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Analysis of LSRL

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Analysis of LSRL

EQ: How well does the line fit the data?

Data from 1860-1940

Barrels of Rum Sold

Ministers in Boston

- Lurking variables: Something in the background that affects both variables the same way.

For the historical data, as population increased the number of ministers increased as did the amount of alcohol being sold.

Determine if a linear model is a good idea.

Create a scatterplot and examine the relationship.

DON’T FIND THE LSRL!!!!

r : There is a strong positive linear relationship

a: when the shoe size is 0 the height

will be 51.36 inches

b: for every 1 increase in shoe size

the height will increase by 1.87 inches

The percent of variation in the y values that is explained by the linear model with x.

Coefficient of determination = r2

where r is the correlation.

The coefficient of determination between shoe size and height is .8575.

What does this mean????

85.75% of the variation in heights is explained by the linear model with shoe size.

A residual is the

difference

between an

observed

value of the response variable and the value

predicted

by the

LSRL

Predict the height of a person with a shoe size of 8.5

Actual Data:

Residual:

Observed - Predicted

8.5 shoe size and 66 inches for height

66-67.25 =-1.25

A good residual plot has

No patterns

No outliers

Balance between +’s and –’s

How are American female (30-39) heights and weights related?

- Create a scatterplot and comment on the relationship
- Determine the LSRL, r, and r2 and interpret the values.
- Evaluate the model by analyzing the residuals.
- Predict the weight for a 63 inch female.
- Calculate the residual for a 63 inch female.
- How confident are you in your prediction?