Statistic Cheat Sheet

Variable(s) Categorical

Quantitative

Categorical vs Categorical Quantitative vs Categorical Quantitative vs Quantitative

Visualization

Summary Statistics bar chart, frequency pie chart table, relative frequency table, proportion dotplot, mean, histogram, median, max, boxplot min, standard deviation, z-score, range, IQR, five number summary side-by-side two-way bar chart, table, segmented bar difference in chart proportions side-by-side statistics by boxplots group, difference in means scatterplot correlation

Z* 95% = 1.96 90% = 1.645 99% = 2.58

sample statistic  z *  SE

- Single Proportion

2

 z*  n  pˆ (1  pˆ ) ME  

SE 

SE 

p1 (1  p1 ) p2 (1  p2 )  n1 n2

 12 n1



- Difference in proportions

 22 n2

- Difference in means

- Chi Square 2

yˆ  ˆ0  ˆ1x* “We are 95% confident that the average y value for x=x* lies in this interval” We are 95% confident that the y value for x = x* lies in this interval 1) Linearity 2) Constant Variability of Residuals 3) Normality of Residuals

R2 

SSM "Variability in Y explained by the model"  SST "Total variability in Y"

H 0 : 1   2  ...   k  0 H a : At least one i  0 •

R2 is the proportion of the variability in Y that is explained by the model

• •

Adjusted R2 is like R2, but takes into account the number of explanatory variables As the number of explanatory variables increases, adjusted R2 gets smaller than R2

• • •

P(A and B) is the probability that both events A and B will happen P(A or B) is the probability that either event A or event B will happen Events A and B are disjoint or mutually exclusive if only one of the two events can happen

 P( A if B) 

P( B if A) P( A) P( B)

• Bayes Rule • The more explanatory variables in the model, the more uncertainty.