Analysis of LSRL. EQ: How well does the line fit the data?. What would you conclude based on the graph?. Data from 1860-1940. Barrels of Rum Sold. Ministers in Boston. Reasons for strong correlation. Lurking variables: Something in the background that affects both variables the same way.
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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
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
value of the response variable and the value
Predict the height of a person with a shoe size of 8.5
Observed - Predicted
8.5 shoe size and 66 inches for height
A good residual plot has
Balance between +’s and –’s
How are American female (30-39) heights and weights related?