STATS

The alternative hypothesis (H1) represents the opposite of the null hypothesis (research hypothesis)

- Is believed to be true if the null hypothesis is found to be false - This is usually what we want to test/find out - In my research I am trying to "prove" a certain hypothesis

A Type I error occurs when the null hypothesis is rejected when it is true

- The probability of making a Type I error is known as α , the level of significance - This is the same α that you already know from Chapter 7 and Chapter 8

A Type II error occurs when we fail to reject the null hypothesis when it is not true

- The probability of making a Type II error is known as β

A Type II error is known as the consumer's risk

- when it occurs the customer is getting a product from a process that is not performing properly

A Type I error is known as the producer's risk

- when it occurs the producer is looking for a problem in its process that does not exist

The null hypothesis (H0) represents the status quo

The null hypothesis is believed to be true unless there is overwhelming evidence to the contrary

The least squares method will minimize the sum of squares ____ when describing the equation that best fits the ordered pairs

error

The ___ is another term for the variance of the sample data

mean square total

Type 1 error occurs in hypothesis testing when we

reject the null hypothesis and the null hypothesis is true

the p-value for a hypothesis test is defined as the probability of observing a

sample mean at least as extreme as the one selected for the hypothesis test, assuming the null hypothesis is true

the ___ measures the variation in the dependent variable that is explained by the independent variable in simple regression analysis

sum of squares regression

The sum of squares ____ measures the variation between each data value and the corresponding sample mean

within

For a two tailed test reject null if

zx>alpha