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Vocab
LSRL, residual
r and R-squared
Interpreting Slope & Y-intercept
Miscellaneous
100
A measure of how far a prediction is from what is actually observed.
What is a residual?
100
The LSRL for an experiment is y = 1.04x - 0.4. (4, 3.5) was one of the data points collected during the experiment. Calculate the residual.
Residual = -0.26
100
Interpret what a correlation coefficient of r = 0.9 means.
What is strong, positive association?
100
y = 1.25x + 10, where x is the amount of fertilizer in mL and y is the height of the plant (cm). Interpret the y-intercept.
When zero mL of fertilizer are used, the plant is expected to be 10 cm tall.
100
Describe the association in the graph comparing temperature and ice cream sales. https://www.mathsisfun.com/data/images/scatter-ice-cream1.gif
positive, linear, strong association with no apparent outliers
200
A unique line that has the smallest possible value for the sum of the squares of the residuals
What is the Least Squares Regression Line (LSRL)?
200

The LSRL is y = 6.5 + 4.2x . Is the direction positive or negative? 

Positive

200

If the correlation coefficient is zero, what does that mean about the association?

There is no association 

200
y = 1.25x + 10, where x is the amount of fertilizer in mL and y is the height of the plant (cm). Interpret the slope.
For every additional mL of fertilizer used, the height of the plant increases by 1.25 cm.
200

Is a linear model appropriate for the data shown in the residual plot? Explain. http://oregonstate.edu/instruct/st352/kollath/handouts/simplereg/residuals_files/image006.gif

Yes, because there is no apparent pattern in the residual plot.

300
The measure of the strength of the association between two variables.
What is the correlation coefficient (r)?
300

The LSRL is y = 5.1+ 2.4x  . 

What is the slope? 

What is the y-intercept?

300

What must be true for the correlation coefficient to be -1?

The association is VERY strong and the slope is negative.

300
Given the interpretations of slope and y-intercept, write the equation of the LSRL: When the length of the paint on a pencil is 0 cm, the weight of the pencil is 2 grams. For every additional cm of paint, the weight of the pencil increases by 1.4 grams.
y = 1.4x + 2
300

How do you calculate the residuals on your graphing calculator? Write out the steps.

y - yhat

400

What can explain the association between two other variables that are linked?

What is a lurking (or hidden) variable?

400

Determine the LSR for the following data set: (2, 1.9); (4, 3.5); (6, 6.3); (8, 6.9); (10, 10.6)

LSRL: y = - 0.4+1.04x 

400

Given an LSRL of y = -0.5x + 17 and an R-squared value of .68, determine and interpret the correlation coefficient. Round to the nearest hundredth.

What is r = -0.82? This means there is a strong, negative association.

400
y = 0.41x -14 where x is temperature (degrees F) and y is the number of people at the park (in 1000s). Interpret the y-intercept.
When the temperature is 0 degrees Fahrenheit, there are -14000 people in the park. (Doesn't make sense)
400

What is Ms Martinez's Hogwart's House

Hufflepuff <3

500
The four categories that are used to describe association.
What is form, direction, strength, and outliers?
500

What does it mean for the actual and predicted y-values if the residual is 0? 

Predicted y value = actual y value

500
y = 1.25x + 10, where x is the amount of fertilizer in mL and y is the height of the plant (cm). r = 0.92 Determine and interpret R-squared.
What is R-squared = 0.8464 About 85% of the variability in plant height can be explained by a linear relationship with the amount of fertilizer used.
500
y = 0.41x -14 where x is temperature (degrees F) and y is the number of people at the park (in 1000s). Interpret the slope.
For every additional degree F in temperature, the number of people at the park increases by 410. When the temperature increases by 1 degree F, the number of people at the park increases by 410.
500

When comparing age and height, Eva calculated R-squared to be 0.76. Write a sentence to explain what R-squared means in this context AND provide at least one other factor that can explain the variability in height.

76% of the variability in height can be explained by a linear relationship with age. The other 24% could be explained by genetics, general health, environmental factors, etc.