HDFS 330 1st Edition Lecture 7Outline of Last Lecture I. Normal DistributionII. Z DistributionIII. Percentile Outline of Current Lecture I. Data Interpretation and T-table II. Confidence intervals Current LectureI. Data Interpretation and T-tablea. T-distributioni. More area in the tails and less in the center than z-distributionii. As “n” increases though, a t-distribution will approach the standard normal distributioniii. T-values allows us to estimate uncertainty - Alpha= acceptable probability of erroro As we want to be more confident in our analyses, we decrease our alpha level o Alpha value is the acceptable probability of confidence (the inferencesyou make from the data you collect is wrong) Decrease in alpha levels= more confidence levels  Acceptable probability of error is the alpha value - Ex. For 95% confidence, we are saying that we are willing to accept a 5% chance our range of possible means is wrong, so alpha=.05 - Based on your degrees of freedom, you can calculate t-score from any alpha level interested o df= degrees of freedom= n-1 - within the distribution there is a true population mean- more samples results in less variability tighten cluster of t-distribution  less range of predicted values (more confident, more accurate numbers) These notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.- less samples more variability= bad II. Confidence intervals - A way to incorporate our sampling error in our population parameter estimate- Range of values around a sample mean that has a probability of containing the true population mean - Steps for determining confidence interval1. Calculate mean of sample 2. Calculate variance, SD and SE3. Determine critical value from distribution table4. Plug values into CI formula- As CI increases, the CI get wider- Decrease variability, add more samples- Smaller sample sizes generate wider