Notes taken during lecture; used as an extension to lecture notes. Includes easier to digest wording of key concepts and additives said verbally by the professor.
Lecture 1 Review
● Ordinal cannot be shuffled in such a way that makes sense
● When make a claim make it about the parameter (population)
● Mu is unknown unless took a census (rare)
● Associated = dependent = paired samples (ex/ twins)
○ Have to be same size
● No association = independent = not paired samples
○ Not the same size
Lecture 2 Comparing Two Means from Independent Samples
● Parameter = mu1 - mu2 (difference between independent samples)
● Point estimate = x1 - x2 = mu1 - mu2
● As sample size increases variance decreases
● The calculation of the variance is by addition
○ More variability/error when have two things
● Standard error = standard deviation
● For sd first sum then square root
● 95%(CL) of intervals contain this value
● T Test: do not know sd or sample size not big enough (need df)
○ As long as one sample size is small have to use t-test
● For DF: use the smallest # between n1 - 1 and n2 -1
● Two-sample t-test: to compare the difference in means (independent)
● Paired t-test: compare two samples from same population same variable two different
times (dependent)
Lecture 3: Comparing Two Means from Independent Samples (Cont’t)
● T star = (p = 1 - alpha/2, df, lower.tail)
● If 0 is not in the confidence interval then there is a significant difference
if know the variances, otherwise use above
● Type one error = lower alpha Type two error = increase alpha
● Things that happen by chance are between -1.9 to 1.9 (for test_stat)
, Square root of the sums 1
Lecture 4: Comparing Two Proportions
● Need 10 successes and 10 failures or else data = skewed and CLT does not apply
sum, then square root (SE for CI)
= Confidence Interval
● Change percentage to decimals
● Narrow CI raise alpha
SE for null hypothesis/HT
Lecture 5: ANOVA
● F-statistic should equal about one
○ If the top number is larger than leans towards rejecting the null
Lecture 5: ANOVA (cont’d from understanding the ANOVA table)
● X with two bars is the grand mean (the mean of all means)
● In the df calculation no longer have k factors have k -1 because one of them gets lost in
the process b/c the third # is bound to the mean (?)
● Reasonably symmetric, no outliers at most 1 - conditions for ANOVA for R assignment
Lecture 6:ANOVA (cont’d from Back to the Example)
● In one way anova test - statistics
○ as a measure of variation among the sample means - MSTR
○ (b) as a measure of variation within the samples - MSE
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller niasimmons. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $8.24. You're not tied to anything after your purchase.
