OpenIntro 6

Question 1

Poisson distribution

Answer 1

Estimating number of events in a large population over time (a day, month, year) Eg: Having a heart attack, getting married and getting struck by lightning

Question 2

Point estimate

Answer 2

We would consider the 45% to be a point estimate of the approval rating.

Question 3

P hat

Answer 3

Sample proportion Unless we collect responses from the entire population, p remains unknown and we use p hat to estimate p

Question 4

Central limit theorem

Answer 4

Check 2 (3) things: Independence Success failure condition Eg: 1000x0.82 = 820= > 10 1000x(1-0.82) = 120 = > 10 (Sample not over 10% if population)

Question 5

Chi square test

Answer 5

There are two types of chi-square tests. Both use the chi-square statistic and distribution for different purposes: A chi-square goodness of fit test determines if a sample data matches a population. A chi-square test for independence compares two variables in a contingency table to see if they are related. In a more general sense, it tests to see whether distributions of categorical variables differ from each another. A very small chi square test statistic means that your observed data fits your expected data extremely well. In other words, there is a relationship. A very large chi square test statistic means that the data does not fit very well. In other words, there isn’t a relationship.

Question 6

When p value is low?

Answer 6

H0 can be rejected and data is not weird A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis

Question 7

P value

Answer 7

Always between 0 and 1

Question 8

General test statistic

Answer 8

Diffrent ones, follow the some of the same rules