https://padlet.com/10014704/wq55le89gojq en-us 2017-03-16 15:22:39 UTC 2025-04-06 16:38:29 UTC hello@padlet.com 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160568286 Bivariate Data: the values of two different variables obtained from the same population. 
1) both categorical: gender & favorite movie: two-way table
2) one categorical-one quantitative: gender & height: comparative boxplot
3) both quantitative (numerical): height & weight: scatterplot

]]> 2017-03-16 15:24:36 UTC https://padlet.com/10014704/wq55le89gojq/wish/160568286 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160574860 2017-03-16 15:43:10 UTC https://padlet.com/10014704/wq55le89gojq/wish/160574860 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160575385 2017-03-16 15:44:29 UTC https://padlet.com/10014704/wq55le89gojq/wish/160575385 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160576643 Goal with using scatter-plots is to see if there is some correlation between the two variables.
Negative correlation, positive correlation, no correlation
-strong
-moderate
-weak]]> 2017-03-16 15:48:03 UTC https://padlet.com/10014704/wq55le89gojq/wish/160576643 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160577610 2017-03-16 15:50:45 UTC https://padlet.com/10014704/wq55le89gojq/wish/160577610 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160579548 2017-03-16 15:56:08 UTC https://padlet.com/10014704/wq55le89gojq/wish/160579548 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160581414 A correlation coefficient is a number (r) that describes the strength and direction of a linear relationship. 
A line of best fit (or regression line), is a line that best represents the data in a scatter plot.]]> 2017-03-16 16:01:37 UTC https://padlet.com/10014704/wq55le89gojq/wish/160581414 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160581657 Domain: everything between the x values
Interpolation: prediction inside the domain
Extrapolation: prediction outside the domain
The stronger the correlation, the more accurate the prediction will be.]]> 2017-03-16 16:02:22 UTC https://padlet.com/10014704/wq55le89gojq/wish/160581657 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160585311 2017-03-16 16:13:02 UTC https://padlet.com/10014704/wq55le89gojq/wish/160585311 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160585682 You take the amount of data points for x and look for it in the n column.. You then look at the numbers to the right of your n and if your r value is less than the number in the first column, it is not a good representative. If your r value is in between the 0.05 and 0.01 columns, your data is a good representative with 5% error. And if your r value is greater than the number in the 0.01 column, your data is a good representative with 1% error.]]> 2017-03-16 16:14:02 UTC https://padlet.com/10014704/wq55le89gojq/wish/160585682 10014704 https://padlet.com/10014704/wq55le89gojq/wish/160587176 The fact that two variables are correlated does not always prove a cause-and-effect relationship between the variables.
Causation: variable A cause variable B
Lurking Variable: Variable C has an impact on the relationship-but it isn't incorporated into the design of the study. 
Confounded variable: C is a variable that the researcher was unable to eliminate. It isn't distinguished from the other variables, but has an affect on the relationship.]]> 2017-03-16 16:18:19 UTC https://padlet.com/10014704/wq55le89gojq/wish/160587176