9, 78, 60, 22, 45, 83, 59, 25) y <- c(126, 347, 298, 309, 450, 187, 266, 385, 400) model <- lm(y ~ x)
We can test whether a coefficient is zero by using that as our null hypothesis, $H_0: \beta_i = 0$. We can use any value $0 \le \alpha \le 1$ as our Type 1 error rate; we will set $\alpha$ to be 0.05 here.
The answer to our hypothesis test can be obtained by looking at just the coefficients portion of the model summary:
1 2 3 Estimate Std. Error t value Pr(>|t|) (Intercept) 354.082248 76.732772 4.6144853 0.002441995 x -1.009013 1.472939 -0.6850334 0.515358250
The final column of output shows $p$-values for each $\beta_i$. The $p$-value associated with the $x$ row is therefore for $\beta_1$, the coefficient on $x$. Because it is 0.515358250, which is greater than $\alpha$, we cannot reject the null hypothesis, and we should continue to assume that $\beta_1=0$ and there is no significant relationship between the explanatory and response variable in this situation.
Content last modified on 24 July 2023.
Contributed by Elizabeth Czarniak (CZARNIA_ELIZ@bentley.edu)