The lesson covers the conditions for using a normal model by examining one-variable data distributions. It explains different distribution shapes, such as uniform, skewed, and symmetric, and emphasizes the importance of visual displays like dot plots. Examples include household income, student heights, and school district salaries, highlighting when a normal model is appropriate. The lesson concludes with a reminder to check assumptions before using a normal model.

What is a key characteristic of a single-peaked symmetric distribution?

• It is always multi-peaked.

• It can sometimes be modeled with a bell curve.

• It cannot be represented on a dot plot.

• It is always skewed to the right.

Why should the distribution of household income in a New England town not be modeled with a normal curve?

• It is single-peaked.

• It is uniform.

• It is skewed.

• It is symmetric.

What is a common issue with using a normal model for the distribution of student wages?

• The data is skewed to the left.

• The data is multi-peaked.

• The data is too uniform.

• The data has no outliers.

Which of the following distributions is likely to be single-peaked and symmetric?

• IQ scores of randomly selected adults

• Household income in a town

• Salaries of school district employees

• Heights of Algebra 2 students

What does the empirical rule specify in relation to normal distributions?

• The number of peaks in a distribution

• The presence of outliers

• The skewness of a distribution

• The percentages that result in a bell shape