Sec5-12 - Project 1 STAT 101 Dr Kari Lock Morgan Due Tuesday Normal Distribution 5 pages double spaced including figures Chapter 5 Normal distribution

Sec5-12 - Project 1 STAT 101 Dr Kari Lock Morgan Due...

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10/18/2012 1 Statistics: Unlocking the Power of Data Lock5Normal Distribution STAT 101 Dr. Kari Lock Morgan 10/18/12 Chapter 5 Normal distribution Central limit theorem Normal distribution for confidence intervals Normal distribution for p-values Standard normal Statistics: Unlocking the Power of Data Lock5Project 1 Due Tuesday 5 pages, double spaced, including figures Hypotheses should not change based on data This is a research paper there should be text and complete sentences. Statistics: Unlocking the Power of Data Lock5Exam 1 Grades Min. :23.00 1st Qu.:39.00 Median :43.00 Mean :41.46 3rd Qu.:45.00 Max. :50.00 Regrade requests must be submitted in writingto me by Thursday, 10/25/12. I will regrade entire problems, so your grade may go up or down. Statistics: Unlocking the Power of Data Lock5slope(thousandths)-60-40-200204060Dot Plotr-0.20.00.20.40.6Nullxbar98.298.398.498.598.698.798.898.999.0Diff-4-3-2-101234xbar26272829303132Dot PlotSlope :Restaurant tips Correlation: Malevolent uniforms Mean :Body Temperatures Diff means: Finger taps Mean : Atlanta commutes phat0.30.40.50.60.70.8Proportion : Owners/dogs All bell-shaped distributions! Bootstrap and Randomization Distributions Statistics: Unlocking the Power of Data Lock5The symmetric, bell-shaped curve we have seen for almost all of our bootstrap and randomization distributions is called a normal distribution Normal Distribution -3-2-10123Statistics: Unlocking the Power of Data Lock5Central Limit Theorem! For a sufficiently large sample size, the distribution of sample statistics for a mean or a proportion is normal
10/18/2012 2 Statistics: Unlocking the Power of Data Lock5Central Limit Theorem The central limit theorem holds for ANY original distribution, although “sufficiently large sample size” variesThe more skewed the original distribution is (the farther from normal), the larger the sample size has to be for the CLT to work Statistics: Unlocking the Power of Data Lock5Central Limit Theorem For distributions of a quantitative variable that are not very skewed and without large outliers, n ≥ 30 is usually sufficient to use the CLTFor distributions of a categorical variable, counts of at least 10 within each category is usually sufficient to use the CLT

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