Introduction to Statistics Notes
... Example: In an investigation of the heights of the blades of grass in a field, you measure blades of grass with a ruler. The result is a set of values. These values must be handled appropriately to give us useful information. The values have units (e.g. the height of a particular blade of grass is 2 ...
... Example: In an investigation of the heights of the blades of grass in a field, you measure blades of grass with a ruler. The result is a set of values. These values must be handled appropriately to give us useful information. The values have units (e.g. the height of a particular blade of grass is 2 ...
Using Excel to Construct Confidence Intervals
... will also need a number for α, which can be interpreted as the “unconfidence level”, i.e., α= 100-confidence level. So, for a 95% confidence level, we have α=5% or 0.05. In the remainder, n stands for the sample size. In general, a confidence interval always takes on the following form: ...
... will also need a number for α, which can be interpreted as the “unconfidence level”, i.e., α= 100-confidence level. So, for a 95% confidence level, we have α=5% or 0.05. In the remainder, n stands for the sample size. In general, a confidence interval always takes on the following form: ...
Hypothesis Testing Using a Single Sample
... might be made when making a decision in a hypothesis-testing problem. Definition 10.2: Type I error – the error of rejecting H0 when H0 is true. Type II error – the error of failing to reject H0 when H0 is false. The only way to guarantee that neither type of error will occur is to make such decisio ...
... might be made when making a decision in a hypothesis-testing problem. Definition 10.2: Type I error – the error of rejecting H0 when H0 is true. Type II error – the error of failing to reject H0 when H0 is false. The only way to guarantee that neither type of error will occur is to make such decisio ...
Unit 4A
... that unmarried respondents purchase more fashion clothing than those who are married, a third variable, the buyer's sex, was introduced into the analysis. • As shown in the table, in the case of females, 60% of the unmarried fall in the high-purchase category, as compared to 25% of those who are mar ...
... that unmarried respondents purchase more fashion clothing than those who are married, a third variable, the buyer's sex, was introduced into the analysis. • As shown in the table, in the case of females, 60% of the unmarried fall in the high-purchase category, as compared to 25% of those who are mar ...
