[a] Welcome Back! Please pick up a new packet Get a Chrome Book Complete the warm up Choose points on each graph and find the slope of the line. [b]
Agenda 05 MIN Warm Up 25 MIN Notes Correlation 15 MIN Assignment 05 MIN Wrap Up Please write in the dates 1/2 1/3 1/4 1/5 1/8 1/9 1/10
Unit 8: Linear Regression Correlation Coefficients and Bivariate Data (13.5) I can match correlation coefficients with scatterplots. I understand that correlation does not imply causation due to a lurking variable(s). Let's investigate correlation coefficients.... http://rpsychologist.com/d3/correlation/ http://www.rossmanchance.com/applets/guesscorrelation.html ~LT: I can match correlation coefficients with scatter plots.
Erase ~LT: I can match correlation coefficients with scatter plots. Bivariate Sampling: Two variable sampling to determine a relationship between the variables Correlation Coefficient: A measure of the interdependence of two random variables that ranges in value from -1 to +1-1 +1 0 Correlation: Positive, negative or none Move each term to the correct place on the continuum. Perfect Negative Correlation Strong Negative Correlation Weak Negative Correlation No Correlation 1 0 1 Weak Positive Correlation Strong Positive Correlation Perfect Positive Correlation ~LT: I can match correlation coefficients with scatter plots.
Positive Correlation: As x increases, y Negative Correlation: As x increases, y ~LT: I can match correlation coefficients with scatter plots. For the following situations, would you expect a positive correlation, negative correlation, or no correlation? a) The number of calories consumed and weight b) Hours of sleep and age c) Accumulated wealth and age ~LT: I can match correlation coefficients with scatterplots.
Be careful that you don t confuse the ideas of correlation and causation! A strong correlation may exist between two sets of data, but this does not imply a causal relationship. necessarily CORRELATION DOES NOT MEAN CAUSATION!!!! ~LT: I understand that correlation does not imply causation due to a lurking variable(s). The director of a summer camp has collected data for two weeks on both daily ice cream sales from the camp store and visits to the camp nurse for treatment of sunburn. They found a positive correlation! So, can you conclude that buying ice cream causes a sunburn? Or does getting a sunburn cause ice cream buying? There is most likely another variable causing both of these effects. Perhaps the daily temperature might be a behind both of these results. lurking variable ~LT: I understand that correlation does not imply causation due to a lurking variable(s).
What could be the lurking variable in the following examples? a) As ice cream sales increase, so do the number of drownings. Does eating ice cream cause you to drown? b) A certain data set showed that taller adults have higher annual salaries. Does height cause you to earn more? ~LT: I understand that correlation does not imply causation due to a lurking variable(s). We can use the "rectangle method" to draw in a line of best fit for a scatter plot.
Did we meet the goals? I can match correlation coefficients with scatterplots. I understand that correlation does not imply causation due to a lurking variable(s). Assignment Correlation WS#1